Paravee Maneejuk

Seemingly Unrelated Regression Based Copula: An Application on Thai Rice Market

This paper introduced the seemingly unrelated regression (SUR) model based on Copula to improve a linear regression system since the conventional SUR model has a strong assumption of normally distributed residuals. The Copula density functions were incorporated into the likelihood to relax the restriction of the marginal distribution. The real dataset of Thai rice was used for an application comparing the conventional SUR model estimated by GLS and the Copula-based SUR model. The result indicated that the Copula-based SUR model performed slightly better than the conventional SUR. In addition, the estimated results showed that Gaussian Copula was the most appropriate function for being the linkage between the marginal distributions. Moreover, the marginal distributions also were tested, and the result showed that a normal distribution and student-t distribution were the best fit for the marginal distributions of demand and supply equations, respectively.

Welfare Measurement on Thai Rice Market: A Markov Switching Bayesian Seemingly Unrelated Regression

This paper aimed to measure the welfare of the Thai rice market and provided a new estimation in welfare measurement. We applied the Markov Switching approach to the Seemingly Unrelated Regression model and adopted the Bayesian approach as an estimator for our model. Thus, we have the MS-BSUR model as an innovative tool to measure the welfare. The results showed that the model performed very well in estimating the demand and supply equations of two different regimes; namely, high growth and low growth. The equations were extended to compute the total welfare. Then, the expected welfare during the studied period was determined. We found that a mortgage scheme may lead the market to gain a high level of welfare. Eventually, the forecasts of demand and supply were estimated for 10 months, and we found demand and supply would tend to increase in the next few months before dropping around March, 2015.

A Generalized Information Theoretical Approach to Non-linear Time Series Model

The limited data will bring about an underdetermined, or ill-posed problem for the observed data, or for regressions using small data set with limited data and the traditional estimation techniques are difficult to obtain the optimal solution. Thus the approach of Generalized Maximum Entropy (GME) is proposed in this study and applied it to estimate the kink regression model under the limited information situation. To the best of our knowledge, the estimation of kink regression model using GME has been not done yet. Hence, we extend the entropy linear regression to non-linear kink regression by modifying the objective and constraint functions under the context of GME. We use both Monte Carlo simulation and real data study to evaluate the performance of our estimation from Kink regression and found that GME estimator performs slightly better compared to the traditional Least squares and Maximum likelihood estimators.

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.

Economic Growth and Income Inequality: Evidence from Thailand

Evidence from this study shows a significant relationship between growth and income inequality in which inequality creates a negative impact on the growth, but the impact of economic growth on income inequality is nonlinear. This result captures the Kuznets Hypothesis, finding the level of growth that can reduce unequal distribution of income in Thailand. Finally, this study also discovers a certain level of inflation that can help reduce income inequality effectively.

The Best Copula Modeling of Dependence Structure Among Gold, Oil Prices, and U.S. Currency

As internationally traded commodities typically depend on the value of US dollar, this paper especially focuses on the most traded commodities, gold and crude oil, and tries to examine the dependence structures between these variables and the US currency. We employ various types of copulas i.e. the multivariate copula, vine copula, and the Markov switching copula and examine for the best-fit copula functions to model the dependency. Evidence from this study shows that gold and oil prices follow an inverse relationship with the value of US dollar but the relationship between gold and oil itself is strongly positive. However, the pair copulas given condition by another variable results in some attractive correlations.

Price Transmission Mechanism in the Thai Rice Market

This study aimed to analyze price transmission in the Thai rice market using the MS-BVECM. We focused on the data set related to Thailands rice price, including Thai white rice price, Thai parboiled rice, Thai paddy price, and World rice price collected from M1/2004 to M3/2014. We estimated the model with two regimes; namely high market price regime and low market price regime. The estimated results showed that there existed some short-run relationships between these rice prices in both regimes. Unlike the long-run, there existed only one long-run relationship (one cointegrating equation) in the high market price regime expressed in the Thai white rice equation. Meanwhile, Thai paddy price has the long-run relationship and short-run adjustment dynamics in the low market price regime. In addition, we found that India’s non-basmati rice exports and the paddy price guaranteed at 15,000 THB per ton are two main reasons which caused the switching between these two regimes.

Modeling Dependence with Copulas: Are Real Estates and Tourism Associated?

Several families of copulas are considered in this study to illustrate the correlation between real estate–particularly hospitality real estate investment trust- and the tourism sector. In essence, this study uses Elliptical copulas and Archimedean copulas, and more recent classes, like extreme value copulas and mixed copulas to conduct the experiment. Under a specific data set, it is revealed that the classical classes of copulas i.e., Elliptical and Archimedean, are selected most often for illustrating the dependency, followed by the extreme value class, particularly the Husler-Reiss copula. However, surprisingly, the mixed copula is not entirely preferable for this data set.

Could Bitcoin enhance the portfolio performance

This study analyses the effect of adding bitcoin into the portfolio by exploiting the Long Only investment strategy. The Portfolio consists of five assets: bitcoin, crude oil price index, stock exchange of Thailand (SET) price index, the exchange rate between Thai and USD and Thai government bond compound with treasurer bill. The model used for modelling the return of all asset is Multivariate t-copula based on GARCH and also measure the risk of the portfolio using the Value-at-risk (VaR) under the condition of minimizing the variance of return. We find that when adding more bitcoin into the portfolio, the return and risk of asset increased. If we only invest in bitcoin, we will face the risk at 16.90% and gain 6.27%. When comparing the effectiveness of portfolio by using Return-risk ratio, it found that portfolio with bitcoin shows the higher return rate than portfolios without bitcoin. Therefore, it can conclude that bitcoin could indeed increase the effectiveness of portfolio.

Mixed-Copulas Approach in Examining the Relationship Between Oil Prices and ASEAN’s Stock Markets

This study aims to examine the relationship between oil prices and stock markets in five ASEAN countries: Thailand, Indonesia, Malaysia, Singapore, and the Philippines. Copula approach is used for modelling dependence structure between variables. In essence, this study considers four classes of copula, namely Archimedean copulas, Elliptical copulas, extreme value copulas, and mixed copulas, to examine the dependency between oil prices and stock market prices. We found that Thai, Malaysian, and Indonesian stock markets are likely to boom when crude oil prices increase while the Singaporean stock market as well as the Philippines’s stock market tend to move in the opposite direction to crude oil prices. However, the results show that these relationships are not strong.