Songsak Sriboonchitta

Forecasting GDP Growth in Thailand with Different Leading Indicators Using MIDAS Regression Models

In this study, we compare the performance between three leading indicators, namely, export, unemployment rate, and SET index in forecasting QGDP growth in Thailand using the mixed-frequency data sampling (MIDAS) approach. The MIDAS approach allows us to use monthly information of leading indicators to forecast QGDP growth without transforming them into quarterly frequency. The basic MIDAS model and the U-MIDAS model are considered. Our findings show that unemployment rate is the best leading indicator for forecasting QGDP growth for both MIDAS settings. In addition, we investigate the forecast performance between the basic MIDAS model and the U-MIDAS model. The results suggest that the U-MIDAS model can outperform the basic MIDAS model regardless of leading indicators considered in this study.

Estimating Efficiency of Stock Return with Interval Data

Existing studies on capital asset pricing model (CAPM) have basically focused on point data which may not concern about the variability and uncertainty in the data. Hence, this paper suggests the approach that gains more efficiency, that is, the interval data in CAPM analysis. The interval data is applied to the copula-based stochastic frontier model to obtain the return efficiency. This approach has proved its efficiency through application in three stock prices: Apple, Facebook and Google.

Analysis of Transmission and Co-Movement of Rice Export Prices Between Thailand and Vietnam

Copulas have become one of the most significant new tools to measure nonlinear dependence structure and tail dependence. Combining time-varying copulas and VAR model with kernel density function, this paper proposes a new method, called the time-varying copula-based VAR model, to analyze the transmission and co-movement of rice export prices between Thailand and Vietnam. The time-varying BB1 and BB7 copulas are proposed to measure asymmetric tail dependences. The main findings of this study reveal that there exists obvious co-movement between rice export prices of Thailand and Vietnam, and the time-varying BB7 copula has a better performance than others. In addition, the price transmission between the two markets is bi-directional, and the Vietnamese price is more suitable as price leader in terms of the results of impulse response functions.

Forecasting Asian Credit Default Swap Spreads: A Comparison of Multi-regime Models

This paper aims to explore the best forecasting model for predicting the Credit Default Swap (CDS) index spreads in emerging markets Asia by comparing the forecasting performance between the multi-regime models. We apply threshold, Markov switching, Markov switching GARCH and simple least squares for structural and autoregressive modeling. Both in- and out-of-sample forecasts are conducted to compare the forecasting performance between models. The results suggest that Markov switching GARCH(1,1) structural model presents the best performance in predicting Asian Credit Default Swap (CDS) index spreads. We also check the preciseness of our selected model by employing the robustness test.

The Role of Asian Credit Default Swap Index in Portfolio Risk Management

This paper aims at evaluating the performance of Asian Credit Default Swap (CDS) index in risk measurement and portfolio optimization by using several multivariate copulas-GARCH models with Expected Shortfall and Sharpe ratio. Multivariate copula-GARCH models consider the volatility and dependence structures of financial assets so that they are conductive to accurately predict risk and optimal portfolio. We find that vine copulas have better performance than other multivariate copulas in model estimation, while the multivariate T copulas have better performance than other kinds of copulas in risk measurement and portfolio optimization. Therefore, the model estimation, risk measurement, and portfolio optimization in empirical study should use different copula models. More importantly, the empirical results give evidences that Asian CDS index can reduce risk.

Testing the Validity of Economic Growth Theories Using Copula-Based Seemingly Unrelated Quantile Kink Regression

The distinct points of view about factors driving economic growth are introduced all the time in which some effectively useful suggestions then become the growth theories, which in turn lead to various researches on economic growth. This paper aims to examine the joint validity of the growth theories using our introduced model named copula based seemingly unrelated quantile kink regression as a key tool in this work. We concentrate exclusively on the experience of Thailand and found that the growth models can prove their validities for the Thai economy through this experiment.

Estimating and Predicting Financial Series by Entropy-Based Inferential Model

In this study, the non-parametric Inferential Model or IM with the entropy-based random set has been proposed for the investigation of financial data in the two statistical domains i.e. estimation and prediction. The samples from five financial markets were chosen for representing the different types of financial assets to make a conclusion about this new framework. We found that the Inferential Model performed equally well compared with the traditional method but was more robust so that it might be more appropriate for some specific uses.

Adjusting Beliefs via Transformed Fuzzy Prices

In the situation that the gut feeling tells otherwise, incorporating information from expert opinions can significantly improve the accuracy of standard estimation and prediction methods, which rely only on observed data. To cope with this problem, we propose the fusion of data under the Bayesian framework by transforming price estimates into initial beliefs of assets. The proposed methodology focuses on modeling the price expectation by linguistic terms and mathematically extending them to other parameters like the standard deviation. On five sample assets from different markets, our method was experimented and compared with the method of the ARMA-GARCH beyond the points of structural change. The problems are multi-dimensional but conveniently solved by the Metropolis-Hastings algorithm. The results show the significant impacts of expert opinions on the posterior.

The Future of Global Rice Consumption: Evidence from Dynamic Panel Data Approach

This study investigates the future outlook of global rice consumption using dynamic panel data regression (DPD) with penalised fixed effect model. The three main factors affecting rice consumption include previous rice demand, GDP per capita, and world rice price. The data set covers 73 countries that is almost 80% of world rice consumption from 1960 to 2015. We separate these countries into 4 groups based on income levels classified by the World Bank including low income, lower middle-income, upper middle-income, and high income. The results show that, at the global scale, rice consumption is expected to be slightly higher. Such demand is driven by rising demand from the upper middle- and high income countries, while it is offset by the lower demand from lower middle- and low income countries.

The Impact of Extreme Events on Portfolio in Financial Risk Management

We use the concept of copula and extreme value theory to evaluate the impact of extreme events such as flooding, nuclear disaster, etc. on the industry index portfolio. A t copulas based on GARCH model is applied to explain a portfolio risk management with high-dimensional asset allocation. Finally, we calculate the condition Value-at-Risk (CVaR) with the hypothesis of t joint distribution to construct the potential frontier of the portfolio during the times of crisis.