Jianxu Liu

Modeling volatility and dependency of agricultural price and production indices of Thailand: Static versus time-varying copulas

Abstract Volatility and dependence structure are two main sources of uncertainty in many economic issues, such as exchange rates, future prices and agricultural product prices etc. who fully embody uncertainty among relationship and variation. This paper aims at estimating the dependency between the percentage changes of the agricultural price and agricultural production indices of Thailand and also their conditional volatilities using copula-based GARCH models. The motivation of this paper is twofold. First, the strategic department of agriculture of Thailand would like to have reliable empirical models for the dependency and volatilities for use in policy strategy. Second, this paper provides less restrictive models for dependency and the conditional volatility GARCH. The copula-based multivariate analysis used in this paper nested the traditional multivariate as a special case (Tae-Hwy and Xiangdong, 2009) [13] . Appropriate marginal distributions for both, the percentage changes of the agricultural price and agricultural production indices were selected for their estimation. Static as well as time varying copulas were estimated. The empirical results were found that the suitable margins were skew t distribution and the time varying copula i.e., the time varying rotate Joe copula (270°) was the choice for the policy makers to follow. The one-period ahead forecasted-growth rate of agricultural price index conditional on growth rate of agricultural production index was also provided as an example of forecasting it using the resulted margins and time-varying copula based GARCH model.

Analysis of Volatility and Dependence between the Tourist Arrivals from China to Thailand and Singapore: A Copula-Based GARCH Approach

This paper aims to estimate the dependency between the growth rates of tourist arrivals of Thailand and Singapore from China, and also analyze their conditional volatilities. Firstly, we assume that both margins are skewed-t distribution, and then make use of ARMA-GARCH model to fit monthly time series data. Secondly, fifteen types of static copulas are used to fit static dependence between tourist arrivals to Thailand and Singapore from China. We take the AIC, BIC and the two tests based on Kendall’s transform as criterions for goodness of fit test. Moreover, we apply time-varying copulas that described the dynamic Kendall’s tau process. Results show that each growth rate of tourist arrivals has a long-run persistence of volatility, and the time-varying Gaussian copula has the highest explanatory power of all the dependence structures between tourist arrivals to Thailand and Singapore from China in terms of AIC and BIC values.

Modeling dependence between error components of the stochastic frontier model using copula

In the standard stochastic frontier model, the two-sided error term V and the one-sided technical inefficiency error term W are assumed to be independent. In this paper, we relax this assumption by modeling the dependence between V and W using copulas. Nine copula families are considered and their parameters are estimated using maximum simulated likelihood. The best model is then selected using the AIC or BIC criteria. This methodology was applied to coffee production data from Northern Thailand. For these data, the best model was the one based on the Clayton copula. The main finding of this study is that the dependence between V and W is significant and cannot be ignored. In particular, the standard stochastic frontier model with independence assumption grossly overestimated the technical efficiency of coffee production. These results call for a reappraisal of previous production efficiency studies using the SFM with independence assumption, which may occasionally lead to overoptimistic conclusions. A copula-based stochastic frontier model is investigated.The use of copula allows us to capture dependency between the two error components.The method was applied to coffee production in Northern Thailand.The conventional stochastic frontier model severely overestimates the technical efficiencies.

Studying Volatility and Dependency of Chinese Outbound Tourism Demand in Singapore, Malaysia, and Thailand: A Vine Copula Approach

This paper investigates the volatility and dependence of Chinese tourism demand for Singapore, Malaysia, and Thailand (SMT) destinations, using the vine copula based auto regression moving average-generalized autoregressive conditional heteroskedasticity (ARMA-GARCH) model. It is found that a jolt to the tourist flow can have long-standing ramifications for the SMT countries. The estimation of the vine copulas among SMT show that the Survival Gumbel, Frank, and Gaussian copulas are the best copulas for Canonical vine (C-vine) or Drawable vine (D-vine) among the possible pair-copulas. In addition, this paper illustrates the making of time-varying Frank copulas for vine copulas. Finally, there is a discussion on tourism policy planning for better managing the tourism demand for the SMT countries. We suggest tour operators and national tourism promotion authorities of SMT collaborate closely in the marketing and promotion of joint tourism products.

A double-copula stochastic frontier model with dependent error components and correction for sample selection

In the standard stochastic frontier model with sample selection, the two components of the error term are assumed to be independent, and the joint distribution of the unobservable in the selection equation and the symmetric error term in the stochastic frontier equation is assumed to be bivariate normal. In this paper, we relax these assumptions by using two copula functions to model the dependences between the symmetric and inefficiency terms on the one hand, and between the errors in the sample selection and stochastic frontier equation on the other hand. Several families of copula functions are investigated, and the best model is selected using the Akaike Information Criterion (AIC). The methodology was applied to a sample of 200 rice farmers from Northern Thailand. The main findings are that (1) the double-copula stochastic frontier model outperforms the standard model in terms of AIC, and (2) the standard model underestimates the technical efficiency scores, potentially resulting in wrong conclusions and recommendations. We propose a stochastic frontier model with self-selection, based on two copulas.The model is estimated using maximum simulated likelihood.Several copula families are considered; the best model is selected using AIC.The model was applied to cross-sectional rice production data.Our model provides more reliable estimates of technical efficiency scores.

A Vine Copula Approach for Analyzing Financial Risk and Co-movement of the Indonesian, Philippine and Thailand Stock Markets

This paper aims at analyzing the financial risk and co-movement of stock markets in three countries: Indonesia, Philippine and Thailand. It consists of analyzing the conditional volatility and test the leverage effect in the stock markets of the three countries. To capture the pairwise and conditional dependence between the variables, we use the method of vine copulas. In addition, we illustrate the computations of the value at risk and the expected shortfall using Monte Carlo simulation with copula based GJR-GARCH model. The empirical evidence shows that all the leverage effects add much to the capacity for explanation of the three stock returns, and that the D-vine structure is more appropriate than the C-vine one for describing the dependence of the three stock markets. In addition, the value at risk and ES provide the evidence to confirm that the portfolio may avoid risk in significant measure.

Vine Copula-Cross Entropy Evaluation of Dependence Structure and Financial Risk in Agricultural Commodity Index Returns

Many studies used the empirical Kendall’s tau to select a preferable ordering of vine copulas or to fix such a sequence. In this study, for high dimension vine copulas, we propose the vine copula based cross entropy method to figure out a more appropriate ordering of the vine copula. The goal of this study is to estimate the non-conditional, conditional, and tail dependences for agricultural price index returns by using the C-vine and D-vine copula based cross entropy model. In addition, we show that a framework uses the Monte Carlo simulation and the results of vine copula to estimate the expected shortfall (ES) of an equally weighted portfolio. The optimal portfolio allocations can also be estimated using global optimization with the differential evolution algorithm.

Vine Copulas as a Way to Describe and Analyze Multi-Variate Dependence in Econometrics: Computational Motivation and Comparison with Bayesian Networks and Fuzzy Approaches

In the last decade, vine copulas emerged as a new efficient techniques for describing and analyzing multi-variate dependence in econometrics; see, e.g., [1, 2, 3, 7, 9, 10, 11, 13, 14, 21]. Our experience has shown, however, that while these techniques have been successfully applied to many practical problems of econometrics, there is still a lot of confusion and misunderstanding related to vine copulas. In this paper, we provide a motivation for this new technique from the computational viewpoint. We show that other techniques used to described dependence - Bayesian networks and fuzzy techniques - can be viewed as a particular case of vine copulas.

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.

Volatility and Dependence for Systemic Risk Measurement of the International Financial System

In the context of existing downside correlations, we proposed multi-dimensional elliptical and asymmetric copula with CES models to measure the dependence of G7 stock market returns and forecast their systemic risk. Our analysis firstly used several GARCH families with asymmetric distribution to fit G7 stock returns, and selected the best to our marginal distributions in terms of AIC and BIC. Second, the multivariate copulas were used to measure dependence structures of G7 stock returns. Last, the best modeling copula with CES was used to examine systemic risk of G7 stock markets. By comparison, we find the mixed C-vine copula has the best performance among all multivariate copulas. Moreover, the pre-crisis period features lower levels of risk contribution, while risk contribution increases gradually while the crisis unfolds, and the contribution of each stock market to the aggregate financial risk is not invariant.