ISBIS 2019 Satellite Conference August 15-16 2019 Lanai Kijang … book ISBIS 2019 Conference.pdf · 2019-08-12 · 15:20 - 16:50 TIME SERIES IN FINANCE Chair: Katherine B. Ensor

ISBIS

2019 Satellite Conference

August 15-16 2019

Lanai Kijang

Kuala Lumpur

Malaysia

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We thank our partners:

• Bank Negara Malaysia

• Department of Applied Statistics, Faculty of Eco-

nomics and Administration, University of Malaya

• Malaysian Economic Association.

We thank our sponsors:

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Scientific Programming Committee:

• Julie Novak, Netflix, USA

• Fugee Tsung, University of Science & Technology, Hong Kong

• Bovas Abraham, University of Waterloo, Canada

• Wei Liem Loh, National University of Singapore

• Refik Soyer, George Washington University, USA

• Asha Gopalakrishnan, Cochin University of Science and Technology, India

• Wee-Yeap Lau, University of Malaya, Malaysia

• Song Won Park, University of Sao Paulo, Brazil

• Toh Hock-Chai, Bank Negara Malaysia (co-chair)

• Martina Vandebroek, KU Leuven, Belgium (chair)

Local Organising Committee:

• Lau Wee Yeap (chair)

• Muzalwana Abdul Talib

• Yee Chee May

• Ahmad Farid Osman

• Soon Siew Voon

• Hanija Jaafar

• Wan Rosman Effendi

• Ng Sor Tho

• Tey Nai Peng

• Ng Yin Mei

• Adilah Abdul Ghapor

• Lai Siow Li

• Diana Abdul Wahab

• Martina Vandebroek 3

PROGRAM

THURSDAY AUGUST 15 2019

8:30 - 9:00 Registration

9:00 - 9:15 Welcome

9:15 - 10:25 Keynote: Galit Shmueli (Multi-purpose Hall)

To Explain, To Predict, or To Describe?

Chair: Martina Vandebroek

10:30 - 10:50 Coffee/Tea break

10:50 - 12:20 STATISTICS IN FINANCE

Chair: Indranarain Ramlall (Meeting Room 1)

Indranarain RamlallA Global Analysis of Public Debt-Economic Performance Nexus in light of

the US Subprime Crisis

Kee-En Lim and Wee-Yeap Lau

Effects of 2018-19 US-China Trade War on USD/RMB Exchange Rate Vo-

latility

Ahmad Maulin Naufa

The Impact of Foreign Ownership to Return Volatility, Volume and Risk of

Stock: Evidence from ASEAN Countries

10:50 - 12:20 STATISTICAL LEARNING APPLICATIONS

Chair: Ekin Tahir (Meeting Room 2)

Luca Frigau

Regularization in tree-based classification models

Vadim Sokolov

Strategic Bayesian Asset Allocations

Tahir Ekin

Probabilistic Variable Accuracy Algorithm

12:20- 13:30 Lunch

4

13:30 - 15:00 NOVEL TIME SERIES APPROACHES WITH BUSINESS AND

INDUSTRY APPLICATIONS

Chair: Nalini Ravishanker (Meeting Room 1)

Katherine B. Ensor

Time-varying wavelet-based applications for evaluating the Water- Energy

Nexus

Balaji Raman

Return on Investment Calculator for Promotions in CPG Industry

Suparna Biswas

Non-parametric Estimation of Spectral Risk Measures

13:30 - 15:00 BAYESIAN METHODS FOR BUSINESS AND INDUSTRIAL

STATISTICS

Chair: Refik Soyer (Meeting Room 2)

Hedibert Lopes

Analysis of time series of proportions: a linear Bayes approach

Refik Soyer

A Bayesian Competing Risks Model for Assessment of Mortgage De-

fault/Prepayment

Thomas Mazzuchi

Reliability Growth Test and Evaluation for Multistage Systems

13:30 - 15:00 CONTRIBUTED SESSION 1

Chair: Pooi Ah Hin (Discussion Lounge)

Pooi Ah Hin

Asset Pricing Model with Jumps

Ng Yew Seong

A Portfolio Trading Strategy

Wayan Nuka Lantara

The impact of Net Stable Funding Ratio: Evidence from Indonesian Banks

Soon Siew Voon

Asymmetry Causal Relationship between Exchange Rate and Interest Rate

5


15:20 - 16:50 TIME SERIES IN FINANCE

Chair: Katherine B. Ensor (Meeting Room 1)

Ananya Lahiri

A model driven by mixed fractional Brownian motion in context of finance

Anindya Goswami

Testing of Binary Regime Switching Models using Squeeze Duration Analysis

Nalini Ravishanker

Clustering High-Frequency Financial Time Series Based on Mutual Infor-

mation

15:20 - 16:50 MACHINE LEARNING IN BUSINESS

Chair: David Banks (Meeting Room 2)

David Banks

The Bright Future of Experimental Design

Aniruddha Pant

Applications of Machine Learning in Healthcare

15:20 - 16:50 MEASURING PRICES OF REAL ESTATE AND ITS

CHALLENGES

Chair: Toh Hock Chai (Discussion Lounge)

Jens Mehrhoff

Commercial Real Estate Indicators: Prices and Beyond

Nur Fazila Mat Salleh

Enterprise Data Governance and Direction Going Forward

Soong Kim Loong

Data Governance: Adapting to the Digital Age

Silke Stapel-Weber

ECB data governance for better analysis and decision-making

16:20 - 16:30 Comfort break

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16:30 -18:00 APPLIED STATISTICS

Chair: Muzalwana Binti Abdul Talib (Meeting Room 1)

Muzalwana Binti Abdul Talib

Air Quality Monitoring with Exponentially Weighted Moving Average

(EWMA) control charts

Ahmad Farid Osman

Forecasting weekly electricity demand in Malaysia using exponential

smoothing approaches

Siow-Li Lai

The non-linear nexus between financial development and fertility: A dyna-

mic panel GMM analysis

16:30 -18:00 RECENT DEVELOPMENTS IN COMPLEX DEPENDENT

DATA

Chair: Lionel Truquet (Meeting Room 2)

Liudas Giraitis

Standard testing procedures for white noise and heteroscedasticity

Lionel Truquet

Time-varying time series models for categorical data

16:30 -18:00 CONTRIBUTED SESSION 2

Chair: Wiji Tri Wilujeng (Discussion Lounge)

Wiji Tri Wilujeng

Advertised Landed House Price in Balikpapan

Deja Firda

Bivariate Landed House Data using Minimum Area of Ellipse

Astari Raihanah

Pearson product moment is used more than just measuring correlation

Ang Siew Ling

Improvement of Reserve Estimate Using Information on Subclasses of In-

surance

18 00 - 20:00 WELCOME BANQUET (Residential Cafe)

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FRIDAY AUGUST 16 2019

8:30 - 9:00 Registration

9:00 - 10:30 ADVANCED METHODOLOGICAL CONTRIBUTIONS IN

TIME SERIES

Chair: Yuanbo Li (Meeting Room 1)

Yuanbo LiGroup Orthogonal Greedy Algorithm for Change-point Estimation of Multi-

variate Time Series

Zhan LiuFitting time series models for longitudinal surveys with non-ignorable mis-

sing data

Chi Tim NgReal time prediction of irregular periodic time series data

9:00 - 10:30 COMPUTER EXPERIMENTSS

Chair: Alessandro Fasso (Meeting Room 2)

Matthias HwaiGaussian Process Modeling and Optimization of Simulators for Physical

Systems

Xinwei DengGaussian Process Models for Computer Experiments with Quantitative and

Qualitative Inputs

Alessandro FassoGaussian Processes and LASSO for change detection of 4D climate datasets


Chair: Titi Kanti Lestari (Discussion Lounge)

Tigor Nirman SimanjuntakGender statistics of household fisheries industry in Indonesia

Suryo Adi RakhmawanThe gender gap on formal worker participation in Indonesia: Trend and the

way forward

Erli Wijayanti PrastiwiWomen and technical efficiency analysis of the creative manufacturing in-

dustry in Indonesia

Lilis Heri Mis CicihGender Mainstreaming in Fisheries’ Household in Indonesia: Statistical

Analysis

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10:50 - 12:20 RELIABILITY MODELING AND PROCESS MONITORING

Chair: Sheng-Tsaing Tseng (Meeting Room 1)

Yi-Fu Wang

End of Performance Prediction of Lithium-ion Batteries

Su-Fen Yang

A New Loss Control Chart for Monitoring the Deviation of the Quality

Variable from the Target Value

Sheng-Tsaing Tseng

Misspecification Analysis of a pH Acceleration Model

10:50 - 12:20 APPLICATIONS OF DEPENDENT DATA

Chair: Rahim Mahmoudvand (Meeting Room 2)

Vincent Raja

Bivariate Discrete Distributions for Load Sharing Models with Applications

Rahim Mahmoudvand

Is the Exchange Rate Predictable in Long-Run: Experiences from Iranian

Currency?

Diana Abdul Wahab

Decomposition of Graduate’s Gender Wage Gap in the Public and Private

Sectors in Malaysia.


Chair: Shinsuke Kamoto (Discussion Lounge)

Shinsuke Kamoto

Capacity expansion and financial leverage under a potential entry threat

Kwardiniya Andawaningtyas

Analysis of grouping ABC - VED and predicting the number of requests

Fethi Ozbek

Poultry industry production statistics in Turkey

Ani Budi Astuti

Series of Activities for Increasing the Mount Bromo Tourism Business in

Indonesia through Modeling Nonlinear Principal Component Analysis.

12:20- 13:30 Lunch

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13:30 - 15:00 Short course 1: Bayesian Data Analysis (Multi-purpose Hall)


15:25 - 16:25 Short course 2: Bayesian Data Analysis (Multi-purpose Hall)

16:25 - 16:45 Comfort break

16:45 -18:00 Short course 3: Bayesian Data Analysis (Multi-purpose Hall)

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ABSTRACTS

Keynote presentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Statistics in Finance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

y-bis invited session: Statistical Learning Applications . . . . . . . . . . . . . . . . . . . 15

Novel Time Series Approaches with Business and Industry Applications17

ISBA invited session: Bayesian Methods for Business and IndustrialStatistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

Contributed session 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .21

Time Series in Finance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

Machine learning in business . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

Measuring prices of real estate and its challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

Applied statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

IASC invited session: Recent development in complex dependent data . 32


Advanced Methodological Contributions in Time Series . . . . . . . . . . . . . . . . . . . 35

Computer Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37


Reliability Modeling and Process Monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

Applications of Dependent Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44


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KEYNOTE SPEAKER:

Galit Shmueli, National Tsing Hua University, Taiwan

Title: To Explain, To Predict, or To Describe?

Abstract: Statistical modeling is a powerful tool for developing and testing theories by

way of causal explanation, prediction, and description. However, modeling for the purpose

of causal explanation, prediction, or description call for substantially different modeling

and evaluation processes. Conflation of the three as well as under-appreciation of one or

more of these components are dangerous both to scientific research and to practice. In

this talk I will clarify the distinction between the three types of modeling and describe

practical implications in terms of the data analysis process.

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ORGANIZER: WeeYeap Lau, University of Malaya, Malaysia

SESSION TITLE: Statistics in Finance

CHAIR: Indranarain Ramlall, University of Mauritius

• Indranarain Ramlall, University of Mauritius

Title: A Global Analysis of Public Debt-Economic Performance

Nexus in light of the US Subprime Crisis

Abstract: This paper focuses on the interaction between public debt and econo-

mic performance by introducing new instrumental variables, let alone an assessment

of the impact of the 2007-2008 crisis. Findings show that estimation based on over-

looking of endogeneity problem leads to inconsistent sign effects with respect to

public debt. A crisis-induced shift in the tipping point of public debt is noted from

77% to 92%. Similarly, the optimal level of GFCF underwent an increase of 15% post

the onset of the crisis to 52%. For most variables, the effects tend to be pronounced

on real GDP per capita relative to nominal GDP per capita. Continental dum-

mies show high levels of public debts prevailing in America and Asia. Policy-wise,

structural changes may be required in order to effectively deal with crisis-induced

shifts.

• Kee-En Lim and Wee-Yeap Lau, University of Malaya, Malaysia

Title: Effects of 2018-19 US-China Trade War on USD/RMB Ex-

change Rate Volatility

Abstract: This paper examines the potential impact of the trade war on Chinese

currency, RMB. Time series: GARCH model is used to measure the volatility of

RMB before and after the trade war. Initial result reveals that volatility of RMB

increases after Trump became the President. Structural change test also reveals

that the volatility begin after Trump became the president. More GARCH time

series analysis will be conducted on the data. The final implication of this paper is

to prove that: (1) US-China trade war affects RMB volatility (2) determine and me-

asure of the effects of the volatility of China-US investment and international flow.

Using the same program codes and model we will examine the currency volatility

on other currencies from other trade wars. Eventually, we wish to answer whether

the 2018/2019 US-China trade war is affecting RMB volatility.

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• Ahmad Maulin Naufa, Wayan Nuka Lantara; Wee-Yeap Lau , Uni-

versitas Gajah Mada, Malaysia

Title: The Impact of Foreign Ownership to Return Volatility, Vo-

lume and Risk of Stock: Evidence from ASEAN Countries

Abstract: This paper aims to test the effect of foreign ownership on the re-

turn volatility, trading volume, and risk of the stock. Based on a panel data from

six ASEAN Countries (Indonesia, Thailand, Malaysia, Singapore, Philippines, and

Vietnam) from 2011 to 2017. Our results show: First, foreign ownership has a nega-

tive relationship to return volatility and risk of the stock. Second, foreign ownership

has a positive effect on the trading volume. The models are further extended to in-

clude the lag variables and control variables for the currency, domestic shareholders

and others. Although the level of foreign ownership is different for each ASEAN

member countries, our results show the benefit outweighs the cost. The presence of

foreign ownership contributes to the stability of the capital market besides facilita-

ting better governance of the listed firms

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ORGANIZER: Ekin Tahir, Texas State University, USA

SESSION TITLE: y-bis Session: Statistical Learning Applications

CHAIR: Ekin Tahir, Texas State University, USA

• Luca Frigau, University of Cagliari,Italy

Title: Regularization in tree-based classification models

Abstract: In order to enhance the performance of tree-based classifiers, both in

terms of prediction error and interpretability, several strategies may be used, such as

predictor subset selection and regularization. The main problem with these methods

is that they often exhibit high variance. The greedy search for splitting variables

means that small perturbations of the data can lead to dramatically different trees.

In the same way that L1 and L0 penalties improve regression performance, we seek

similar strategies in the context of tree-based classification. In this work we propose

a new regularization strategy for tree-based classification models, one that enhances

model interpretability. It penalizes the number of different variables used in the

tree, and employs bounded greed in building the classification tree. Specifically,

at each node, the choice of the splitting variable favors variables that have already

been used in the tree

• Vadim Sokolov, George Mason University, USA

Title: Strategic Bayesian Asset Allocations

Abstract: Srategic asset allocation requires an investor to select stocks from

a given basket of assets. Bayesian regularization is shown to not only provide

stock selection but also optimal sequential portfolio weights. The perspective of the

investor is to maximize alpha risk-adjusted returns relative to a benchmark index.

Incorporating investor preferences with regularization is related to the approach

of Black (1992) and Puelz (2015). Tailored MCMC algorithms are developed to

calculate portfolio weights and perform selection. We illustrate our methodology

with an application to stock selection from the SP100, and the top fifty holdings of

Renaissance Technologies and Viking Global hedge fund portfolios.

• Ekin Tahir, Texas State University, USA

Title: Probabilistic Variable Accuracy Algorithm

Abstract: The amount of improper payments in U.S. was estimated to be

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141 billion dollars as of 2015. Data accuracy and matching are critical to prevent

such overpayments in many federal benefit programs. Existing exact data mat-

ching algorithms may not address challenges such as the lack of information about

some variables, inaccuracy of self-reported data and matching of data with different

frequencies. We propose Probabilistic Variable Accuracy (PVA) algorithm as a pro-

babilistic data matching classifier. PVA can be used in the initial application and

continuous verification phases. If the provided information is found to be unlikely

in the initial application, the applicant could be asked to confirm the information

before even that application is processed. This can help eliminate some of the basic

applicant mistakes and errors. As part of the continuous verification efforts, PVA

can flag potential life changing events. The underlying idea of PVA is to model how

likely it is to see a particular input value given all other variables and the context.

It is based on basic Bayesian updating via the use of Bayes theorem. This is more

comprehensive than only scoring the eligibility of the application, in addition it

scores the probabilistic accuracy of the reported variable inputs of the data. The

output of the PVA algorithm for each variable and the applicant can be fed into a

decision tool which would compare it with the specified thresholds.

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ORGANIZER: Nalini Ravinshaker, University of Connecticut, USA

SESSION TITLE: Novel Time Series Approaches with Business and

Industry Applications

CHAIR: Nalini Ravishanker, University of Connecticut, USA

• Katherine B. Ensor, Rice University, USA

Title: Time-varying wavelet-based applications for evaluating the

Water- Energy Nexus

Abstract: World leaders have become increasingly concerned about the water-

energy nexus, a concept that refers to the necessity of water in energy production

and the consumption of energy in the extraction, purification, and delivery of wa-

ter. In this paper, we quantify the dynamic relationship between energy and water

commodities. Commodity markets are complex with a wide variety of participants

having different objectives, resulting in nonstationary time series formed by combi-

nations of different components operating at different temporal frequencies. Using

daily water and energy commodity ETF price data from 2007 to 2017 to assess

this complex relationship. The statistical novelty of our approach lies in the use of

time-varying multivariate wavelet techniques with optimal selection of the wavelet

coefficients. Visualization of the structure in multidimensional time series illustrates

the value added in applying time-varying techniques to quantify the relationships

between nonstationary time series.

• Balaji Raman, Cogitaas, India.

Title: Return on Investment Calculator for Promotions in CPG In-

dustry

Abstract: To drive sales, CPG firms provides two types of promotions trade and

consumer promotions. Trade promotions are incentives given to retailers. Consumer

promotions are incentives to consumers to drive sales, market share and penetration.

Nature of promotions vary by SKUs (Stock Keeping Units) and channels. In this

talk, we describe a comprehensive DLM framework to measure promotion effecti-

veness at different levels of hierarchy store, cluster of stores, channels and regions.

This method also accounts for cannibalization across SKUs. The computational

framework for models is R-INLA.

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• Suparna Biswas, ISI Chennai, India

Title: Non-parametric Estimation of Spectral Risk Measures

Abstract: Spectral risk measures (SRMs) belongs to the family of coherent

risk measures. We propose a kernel based estimator of SRM. We prove that the

estimator is strongly consistent and the estimator is asymptotically normal. We

compare the finite sample performance of the kernel based estimator with that of

empirical estimator of SRM using Monte Carlo simulation. Based on our simulation

study we have estimated the exponential SRM of four future index i.e. Nikkei 225,

Dax, FTSE 100 and Hang Seng using our proposed kernel estimator. We shall also

discuss that Kaplan-Meier estimate is a preferable choice than empirical cdf when

we deal with left-truncated right-censored data in the estimation of SRM

18

ORGANIZER: Refik Soyer, George Washington University, USA

SESSION TITLE: ISBA invited session: Bayesian Methods for Bu-

siness and Industrial Statistics.

CHAIR: Refik Soyer, George Washington University, USA

• Hedibert Lopes, INSPER, Sao Paulo, Brazil; Refik Soyer, George

Washington University, USA

Title: Analysis of time series of proportions: a linear Bayes approach

Abstract: Time series of proportions arise in many applications in economics,

finance and marketing. Bayesian analysis of such time series poses computational

challenges due to the lack of analytical forms for sequential Bayesian updating and

requires the use of Markov chain Monte Carlo (MCMC) methods. MCMC methods

are not computationally efficient for sequential analysis and thus, are not attractive

for real-time online processing of time series of proportions. We propose a dynamic

general linear model setup for analysis of proportions and develop linear Bayesian

inference using a class of conjugate priors. We illustrate the implementation of

our approach using actual marketing data and compare our results with previous

findings.

• Refik Soyer, George Washington University, USA

Title: A Bayesian Competing Risks Model for Assessment of Mort-

gage Default/Prepayment

Abstract: In this paper we present a Bayesian competing risks proportional ha-

zards model to describe mortgage defaults and prepayments. We develop Bayesian

inference for the model using Markov chain Monte Carlo methods. Implementation

of the model is illustrated using actual default/prepayment data and additional

insights that can be obtained from the Bayesian analysis are discussed.

• Thomas Mazzuchi, Valeriya Malobrodskaya, Shahram Sarkani, Ge-

orge Washington University, USA

Title: Reliability Growth Test and Evaluation for Multistage Sys-

tems

Abstract: The goal of developmental testing is to ensure that a certain reli-

ability threshold is achieved and to eliminate existing defects before the system is

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fielded. However, reliability prediction through testing can be a challenging process

due to budget constraints and calculation complexity. Military systems, such as

torpedoes and missiles, known as single-shot systems, introduce an additional chal-

lenge. Due to their ever-increasing complexity and growing demands for the reliable

field performance, reliability prediction continues to be one of the biggest challenges

in the development of the military systems. This research describes a methodo-

logy to overcome the aforementioned challenges and enable the measurement and

prediction of reliability growth of a multistage single-shot system. It proposes the

incorporation of existing test data with expert judgment in a Bayesian framework

that allows point and interval estimation of current and future reliability values, as

well as addressing important managerial questions regarding optimal amount of tes-

ting and number of remaining defects. The proposed framework enables continuous

updating of predicted reliability values, removing the dependence on the estimates

formed at the initial stage of testing.

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CONTRIBUTED SESSION 1

CHAIR: Pooi Ah Hin, Sunway University, Malaysia

• Pooi Ah Hin and Ng Yew Seong, Sunway University, Malaysia

Title: Asset Pricing Model with Jumps

Abstract: . The future asset price which is non-concordant with the distribution

estimated from the price today and the prices on a large number of previous days

may be referred to as a jump. The three major characteristics of a jump are the

length of the interval between the occurrence time of the previous jump and that

of the present jump, the indicator which denotes that the jump is up or down by

its values +1 and -1 respectively, and the degree of non-concordance given by the

negative logarithm of the probability of the left tail or right tail of which one of the

end points is given by the observed future price. The vector of three major charac-

teristics of the next jump is modelled to be dependent on the vector corresponding

to the present jump via a 3-dimensional conditional distribution which is derived

from a 6-dimensional power-normal mixture distribution. The distribution the fu-

ture asset price is modelled as a mixture of the distribution based on the historical

data on prices and the distribution of the jump. From the mixture distribution, we

find a nominally-95% prediction interval for the future asset price. The coverage

probability of the prediction interval is found to be much closer to the target value

of 0.95 than that of the prediction interval based on the model which ignores the

jumps

• Ng Yew Seong and Pooi Ah Hin , Sunway University, Malaysia

Title: A Portfolio Trading Strategy

Abstract: This paper aims to derive a strategy to trade a portfolio of two selected

stocks from the Malaysian share market on a monthly basis, that is with a holding

time of one month. Consider a portfolio of wi units of Stock i , i=1,2, with w1 +

w2 = 1. We use the present months values of two latent variables extracted from

a set of 17 selected Malaysian macroeconomic variables, the Malaysian composite

index and volume, and the prices and volumes of Stocks 1 and 2, as the predictors

to predict the next months portfolio value using the multivariate power-normal

distribution. In the proposed portfolio trading strategy, we invest in the portfolio

21

if the estimated probability of making a profit is larger than half, using a value of

w1 which maximizes the average gain per unit of the sum invested. The numerical

results show that compared with the situation in which the estimated probability

of making a profit is less than half , the situation in which the same probability is

larger than half would offer a better opportunity for investing in the portfolio, and

we would make a bigger profit if the optimal w1 is chosen.

• Wayan Nuka Lantara, Ahmad Maulin Naufa, Bowo Setiyono, Uni-

versitas Gadjah Mada, Indonesia

Title: The impact of Net Stable Funding Ratio: Evidence from Indo-

nesian Banks

Abstract: This research aims to explore deeply the impact of Net Stable Funding

Ratio (NSFR) in Indonesian banks. The Indonesian government has issued the rule

of minimum NSFR at least 100% since 2017. Whether the higher NSFR the better

impact to the Indonesian bank, or at the certain level the reversal effect would occur.

Therefore, is the relationship of NSFR to the bank performance in Indonesia either

linear or non-linear? This research would examine that effect on the Indonesian

Banks (private and state) during 2017-2018. We conducted the regression analysis

(ordinary least square) with some multiple robustness tests to test that relationship.

We find that the NSFR provide benefits to enhance the bank performance (net in-

come, ROA, and ROE). The year where the NSFR is mandatory, Indonesian banks

tend to adjust their NSFR to be lower than the previous years where NSFR is not

compulsory.

• Soon Siew Voon, University of Malaya, Malaysia and Ahmad Zu-

baidi Baharumshah, Universiti Putra Malaysia, Malaysia

Title: Asymmetry Causal Relationship between Exchange Rate and

Interest Rate

Abstract: This paper revisits the empirical relationship between the real ex-

change rate and real interest for 8 Asian countries. Our analyses reveal that the

nominal exchange rate is directly responsible for mean-reverting behavior in real

exchange rate in most of the Asian countries. Based on the asymmetric causality

test, we find the dynamic between nominal exchange rate and relative prices has

22

changed after the Asian Financial crisis (AFC). The causal link amongst the inte-

rest rate differential and nominal exchange rate also behave asymmetric across the

sample periods.

23

ORGANIZER: Katherine B. Ensor, Rice University, USA

SESSION TITLE: Time Series in Finance

CHAIR: Katherine B. Ensor, Rice University, USA

• Ananya Lahiri, IIT Tirupati, India

Title: A model driven by mixed fractional Brownian motion in con-

text of finance

Abstract: We will study a model driven by mixed fractional Brownian mo-

tion, some of its statistical properties and also see some application of this model in

context of finance. We also perform some simulation study in support of our results.

• Anindya Goswami, IISER, Pune India.

Title: Testing of Binary Regime Switching Models using Squeeze Du-

ration Analysis

Abstract: We have developed a statistical technique to test the model assump-

tion of binary regime switching extension of the geometric Brownian motion (GBM)

model by proposing a new discriminating statistics. Given a time series data, we

have identified an admissible class of the regime switching candidate models for

the statistical inference. By performing several systematic experiments, we have

successfully shown that the sampling distribution of the test statistics differs dras-

tically, if the model assumption changes from GBM to Markov modulated GBM,

or to semi-Markov modulated GBM. Furthermore, we have implemented this sta-

tistics for testing the regime switching hypothesis with Indian sectoral indices. The

analysis results in rejection of GBM and non-rejection of semi-Markov modulated

GBM hypotheses.

• Nalini Ravishanker, University of Connecticut, USA

Title: Clustering High-Frequency Financial Time Series Based on

Mutual Information

Abstract: High-frequency transaction-by-transaction financial data are readily

available to investors and researchers who seek to analyze these data in order to

understand patterns in the data that will facilitate risk management. Clustering

the stocks based on properties such as returns is often an attractive step to studying

interdependency patterns between them. Under an efficient clustering scheme, we

24

would expect “similar” stocks that stochastically move together (or commove) to be

grouped into the same cluster. The clusters can then provide insight into properties

of the stocks and investors can avoid volatility risk by allocating investments among

the stocks in different clusters. In this talk, our goal is to describe patterns of

comovement over multiple trading days by clustering the stocks using the mutual

information between them. We look at two criteria to decide the optimal number of

clusters, the elbow change point approach and the average silhouette value method.

The elbow change point method tends to generate a small number clusters with

relatively large cluster sizes within any given trading day. By contrast, the average

silhouette value method provides a larger number of smaller sized clusters within a

trading day. We then estimate the comovement probability of top m-tuples of stocks

under the two methods and discuss similarities and differences between them. This

is joint work with Haitao Liu and Jian Zou, Worcester Polytechnic Institute..

25

ORGANIZER: David Banks, Duke University & SAMSI, USA

SESSION TITLE: Machine learning in business

CHAIR: David Banks, Duke University & SAMSI, USA

• David Banks, Duke University & SAMSI

Title: The Bright Future of Experimental Design

Abstract: For decades, I thought that experimental design had become an

intellectual backwater of statistics—useful for some things, but without much new

energy. The machine learning world has breathed new life into that subdiscipline.

This talk describes some of the new challenges and new heuristics that are driving

design forward

• Aniruddha Pant, CEO of AlgoAnalytics

Title: Applications of Machine Learning in Healthcare

Abstract: I will discuss our work in image analytics as well as text analytics

in healthcare domain. Business problems that we will discuss will range from using

AI to assist digitization of healthcare data to using this digitized data to provide

diagnostic assistance to healthcare providers. In specific I will talk about a suit

of Analytics components around Electronic Healthcare data. On the technology

side we will cover application of classical machine learning techniques like Random

Forests and Support Vector Machines as well as deep learning methods like convo-

lutional neural networks and recurrent neural networks.

26

ORGANIZER: Toh Hock Chai, Bank Negara, Malaysia

SESSION TITLE: Measuring prices of real estate and its challenges

CHAIR: Toh Hock Chai, Bank Negara, Malaysia

• Jens Mehrhoff, Deutsche Bundesbank

Title: Commercial Real Estate Indicators: Prices and Beyond

Abstract: As the global financial crisis has impressively shown, changes in real

estate prices influence the health and soundness of the financial sector. However, the

effective monitoring of the markets is severely hampered by the lack of comparable

and reliable data. While good progress has been made as regards the compilation

and dissemination of housing price statistics, the compilation of commercial real

estate (CRE) price and associated indicators remains very challenging. Against this

background and in the context of the G20 Data Gaps Initiative, Eurostat publis-

hed in December 2017, under the auspices of the Inter-secretariat Working Group

on Price Statistics (IWGPS), a Statistical Report on ’Commercial Property Price

Indicators: Sources, Methods and Issues’ that makes a first attempt at setting out

the wide range of challenges linked to the measurement of CRE. This talk would

present, and invite to discuss, the current state of play as well as the way for-

ward on CRE price and associated indicators. This includes but is not limited

to: 1. data sources for commercial real estate statistics, such as the combination

of different administrative sources, if need be with specialised surveys; valuation

data from, say, investment funds or loan collateralisation; disaggregated data from

private sources; asking data or other big data sources; and stock-market based infor-

mation; 2. conceptual frameworks for commercial real estate indicators, examples

include how the interrelationship between prices, rents and yields (and potenti-

ally vacancy rates) can be exploited; the definition of commercial real estate; the

classification of properties in this domain; and aggregation and weighting issues;

and 3. purposes and uses of commercial real estate indicators and related targets,

comprising the monitoring of mainly disaggregated data rather than country-wide

level; the use of transaction or stock weights for price and associated indicators;

the inclusion or exclusion of rental housing in measures of CRE; and a review of

additional indicators on construction and transactions. To this end, this talk will

reflect upon the most recent developments at the European and the global level;

27

in particular, the follow-ups from the International Conference on Real Estate Sta-

tistics (https://www.real-estate-statistics.eu/) Eurostat organised, in close coopera-

tion with the ECB, in Luxembourg from 20 to 22 February 2019 that was preceded

by a G20 Workshop to advance implementation of Recommendation II.18 on Com-

mercial Property Price Indices (CPPIs) of the Second Phase of the G20 Data Gaps

Initiative (DGI-2).

• Nur Fazila Mat Salleh, Bank Negara Malaysia

Title: Enterprise Data Governance and Direction Going Forward

Abstract: Enterprise data governance comprises a set of policies, procedures

and processes, and standards to ensure that data are formally managed and utili-

sed throughout the organisation. However, enforcing a data governance framework

within an organisation is always not easy due to factors such as conflicting claims

on data ownership, increasing and changing demand of data by policy makers, and

need to balance the burden to reporting entities and compilers against the needs

of data users. The presentation aims to provide insights on the establishment and

implementation of data governance at Bank Negara Malaysia and how does it facili-

tate an end-to-end data management. It will also highlight the role and importance

of IT in supporting the governance framework, and how can we align business and

IT to achieve better data management.

• Soong Kim Loong, Maybank, Malaysia

Title: Data Governance: Adapting to the Digital Age

Abstract: The complexity of managing data has intensified with the onslaught

of exponential increase in data volume, speed and variety. We are increasingly

tasked to store and manage new data that we dont normally think of as traditional

reporting data such as web logs or geo-location data. Data that we manage are

extending beyond structured data that we are familiar with like relational databases.

More and more data are being generated by machine in addition to human. All

these challenges coupled with the expectation to turnaround data from creation to

analytics in the shortest possible time highlighted the need to pay more attention

to how we proactively manage and govern data at various critical data touch points.

This paper will discuss how data governance as a discipline has been increasingly

28

pushed to the forefront of data management and how do we adapt to the fast evolving

data landscape.

• Silke Stapel-Weber, European Central Bank

Title: ECB data governance for better analysis and decision-making

Abstract: Data are necessary to enable analysis and support decision-making.

The paper will outline the ECB data governance structure: how do we foster stra-

tegic alignment, standardisation, as well as collaboration. How do we address the

challenges to data access to exploit the wealth of data available within the ECB

whilst safeguarding confidentiality? And how do we best leverage the data exper-

tise available across business areas?

29

ORGANIZER: WeeYeap Lau, University of Malaya, Malaysia

SESSION TITLE: Applied statistics

CHAIR: Muzalwana Binti Abdul Talib, University of Malaya, Malaysia

• Muzalwana Binti Abdul Talib, Wan Katrun Nadia Wan Yusof,

University of Malaya, Malaysia

Title: Air Quality Monitoring with Exponentially Weighted Moving

Average (EWMA) control charts

Abstract: Air quality monitoring, testing, and measuring are becoming in-

creasingly sophisticated with the widespread adoption of technological equipment.

However, interpreting monitoring data and deciding when and how to apply envi-

ronmental management remains a subjective and underdeveloped area of research.

Control charts, as originally developed for industrial applications, offer some pro-

mises in this regard. Control charting scheme can provide an approach to identify

when a system is going out of control. When such signals identified, corrective

measures can be taken. This technique would be practical in the context of envi-

ronmental monitoring. Despite their potential utilization, control charts have rarely

been adopted in environmental management, especially in air quality monitoring.

There are very few literatures on similar study or have applied a broad range of

statistical process control methods, that includes control charts applications. Based

on these theoretical discussions and applications, this paper presents the develop-

ment of control chart scheme of air quality performance in the Central region of

Malaysia from 2010 to 2015. Two control charts are applied: x -R control chart and

Exponentially Weighted Moving Average (EWMA) control chart. EWMA control

chart are specifically plotted to detect subtler shift away from a mean trend and

hence provide a more consistent warning of the decline in air quality. We believe

that control chart could have clearly communicated this process earlier, enabling

the environmental personnel to make decision effectively.

• Ahmad Farid Osman, University of Malaya, Malaysia

Title: Forecasting weekly electricity demand in Malaysia using ex-

ponential smoothing approaches

Abstract: The study employs time series approaches to generate forecasts of wee-

30

kly electricity demand. In time series modelling, it is common to consider monthly

or quarterly seasonal effect. Due to change of weather and other environmental fac-

tors throughout the year, producing forecasts of electricity demand on quarterly or

monthly basis might not be sufficient to provide input for efficient management of

electricity supply. This study proposes modified modeling and forecasting procedure

that is suitable for weekly frequency of seasonality in the data with the use of two

time series approaches, namely, ETS and ESWR types of exponential smoothing

techniques.

• Siow-Li Lai and Tien-Ming Yip, University of Malaya, Malaysia

Title: The non-linear nexus between financial development and fer-

tility: A dynamic panel GMM analysi

Abstract: Fertility plays a vital role in regulating contemporary population

growth, in which mortality has reached a low level. Reducing fertility was seen

as a prerequisite to national development and poverty eradication in the 1960s/70s.

However, fertility far below replacement level in recent decades has given rise to con-

cern of population ageing and the emergence of labor shortage in the near future.

A comprehensive analysis of the forces affecting fertility is needed to provide the

necessary inputs to be used in development planning. This study aims to investigate

the relationship between financial development and fertility in 65 countries between

2001 and 2015. The dynamic panel GMM analysis was used to build the models

for fertility-financial development analysis, controlling for other variables such as

infant mortality rate, female education, and urbanization. Results show that finan-

cial development has a non-linear inverted U-shaped relationship with total fertility

rate in developing countries, but a non-linear U-shaped association is observed in

developed countries. This study draws some imperative policy recommendations.

31

ORGANIZER: Guodong Li, University of Hong Kong

SESSION TITLE: IASC invited session: Recent development in

complex dependent data

CHAIR: Lionel Truquet, ENSAI, France

• Liudas Giraitis, Queen Mary, University of London, UK

Title: Standard testing procedures for white noise and heteros-

cedasticity

Abstract: Commonly used tests to assess evidence for the absence of serial cor-

relation between time series in applied work rely on procedures whose validity holds

for i.i.d. data. When the series are not i.i.d., the size of correlogram and cumulative

Ljung-Box tests can be significantly distorted. This paper adapts standard corre-

logram tests to accommodate hidden dependence and non-stationarities involving

heteroskedasticity, thereby uncoupling these tests from limiting assumptions that

reduce their applicability in empirical work. To enhance the Ljung-Box test for non

i.i.d. data a new cumulative test is introduced. Asymptotic size of these tests is

unaffected by hidden dependence and heteroskedasticity in the series. An exten-

sive Monte Carlo study confirms good performance in both size and power for the

new tests. Applications to real data reveal that standard tests frequently produce

spurious evidence of serial correlation.

• Lionel Truquet, ENSAI, France

Title: Time-varying time series models for categorical data

Abstract: We will present a general framework for modeling the dynamic

of categorical time series using autoregressive processes and which is compatible

with the inclusion of strictly exogenous covariates. The case of finite and infinite

dependence with respect to past values will be discussed as well as the possibility to

include time-varying parameters. Theoretical properties are justified either by some

techniques developed for studying Markov chains in random environments or by

some coupling methods for chains with complete connections. Our results also give

a theoretical basis for some observation-driven models introduced in econometrics

for studying the dynamic of recessions or price movements.

32


CHAIR: Wiji Tri Wilujeng, Price Directorate BPS, Indonesia

• Wiji Tri Wilujeng, Price Directorate BPS, Indonesia

Title: Advertised Landed House Price in Balikpapan

Abstract: Online advertised landed house price is easily available data. Research

schedule and computer time computation are our constraint to do this research.

From the sample set of 101 advertised landed house prices, 16 observations are

separated as leverage. Leverage is defined as an observation that has land area

more than 300 square meters or building area more than 400 square meters or both.

Covariance and variance are calculated then a matrix is constructed. Determinant

is calculated from covariance matrix in iterative fashion. Iteration is performed to

find minimum determinant. Bivariate Minimum Covariance Determinant [MCD]

is applied to online advertised landed house price data to identify non-Extreme

outlier. Houses in Perumnas, Rumah Wika, Balikpapan Baru, Gunung Sari Ilir,

and Sepinggan Baru are non-Extreme outlier in Balikpapan.

• Deja Firda, North Penajam District Statistical Office, Indonesia

Title: Bivariate Landed House Data using Minimum Area of Ellipse

Abstract: In this research we use housing data located in Balikpapan taken

from online advertised landed house price. We used 124 sample data of landed

house price, size of land, and size of building. Seven landed house are separated

because they are considered as leverage. Leverage is defined as data that is far away

from the bulk of the gro up of the data [Rousseeuw and Zomeren, 1990]. Minimum

area of an ellipse is used to find non-Extreme outlier. Houses in Prapatan Dalam,

Kruing Timur, Batu Ampar are considered as non-Extreme outliers. Minimum area

of an ellipse is used to find non-Extreme outlier. Houses in Tampak Siring [sector

3], Perumahan Pondok Karya Agung, Perum Tamansari, Bukit Mutiara (Wika) are

non-Extreme outliers.

• Astari Raihanah, Sarmi District Statistical Office, Indonesia

Title: Pearson product moment is used more than just measuring

correlation

Abstract: This research uses data from Bengkulu. Firstly from the sample set of

33

advertised landed house prices, 9 observations are separated because the prices are

more than one milliard in local currency. Secondly 11 observations are separated as

leverage. Leverage is defined as an observation that has foot print more than 400

square meters or floor area more than 200 square meters or both. Thirdly Pearson

product moment is applied to online advertised landed house price data to identify

non-Extreme outlier. Houses in Simpang SMP 4 and Sungai Rapat are considered

as non-Extreme outlier in Bengkulu

• Ang Siew Ling, Pooi Ah Hin, Sunway University, Malaysia and Ng

Kok Haur, University of Malaysia

Title: Improvement of Reserve Estimate Using Information on Sub-

classes of Insurance

Abstract: This paper aims to use the individual claims data for estimating

reserve in nonlife insurance. Suppose the claims data of individual customers con-

tain the additional information on their subclasses of insurance, apart from the

delay times in reporting the claims, delay times in payments and the corresponding

amounts of claims / payments. Each subclass may initially be represented by binary

codes. A mixture of two multivariate power-normal distributions and a degenerate

distribution is fitted to the observed vectors of variables consisting of the binary

codes, the sum insured, the claim and payment records until the present time, and

the outstanding claims, liabilities (OCL). When the subclass, the sum insured, and

the claim and payment records of a customer until the present time are given, a

conditional distribution of the OCL is derived from the fitted mixture distribution.

From the conditional distribution, a prediction interval for the OCL is obtained. It

is found that the average length of the prediction interval for the OCL is shorter

when the information on the subclasses of insurance is included in the analysis. The

conditional distribution of the individual OCL is later used to find the distribution

of the sum of the OCL over the customers in a company. The Provision of Risk

Margin of Adverse Deviation (PRAD) can then be obtained from the distribution

of the sum of the OCL

34

ORGANIZER: Chun Yip, The Chinese University of Hong Kong

SESSION TITLE: Advanced Methodological Contributions in Time

Series

CHAIR: Yuanbo Li, University of International Business and Economics, Beijing,

China

• Yuanbo Li, University of International Business and Economics, Be-

ijing, China

Title: Group Orthogonal Greedy Algorithm for Change-point Esti-

mation of Multivariate Time Series

Abstract: We propose a three-step method for the detection of multiple structural

breaks in piecewise stationary vector autoregressive processes. The number of the

structural breaks can be large and unknown. Moreover, the number and the location

of the breaks are not necessarily the same in diff erent components. The proposed

method is based on a connection between structural break problem and high dimen-

sional regression problem. With such connection, we develop a group orthogonal

greedy algorithm, originally from high dimensional variable selection context, for

efficient estimation of structural breaks. A high-dimensional information criterion

is proposed to detect different structural breaks in different components. We prove

the consistency of the estimators and provide Monte Carlo experiments for the finite

sample performance.

• Zhan Liu, Hubei University, China

Title: Fitting time series models for longitudinal surveys with non-

ignorable missing data

Abstract: We develop a new method for handling nonignorable missing data

in fitting time series models for longitudinal surveys. We assume the response

probability not only depends on auxiliary variables but also the current outcome and

the past outcome which are subject to missingness. To incorporate the nonignorable

missing mechanism, an observed likelihood estimation approach is proposed based

on the distribution of the observed part of the sample and the response probability.

Also, we derive a series approximation for the observed likelihood function to achieve

efficient computation. Results from simulation studies are presented to demonstrate

35

the usefulness of the proposed methodology. An empirical example based on a panel

study of income dynamics is provided.

• Chi Tim Ng, Chonnam National University, South Korea

Title: Real time prediction of irregular periodic time series data

Abstract: By means of a novel time-dependent cumulated variation penalty

function, a new class of real-time prediction methods is developed to improve the

prediction accuracy of time series exhibiting irregular periodic patterns, in particu-

lar, the breathing motion data of the patients during the robotic radiation therapy.

The proposed methods are designed so that real-time updates can be done efficiently

with O(1) computational complexity upon the arrival of a new signal without scan-

ning the old data repeatedly. The performances are tested via simulation under

models involving abrupt changes and gradual changes in mean, trend, amplitude,

and frequency.

36

ORGANIZER: Grazia Vicario, Politecnico of Turin, Italy

SESSION TITLE: Computer Experiments

CHAIR: Alessandro Fasso, University of Bergamo, Italy

• Matthias Hwai, City University of Hong Kong

Title: Gaussian Process Modeling and Optimization of Simulators

for Physical Systems

Abstract: Gaussian process (GP) emulators are typically constructed to replace

time consuming simulators of physical systems to expedite quantitative analysis that

depends on the functional relationship between inputs and outputs of the simulator.

In this talk, I will first introduce the idea of using GP models to approximate

partial differential equation models solved numerically by computer codes. Then, I

will present a real case study on optimizing the engineering design of a centrifugal

compressor based on a very time-consuming simulator.

• Xinwei Deng, Virginia Tech, USA

Title: Gaussian Process Models for Computer Experiments with

Quantitative and Qualitative Inputs

Abstract: Computer experiments with both qualitative and quantitative factors

attracts wide attentions. Analysis of such experiments is not yet completely resol-

ved. In this talk, we present some recent development Gaussian process to model

computer experiments with qualitative and quantitative factors. The proposed me-

thods consider the coefficient associated with the qualitative factor to be a varying

coefficient of the quantitative factors. It embraces a flexible structure of incorpora-

ting qualitative factors in modeling the complex systems of computer experiments.

The merits of the proposed method are illustrated by both numerical examples and

real-data applications

• Alessandro Fasso, University of Bergamo, Italy

Title: Gaussian Processes and LASSO for change detection of 4D

climate datasets

Abstract: This talk will discuss the Gaussian Process modelling and change

detection of temperature profiles from the Integrated Global Radiosonde Archive

(IGRA) which consists of global radiosonde observations dating back to 1905. Change

37

detection methods developed for radiosonde have a long history. In this paper, a

locally stationary 4D geostatistical model coupled with fused LASSO is used for

identifying changes.

38


CHAIR: Titi Kanti Lestari, BPS Statistics, Indonesia

• Tigor Nirman Simanjuntak and Sri Hartini Rachmad, BPS Sta-

tistics, Indonesia

Title: Gender statistics of household fisheries industry in Indonesia

Abstract: This study aims to examine and analyze the characteristics of hou-

sehold of fishery business based on gender in Indonesia. Sources of data used are

specifically from latest Agriculture Census 2013 data collected by the Central Bureau

of Statistics Indonesia. The data show that more than 90 percent of fish cultivators

are dominated by men for all types of aquaculture fish (tilapia, koi, shrimp windhu,

and seaweed). The majority of fish farmers are concentrated in productive age (25-

54 years) by 63 percent and 16 percent are 55 years and older. The education level

of women in the fish farming business is very low, the majority is only elementary

school graduates and even more than 60 percent are no schooling. Koi fish farmers

have the highest level of education. Given the koi fish aqua culture is a superior

product of exports, it requires special knowledge and skills to cultivate this type of

koi ornamental fish. The low participation of women in fish cultivation industry and

profession as fisherman shows that male dominance, tradition and culture in this

sector lead to small space for women. In addition, the position and status of Indo-

nesian women who have multiple roles, namely: as family caretaker and household

income support, not as the main breadwinner cause that low participation.

• Suryo Adi Rakhmawan, Ema Tusianti, Abdurrahman, BPS-Statistics,

Indonesia

Title: The gender gap on formal worker participation in Indonesia:

Trend and the way forward

Abstract: This research aims to analyse trend of gender gap on formal em-

ployment and influential factors of formal employment participation by gender. By

harnessing descriptive analysis this study reveals that gender gap exists in some

characteristics (location, economic sector, education level, and access to training).

Meanwhile, multilevel panel analysis shows that mean years of schooling, produc-

tive population number, per capita expenditure, training accessibility, GRDP and

39

manufacture share on GRDP significantly determine number of women and man

working on formal status. Share of manufacture on GRDP and education are the

highest contributor of women involving on formal works. Thus, increasing those

factors might reduce gender gap on formal work as a part of decent work for all

target.

• Erli Wijayanti Prastiwi, Karmila Maharani, BPS-Statistics, Indo-

nesia

Title: Women and technical efficiency analysis of the creative ma-

nufacturing industry in Indonesia

Abstract: The world is now facing the digital revolution especially in indu-

stry development that has been occurring since decades ago. Technology usage and

development have been raised dramatically in the past decade, making a fusion

that blurs lines between the physical, digital, and biological spheres. Several new

products, goods and services as well as job creations are emerged. One of them is

called creative economy, a fresh economic research area combining technology and

heritage at once. Indonesia as one of the most populous country in the world is

getting ready to develop creative economy while it is growing fast with very unique

case. Most of creative economy entrepreneurs are women by 54.96 percent in 2017

with domination of young and productive labor in age 25 59 years old. This paper

aims to measure the Technical Efficiency (TE) of creative manufacturing industry in

Indonesia 2017, the factors affecting the technical inefficiency effect (TIE) by using

Stochastic Frontier Analysis (SFA), and how women can contribute to the efficiency

enhancement. The data source of this research is from annual Small Medium Enter-

prises Survey held by Statistics Indonesia with classification for food and beverage

industry, fashion industry, and craft industry. The result shows that there is tech-

nical inefficiency in the three industries. It means that there is chance to achieve

optimum production with combination of current inputs. The factors significantly

affect the Technical Inefficiency Effect (TIE) of the three industry are identified.

The contributions of this paper are not only for the government but also for entre-

preneurs creative manufacturing Industry in Indonesia in as well as for those who

focus on women empowerment in creative economy especially in manufacturing in-

dustry. This paper provides other perspective about productivity measurement as

40

well as the implementation to improve the capability of the business process.

• Lilis Heri Mis Cicih, University of Indonesia, Sri Hartini, BPS-

Statistics, Indonesia

Title: Gender Mainstreaming in Fisheries’ Household in Indonesia:

Statistical Analysis

Abstract: Women play a significant role in fishing activities, but their role has

not been taken into account and involved in fisheries development. The general

objective of this research is to analyze the role of gender in fisheries households in

Indonesia. The specific research objectives are: 1. to know household characteris-

tics factors which is related to the role of gender in the fishery household; 2. to

know the socio-economic conditions; 3. analyze the relationship between household

characteristic factors which is related to the role of gender in the fishery household;

4. analyze the relationship between socio-economic conditions with decision making

in the household fishery. The research method used in this research is a combina-

tion of quantitative and qualitative methods. This combination is done to enrich

the data and better understand the social phenomena studied. Qualitative appro-

ach used in this research is descriptive case study method, to know the description

of research location and general description of respondents. Qualitative data was

obtained through in-depth interviews and direct observation at the research sites

to deliberate subjective understanding of the respondents. Quantitative approach

used is to process data survey and census results been collected by national statistics

Indonesia. This quantitative research is explanatory research explaining the causal

relationship between variables through hypothesis testing. The data gathering of

quantitative and qualitative simultaneously would exploit the phenomenon and ex-

pected to find best problem solve in the gender fisheries of Indonesia experience. The

result shows that household characteristics and socioeconomic factors are linked to

gender roles and decision making in fisheries households. The community attitude

may be influenced by work opportunity and culture through family education. The

coastal fishery development program which is gender sensitive has the most chance

and is considered important by stakeholders to be implemented is human resource

development program for male and female.

41

ORGANIZER: Sheng-Tsaing Tseng, National Tsing Hua University, Taiwan

SESSION TITLE: Reliability Modeling and Process Monitoring

CHAIR: Sheng-Tsaing Tseng, Institute of Statistics & National Tsing Hua

University, Taiwan

• Yi-Fu Wang, Department of Mathematics, National Chung Cheng

University, Chiayi, Taiwan, (co-authors: Sheng-Tsaing Tseng, Bo

Henry Lindqvist and Kwok-Leung Tsui)

Title: End of Performance Prediction of Lithium-ion Batteries

Abstract: Rechargeable batteries are critical components for the performance

of portable electronics and electric vehicles. The long term health performance of

rechargeable batteries is characterized by state of health which can be quantified

by end of performance (EOP) and remaining useful performance. Focusing on EOP

prediction, this paper first proposes an accelerated testing version of the trend-

renewal process model to address this decision problem. The proposed model is also

applied to a real case study. Finally, a NASA dataset is used to address the predic-

tion performance of the proposed model. Comparing with the existing prediction

methods and time series models, our proposed procedure has better performance in

the EOP prediction.

• Su-Fen Yang, Department of Statistics, National Chengchi Univer-

sity, Taiwan

Title: A New Loss Control Chart for Monitoring the Deviation of

the Quality Variable from the Target Value

Abstract: Almost all businesses strive to offer superior customers service. The

quality and loss of products are crucial factors among competitive businesses in glo-

bal market. Firms widely employ a loss function to measure the loss caused by poor

quality of products. The quality of products can be measured by the deviation from

the process target value from the Taguchis philosophy viewpoint. In reality, there

are many situations where the process distributions are not normal. This paper aims

at developing the loss-based control charts for monitoring the deviation of quality

variable from the target value under non-normal distribution. Numerical results

show the out-of-control detection performance of the proposed loss control charts.

42

Key words: Loss function, control chart, target value, non-normal distribution.

• Sheng-Tsaing Tseng, Institute of Statistics, David Shan Hill Wong,

National Tsing Hua University, Taiwan

Title: Misspecification Analysis of a pH Acceleration Model

Abstract: Shelf-life prediction of liquid-type (such as nano-sol) products is an

interesting research topic. Recently, a pH acceleration method has been proposed

in the literature to address shelf-life prediction of nano-sol. The time evolution of

particle-size distribution was obtained and modelled. There are two approaches for

modelling the particle-size distribution, either by using a sophisticated approach

(mixture-normal distribution) or a naive distribution-free approach. The main goal

of this study is to proactively quantify the seriousness of model misspecification on

the shelf-life prediction when the true particle-size distribution follows a mixture-

normal distribution, but wrongly treated it by a distribution-free model. The results

demonstrated that the relative bias of shelf-life prediction may be under-estimated

up to 13.66Keywords: Mis-specification analysis; Shelf-life prediction for Nano-sol;

pH acceleration model; Mixture-normal distribution; Nonparametric Model

43

ORGANIZER: Gopalakrishnan Asha, Cochin University of Science and Tech-

nology, India

SESSION TITLE: Applications of Dependent Data

CHAIR: Rahim Mahmoudvand, Bu-Ali Sina University, Iran

• Vincent Raja, University of Guyana, Guyana & Gopalakrishnan

Asha, Cochin University of Science and Technology, India

Title: Bivariate Discrete Distributions for Load Sharing Models

with Applications

Abstract: In reliability literature, lifetime data has been analysed by variety

of bivariate distributions. Most of the existing models deal with continuous failu-

res rates. Discrete failure rates quite often occur with situation where product or

organ lifetime can best be described through non-negative integer valued random

variables. Motivated by this, we propose a general class of bivariate discrete load

share model for two component parallel system. Both the components are assumed

with independent failure rates and the dependence occurs when one of the two com-

ponents fails. We study the general properties of the model. A particular example

using geometric as the baseline distribution and its characterisations have been stu-

died in detail. A Maximum Likelihhod estimation procedure is discussed. Two real

time data sets one of which is a diabetic foot ulcer data, are analyzed to show our

model applicability. We conclude with summary and discussion.

• Rahim Mahmoudvand, Bu-Ali Sina University, Iran

Title: Is the Exchange Rate Predictable in Long-Run: Experiences

from Iranian Currency?

Abstract: Exchange rates are among the most important economic indices that

have a big influence on the level of imports and exports, domestic and international

markets, and inflation. On the other hand, exchange rates are affected by many

highly correlated economic, political and even psychological factors. The interaction

of these factors is in a very complex fashion. Therefore, forecasting the changes

of foreign exchange rates is generally very difficult. However, obtaining a precise

prediction of exchange rate can be very useful for investors and government. In

this paper, we consider monthly exchange rate between the Iranian Rial (IRR) and

44

the US Dollar (USD). Since the beginning of 2018 IRR has depreciated by nearly

70 percent against its benchmark. Most experts believe this situation as the effect

of rising tensions in US-Iran relations, particularly the USs withdrawal from the

landmark 2015 Iran nuclear deal. However, we believe that there is a strong cyclic

behavior in IRR. We guess such behavior is due to the presidential change in Iran.

In order to assess our proposal, we examined the predictability of IRR/USD in long-

run using monthly data over the period 1981 to 2018. A preliminary analysis of the

original time series reveals a strong positive trend. For further analysis, we adjusted

the original time series for each presidential term by dividing exchange rate to the

first value of that period. The new time series reveal a strong negative trend and

harmonic components with frequencies 4 and 8 years. As a conclusion, the results

confirm that IRR/USD is predictable in long-run.

• Diana Abdul Wahab, University of Malaya, Malaysia

Title: Decomposition of Graduate’s Gender Wage Gap in the Public

and Private Sectors in Malaysia.

Abstract: This paper studies aspects related to the public-private sectors wage

determination for fresh graduates and the evidence of gender wage gap between and

within industries. Using the national data from the Tracer Study in 2013 involving

first degree graduates from higher learning institutions in Malaysia, the first part

discusses the determinants of the overall gender pay gap for all graduates where it is

found that graduate’s income is determined by their academic achievement, English

proficiency, family income, and specific types of courses they took in university.

The income difference between male and female graduates is computed using the

Oaxaca-Blinder (OB) decomposition. The results from separate ordered choice esti-

mation for each gender are decomposed into two portions: one portion represents

difference in wages between two graduates having different characteristics, where

we would expect individual with higher productive characteristics to earn more;

and one portion explains the difference in the earning among two graduates pos-

sessing similar characteristics and job attributes and hence may provide potential

evidence of discrimination. The average earning of public sector workers is found to

be slightly lower than private sector workers but not significant. Detailed analysis

of earning found that entry level earning between public and private sectors is not

45

significantly different, but men may earn more in the private sector while Malays

may earn more in the public sector. Higher earners in the public sector generally

have higher CGPA, indicating that graduates with better academic achievement

may seek public sector employment for its monetary reward. English language is

not an important element in the public sector earning determination. Business sub-

jects are rewarded more in the private sector, while graduates who took Sciences

subjects, Arts and Social Sciences may earn more if they work in the public sector.

46


CHAIR: Shinsuke Kamoto, Kagawa University, Japan

• Ani Budi Astuti, University of Brawijaya Malang, Indonesia

Title: Series of Activities for Increasing the Mount Bromo Tourism

Business in Indonesia through Modeling Nonlinear Principal Compo-

nent Analysis

Abstract: The Mount Bromo is one of the most exotic tourist destinations in In-

donesia and very famous so that it is visited by many domestic and foreign tourists.

The location of Mount Bromo is in East Java Province, precisely surrounded by four

regency government regions, namely Probolinggo Regency, Pasuruan Regency, Ma-

lang Regency and Lumajang Regency. Various series of activities from the tourism

management of Mount Bromo continue to be carried out in an effort to improve the

quality of tourism to maintain and increase visits of domestic and foreign tourists.

Through the Modeling Nonlinear Principal Component Analysis with mixed type

latent variables, we can know the main influences of various variables that determine

satisfaction and interest in visiting tourists at Mount Bromo. The purpose of this

study is to identify the main indicators of each mixed type latent variable as a va-

riable determinant of satisfaction and interest in visiting for Mount Bromo tourists

in Indonesia through Modeling Nonlinear Principal Component Analysis. The data

used in this study are primary data from respondents from Mount Bromo tourists

in Indonesia at 2018. The results showed that the mixed demographic latent varia-

bles with five indicators can be formed by a single nonlinear principal component

with the dominant contribution of indicators indicated by the last education and

age from tourists. The service quality latent variables with five indicators can be

formed by a single linear principal component with the contribution of the domi-

nant indicators in responsiveness and assurance. The marketing mix latent variable

with seven indicators can be formed by a single linear principal component with

the dominant indicator contribution to the process and promotion. The Electronic

Word of Mouth (e-WoM) latent variables with four indicators can be formed by a

single linear principal component with the contribution of the dominant indicators

on expressing positive feelings and concern for other.

47

• Shinsuke Kamoto, Kagawa University, Japan

Title: Capacity expansion and financial leverage under a potential

entry threat

Abstract: The study examines capacity expansion strategy and leverage choice

of an incumbent firm in the presence of a potential entry threat by a new entrant.

The model presented in this paper is based on real options theory that examines in-

vestment decision under uncertainty over future market environments. It formulates

the capacity expansion strategy regarding its timing and size and leverage choice as

an optimal decision problem that maximizes the value of the firm under uncertainty

over a future product price. The problem is solved subject to the optimal response

to the potential competitors market entry decision. The model demonstrates that

an incumbent monopolist undertakes capacity expansion with the strategic motiva-

tion to delay the market entry by a potential competitor for prolonging a period

of monopoly status. The model also demonstrates the interaction between the in-

cumbents capacity expansion strategy and leverage choice under a potential entry

threat. In addition, the model demonstrates that high leverage of the incumbent

can induce the potential competitor to enter the market in the industry downturn

in order to force the monopolist to go bankrupt. This study contributes the lite-

rature in real options theory by examining interactions between strategic capacity

expansion strategy and financial leverage choices in the presence of a potential en-

try threat. The results presented in this study would provide useful insights into

strategic aspects regarding corporate investment and financing policies.

48

• Kwardiniya Andawaningtyas, University of Brawijaya Malang,

Indonesia

Title: Analysis of grouping ABC - VED and predicting the number

of requests

Abstract: Market competition requires a company to be able to optimize

the management of the company that are appropriate, effective and efficient. It is

considered very important because of the rapid development of business in this mil-

lennium era, so it can compete with other companies to be able to meet consumer

demand. To plan management at TB. Bina Usaha Temanggung Indonesia is used

ABC analysis to sort items that dominate sales based on investment value. VED

analysis aims to classify goods based on the level of importance of store owner’s

sales needs into 3 categories, namely Vital, Essential, and Desirable. Classification

of the drug using ABC VED analysis produce a matrix that differentiates into three

categories, namely category I is composed of AV, AE, AD, BV and CV; category II

consists of BE, CE, and BD; and the third category consists of CD. The results of

the ABC-VED analysis are then predicted the number of requests for goods with

the Double Exponential Smoothing method from Brown. This forecasting method

is used when the data indicate a trend. Trend is a smoothed estimate of average

growth at the end of each period. Forecasting results are expected to help plan in-

ventory for the coming year at TB. Bina Usaha Temanggung Indonesia to prioritize

sales.

• Fethi Ozbek, Turkish Statistical Institute, Turkey

Title: Poultry industry production statistics in Turkey

Abstract: Poultry industry production statistics cover annual and monthly

studies. The aims of the study are to monitor the developments in the economic

structure of Turkey, to compile data sets for monitoring the status of poultry hol-

dings, to obtain the information used for economic and social measures taken by

decision makers, to prepare data for various studies for defining poultry policies, to

define the share of sector in national income, to compile data in coherent with Eu-

ropean Union animal production legislation. Poultry and eggs production statistics’

studies are only conducted to poultry holdings in industry (excluding household

poultry farms). The following variables are produced; hen eggs, eggs placed in in-

49

cubation for chicks of broiler, number of hatched chicks for broiler, eggs placed in

incubation for chicks of laying hen, number of hatched chicks for laying hen, eggs

placed in incubation for chicks of turkey, number of hatched chicks for turkey, eggs

placed in incubation for chicks of quail, number of hatched chicks for quail, slaugh-

tered broiler, broiler meat, slaughtered turkey, turkey meat, slaughtered quail, quail

meat. Seasonally adjustment methods have been applied for hen eggs production,

slaughtered chicken numbers, produced chicken meat, slaughtered turkey numbers,

and produced turkey meat since 2014. Seasonally adjusted data have been publis-

hed monthly. The seasonal adjustment of poultry production statistics is carried

out using TRAMO-SEATS methodology. The software that is used for the applica-

tion of this method is 2.2.0 version of JDemetra+ developed by the National Bank

of Belgium (NBB) in cooperation with the Deutsche Bundesbank and Eurostat in

accordance with the Guidelines of the European Statistical System.

50

Documents

ISBIS 2019 Satellite Conference August 15-16 2019 Lanai Kijang … book ISBIS 2019 Conference.pdf · 2019-08-12 · 15:20 - 16:50 TIME SERIES IN FINANCE Chair: Katherine B. Ensor