Satawat Wannapan

The Optimizing Algorithm for Economic Cycles in ASEAN Stock Indexes

This paper is aimed to employ econometric tools for clarifying switching regimes inside time-series trends of ASEAN’s stock indexes as well as suggesting the proportional target to invest in an optimal portfolio The tools are the Markov-Switching model (MS-model) and Markovian optimization for portfolio model selecting, respectively. Daily sampled stock variables in five sectors such as banking system, energy, financial agriculture, telecommunication, and real estates were collected during 2010 to 2017. Technically, the condition of stationarity in collected data is verified by using the ADF unit-root test. Empirically, the findings of the switching states estimation show that financial markets in ASEAN countries have been continuously growing since 2010. In other words, there are 1,358 times standing for bull periods and they are more than bear periods which are 322 times during total collected 1,680 days. The second crucial process is the portfolio optimization. The empirical results indicate that the most efficient choice to minimize the risk values in portfolios following optimal solutions is to focus on long-term investments rather than speculative investments. For bull periods, the stock exchange regarding real estate (LogProb) in Singapore potentially provides the best opportunity to invest. Conversely, the banking stock index (Maybank) in Indonesia is the stock that provides the most safety choice and lowest risk value when the ASEAN stock markets are in bear periods.

The Extreme Value Forecasting in Dynamics Situations for Reducing of Economic Crisis: Cases from Thailand, Malaysia, and Singapore

This chapter was successfully proposed to clarify the complicated issue which is the dynamic prediction in the extreme events in economic cycles and computationally estimated its impacts on economic systems in ASEAN-3 countries such as Thailand, Malaysia, and Singapore by employing econometric tools, including the Markov-Switching Bayesian Vector Autoregressive model (MSBVAR), Bayesian Non-Stationary Extreme Value Analysis (NEVA), and Bayesian Dynamic Stochastic General Equilibrium approach (BDSGE). Technically, the yearly time-series variables such as Thailand’s gross domestic products, Malaysia’s gross domestic products, and Singapore’s gross domestic products were observed during 1961–2016. Empirically, the results showed the economic trends in the countries containing fluctuated movements relied on the real business cycle concept (RBC model). Additionally, these trends had unusual points called “extreme events” which should be mentioned as an economic alarming signal. Furthermore, the speedy economic adjustments estimated by BDSGE indicated that the extreme fluctuated rates of GDP in ASEAN-3 countries can be the harmful factor to face capital bubble crises, chronic unemployment, and even overpricing indexes. Accordingly, practical policies and private collaboration regarding economic alarming announcements in advance should be intensively considered.

UNDERSTANDING THE RELATIONSHIP OF TOURISM DEMANDS CONNECTING WITH ECONOMY AND TOURISM STOCK INDEX IN AN EXTREME CASE OF THAILAND BIVARIATE EXTREME VALUE COPULA APPROACH

This paper analyses part of the viability of green supply chain management practices created for fisheries industry to implement sustainability control system adoption as mediating on reverse logistics innovation and customer environmental collaboration towards sustainability performance. It examines reverse logistics innovation and customer environmental collaboration adopted in the supply chain as a result of pressures from primary stakeholders. The resulting hypotheses are tested using fishery industries in Indonesia data of 262 samples utilizing primary and secondary data. Finding reveal, a phenomenon with sustainability control system adoption have significant effect to enhancing sustainability performance. Moreover, our results yield insights to green practices in optimizing their supply chain and sustainability performance.

Forecasting of VaR in Extreme Event Under Economic Cycle Phenomena for the ASEAN-4 Stock Exchange

This paper was proposed to computationally investigate the cycling details and risk management of the ASEAN-4 financial stock indexes, including Bangkok Bank (BBL), Development Bank of Singapore Limited (DBS), Commerce International Merchant Bankers (CIMB), and Bank Mandiri (Mandiri). These daily time-series data were observed during 2012 to 2017. Technically, this paper employed the econometric tool called Markov Switching Model (MS-model), the extreme value application called Generalized Pareto Distribution (GPD-model), and the risk management method called Value at Risk (VaR) to provide the estimated solutions and recommendations for investing in these financial stocks. Empirically, the switching regime estimation resulted that these four financial indexes obviously contain real business cycling movements, which were described as bull and bear regimes. Additionally, the results estimated by the GPD model confirmed that there were extreme events inside the trends of the four stock indexes. Ultimately, the outcomes calculated by the risk measurement for extreme cases, which were economic crises, stated that there was an enormously high risk to considerably invest only in short earnings within these four financial stock indexes. Consequently, long-run investment should be mentioned.

Macro-Econometric Forecasting for During Periods of Economic Cycle Using Bayesian Extreme Value Optimization Algorithm

This paper aims to computationally analyze the extreme events which can be described as crises or unusual times-series trends among the macroeconomic variables. These data are statistically estimated by employing the optimally extreme point for supporting policy makers to specify the economic expansion target and economic warning level. The Nonstationary Extreme Value Analysis (NEVA) applying Bayesian inference and Newton-optimal method are employed to complete the researchs solutions and estimate the time-series variables such as GDP, CPI, FDI, and unemployment rate collected during 1980 to 2015. The results show there are extreme values in the trend of macroeconomic factors in Thailand economic system. This extreme estimation is presented as an interval. In addition, the empirical results from the optimization approach state that the exactly extreme points can be computationally found. Ultimately, it is clear that the computationally statistical approach, especially Bayesian statistics, is inevitably important for econometric researches in the recent era.

An Analysis of ICT Sectors and Economic Growth: Evidence from ASEAN Countries

This paper proposes to investigate the causal relationship between ICT segments and economic expansionary rates in ASEAN countries. Methodologically, the panel time-series data observed during 2006 to 2015 is employed to estimate the panel Granger causality test. According to the technical problem of lag selection for the panel causality analysis, the computationally statistical approach called Newton’s optimization method is originally applied to verify the suitable lag selection. The empirical results found that ICTs are not the major factor that causally motivates economic growth in ASEAN. To address the issue, equitable educational systems and advanced infrastructural developments are the primary policies that should be corporately implemented.

Economic and Business Cycle of India: Evidence from ICT Sector

This paper aims to study the relationship between Indian ICT industries and GDP by applying Bayesian inference. Five yearly predominant indexes collected during 2000–2015, including Indian GDP, fixed phone usages, mobile phone distributions, Internet servers, and broadband suppliers, are analyzed by employing the Markov-switching model (MS model) and Bayesian vector autoregressive (BVAR) models. In addition, the Bayesian regression model is used to investigate the ICT multiplier related to Indian economic growth. The empirical results indicate that IT sectors are becoming the major role of Indian economic expansion in the forthcoming future, compared with telecommunication sectors. Moreover, the result of the ICT multiplier confirms that high technological industrial zones should be systematically enhanced continuously, in particular, research and development in cyberspace.