Roengchai Tansuchat

Analyzing and forecasting volatility spillovers, asymmetries and hedging in major oil markets

Crude oil price volatility has been analyzed extensively for organized spot, forward and futures markets for well over a decade, and is crucial for forecasting volatility and Value-at-Risk (VaR). There are four major benchmarks in the international oil market, namely West Texas Intermediate (USA), Brent (North Sea), Dubai/Oman (Middle East), and Tapis (Asia-Pacific), which are likely to be highly correlated. This paper analyses the volatility spillover and asymmetric effects across and within the four markets, using three multivariate GARCH models, namely the constant conditional correlation (CCC), vector ARMA-GARCH (VARMA-GARCH) and vector ARMA-asymmetric GARCH (VARMA-AGARCH) models. A rolling window approach is used to forecast the 1-day ahead conditional correlations. The paper presents evidence of volatility spillovers and asymmetric effects on the conditional variances for most pairs of series. In addition, the forecast conditional correlations between pairs of crude oil returns have both positive and negative trends. Moreover, the optimal hedge ratios and optimal portfolio weights of crude oil across different assets and market portfolios are evaluated in order to provide important policy implications for risk management in crude oil markets.

Modelling Long Memory Volatility in Agricultural Commodity Futures Returns

This paper estimates a long memory volatility model for 16 agricultural commodity futures returns from different futures markets, namely corn, oats, soybeans, soybean meal, soybean oil, wheat, live cattle, cattle feeder, pork, cocoa, coffee, cotton, orange juice, Kansas City wheat, rubber, and palm oil. The class of fractional GARCH models, namely the FIGARCH model of Baillie et al. (1996), FIEGARCH model of Bollerslev and Mikkelsen (1996), and FIAPARCH model of Tse (1998), are modelled and compared with the GARCH model of Bollerslev (1986), EGARCH model of Nelson (1991), and APARCH model of Ding et al. (1993). The estimated d parameters, indicating long-term dependence, suggest that fractional integration is found in most of agricultural commodity futures returns series. In addition, the FIGARCH (1,d,1) and FIEGARCH(1,d,1) models are found to outperform their GARCH(1,1) and EGARCH(1,1) counterparts.

Interdependence of international tourism demand and volatility in leading ASEAN destinations.

International and domestic tourism are leading economic activities in todays world. Tourism has been known to generate goods and services directly and indirectly, attract foreign currency, stimulate employment and provide opportunities for investment. It has also been recognized as an important means of achieving economic development. Substantial research has been conducted to evaluate the role of international tourism, and its associated volatility, within and across various economies. This paper applies several recently developed models of multivariate conditional volatility to investigate the interdependence of international tourism demand, as measured by international tourist arrivals, and its associated volatility in the four leading destinations in ASEAN, namely Indonesia, Malaysia, Singapore and Thailand. Each of these countries has attractive tourism characteristics, such as significant cultural and natural resources. Shocks to international tourism demand volatility could affect, positively or negatively, the volatility in the tourism demand of neighbouring countries. The empirical results should encourage regional cooperation in tourism development among ASEAN member countries and also mobilize international and regional organizations to provide appropriate policy actions.

Modelling conditional correlations in the volatility of Asian rubber spot and futures returns

Asia is presently the most important market for the production and consumption of natural rubber. World prices of rubber are subject to not only to changes in demand, but also speculation regarding future markets. Japan and Singapore are the major future markets for rubber, while Thailand is one of the worlds largest producers of rubber. As rubber prices are influenced by external markets, it is important to analyse the relationship between the relevant markets in Thailand, Japan and Singapore. The analysis is conducted using several alternative multivariate GARCH models. The empirical results indicate that the constant conditional correlations arising from the CCC model lie in the low to medium range. The results from the VARMA-GARCH model and the VARMA-AGARCH model suggest the presence of volatility spillovers and asymmetric effects of positive and negative return shocks on conditional volatility. Finally, the DCC model suggests that the conditional correlations can vary dramatically over time. In general, the dynamic conditional correlations in rubber spot and futures returns shocks can be independent or interdependent.

Modelling Long Memory Volatility In Agricultural Commodity Futures Returns

This paper estimates the long memory volatility model for 16 agricultural commodity futures returns from different futures markets, namely corn, oats, soybeans, soybean meal, soybean oil, wheat, live cattle, cattle feeder, pork, cocoa, coffee, cotton, orange juice, Kansas City wheat, rubber, and palm oil. The class of fractional GARCH models, namely the FIGARCH model of Baillie et al. (1996), FIEGACH model of Bollerslev and Mikkelsen (1996), and FIAPARCH model of Tse (1998), are modelled and compared with the GARCH model of Bollerslev (1986), EGARCH model of Nelson (1991), and APARCH model of Ding et al. (1993). The estimated d parameters, indicating long-term dependence, suggest that fractional integration is found in most of agricultural commodity futures returns series. In addition, the FIGARCH (1,d,1) and FIEGARCH(1,d,1) models are found to outperform their GARCH(1,1) and EGARCH(1,1) counterparts.

A Panel Threshold Model of Tourism Specialization and Economic Development

The significant impact of international tourism in stimulating economic growth is especially important from a policy perspective. For this reason, the relationship between international tourism and economic growth would seem to be an interesting empirical issue. In particular, if there is a causal link between international tourism demand and economic growth, then appropriate policy implications may be developed. The purpose of this paper is to investigate whether tourism specialization is important for economic development in East Asia and the Pacific, Europe and Central Asia, Latin America and the Caribbean, the Middle East and North Africa, North America, South Asia, and Sub-Saharan Africa, over the period 1991- 2008. The impact of the degree of tourism specialization, which is incorporated as a threshold variable, on economic growth is examined for a wide range of countries at different stages of economic development. The empirical results from threshold estimation identify two endogenous cut-off points, namely 14.97% and 17.50%. This indicates that the entire sample should be divided into three regimes. The results from panel threshold regression show that there exists a positive and significant relationship between economic growth and tourism in two regimes, the regime with the degree of tourism specialization lower than 14.97% (regime 1) and the regime with the degree of tourism specialization between 14.97% and 17.50% (regime 2). However, the magnitudes of the impact of tourism on economic growth in those two regimes are not the same, with the higher impact being found in regime 2. An insignificant relationship between economic growth and tourism is found in regime 3, in which the degree of tourism specialization is greater than 17.50%. The empirical results suggest that tourism growth does not always lead to economic growth.

Interdependence of international tourism demand and volatility in leading ASEAN destinations

International and domestic tourism are leading economic activities in the world today. Tourism has been known to generate goods and services directly and indirectly, attract foreign currency, stimulate employment, and provide opportunities for investment. It has also been recognized as an important means for achieving economic development. Substantial research has been conducted to evaluate the role of international tourism, and its associated volatility, within and across various economies. This paper applies several recently developed models of multivariate conditional volatility to investigate the interdependence of international tourism demand, as measured by international tourist arrivals, and its associated volatility in the four leading destinations in ASEAN, namely Indonesia, Malaysia, Singapore and Thailand. Each of these countries has attractive tourism characteristics, such as significant cultural and natural resources. Shocks to international tourism demand volatility could affect, positively or negatively, the volatility in tourism demand of neighbouring countries. The empirical results should encourage regional co-operation in tourism development among ASEAN member countries, and also mobilize international and regional organizations to provide appropriate policy actions.

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.

Investigating Dynamic Correlation in the International Implied Volatility Indexes

This paper investigates dynamic interaction among international volatility indexes, consisting of VIX, VSTOXX, VDAX, VFTSE, VNVIXN, VHSI and VKOSPI. This paper also extends the multivariate normal distribution and multivariate student-t distribution based dynamic conditional correlation (DCC) model to a multivariate skew distribution. We then apply this extended model to estimate the dynamic volatility and correlation in international volatility indexes. The empirical results of model comparison reveal the multivariate skewed student-t distribution based CGARCH-DCC model to perform the best in our real data analysis. This indicates that the time-varying conditional correlation coefficients as well as volatility are skewed and fat tailed or leptokurtic in characteristic.

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.