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

Sriboonchitta, Songsak; Nguyen, Hung T.; Wiboonpongse, Aree; Liu, Jianxu

doi:10.1016/j.ijar.2013.01.004

Cited by 60 publications

(26 citation statements)

References 22 publications

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“…The copula families used in this study are summarized in Appendix A (see [21,31,32]). Copula families can be selected based on their ability to capture positive and negative dependence, as well as tail dependence.…”

Section: Copulamentioning

confidence: 99%

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Modeling dependence between error components of the stochastic frontier model using copula: Application to intercrop coffee production in Northern Thailand

Wiboonpongse

Liu

Sriboonchitta

et al. 2015

International Journal of Approximate Reasoning

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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.

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Section: Copulamentioning

confidence: 99%

“…The bivariate Gumbel copula is given by C Gum (u 1 , u 2 |θ) = exp − (− ln u 1 ) 1/θ + (− ln u 2 ) 1/θ θ , (31) where θ ∈ (1, +∞). It is an asymmetric Archimedean copula that allows for strong upper tail dependence.…”

Section: A5 Gumbel Copulamentioning

confidence: 99%

Modeling dependence between error components of the stochastic frontier model using copula: Application to intercrop coffee production in Northern Thailand

Wiboonpongse

Liu

Sriboonchitta

et al. 2015

International Journal of Approximate Reasoning

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“…Sriboonchitta et al [17] applied the copula based GARCH for modeling the volatility and dependency of the agricultural price and production indices of Thailand. Based on the study, the work mentioned that this approach provided more flexibility for finding out the joint distributions and the transformation of the invariant correlation, without the assumption of linear correlation.…”

Section: Methodsmentioning

confidence: 99%

Dependence Structure between Crude Oil, Soybeans, and Palm Oil in ASEAN Region: Energy and Food Security Context

Kiatmanaroch

Sriboonchitta

2014

Modeling Dependence in Econometrics

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Abstract. The increase in energy and food prices remains a challenge for the ASEAN Economic Community (AEC). Understanding the dependence between energy prices and food prices is imperative for the energy and food security for the people of the ASEAN countries. The C-vine copula model is a flexible tool to analyze the relationship between variables, in which the multivariate dependence modeling. It offers us to define the relationship structure between variables or we call pair-copula construction, according to the purpose of study. This study is interesting to examine an influence of crude oil price on palm oil price and soybeans price. The results can conclude that the change of crude oil price has influence on the prices of palm oil and soybeans. Moreover, the findings show that there exists the dependence between palm oil price and soybeans price, and crude oil price is one factor that has influence on relation of their prices. However, the dependence structure of the static copula for Crude oil-Palm oil (C,P), Crude oil-Soybeans (C,S), Palm oil-Soybeans (P,S), there exists a weak positive dependence in each pair-copula. This indicates that the price of each commodity is slightly related to the price of every other. In the case of soybeans, the ASEAN members should cooperate and incorporate their efforts to increase the capacity and performance in production to reduce relying on soybeans being imported from outside the region.

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“…According to the sequence of occurrence time, the quantitative prediction methods can be divided into econometric method, time series analysis method, and intelligent prediction method. The econometric method finds the supporting economic theory according to the research problem, then puts forward the hypothesis, and establishes the econometric model to verify the hypothesis [4][5][6][7][8][9]. However, most empirical studies do not prove that the prediction effect of the classic econometric model is better than that of the time series analysis method [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

A Novel Agricultural Commodity Price Forecasting Model Based on Fuzzy Information Granulation and MEA-SVM Model

Zhang

2018

Mathematical Problems in Engineering

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Accurately predicting the price of agricultural commodity is very important for evading market risk, increasing agricultural income, and accomplishing government macroeconomic regulation. With the price index predictions of 6 commodities of Food and Agriculture Organization of the United Nations (FAO) as examples, this paper proposed a novel agricultural commodity price forecasting model which combined the fuzzy information granulation, mind evolutionary algorithm (MEA), and support vector machine (SVM). Firstly, the time series data of agricultural commodity price index was transformed into fuzzy information granulation particles made up ofLow,R, andUp, which represented the trend and magnitude of price movement. Secondly, MEA algorithm was employed to seek the optimal parameterscandgfor SVM to establish the MEA-SVM model. Finally, FOA price index fluctuation range and change trend in the future were predicted by the MEA-SVM model. The empirical analysis showed that the MEA-SVM model was effective and had higher prediction accuracy and faster calculation speed in the forecasting of agricultural commodity price.

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

Cited by 60 publications

References 22 publications

Modeling dependence between error components of the stochastic frontier model using copula: Application to intercrop coffee production in Northern Thailand

Modeling dependence between error components of the stochastic frontier model using copula: Application to intercrop coffee production in Northern Thailand

Dependence Structure between Crude Oil, Soybeans, and Palm Oil in ASEAN Region: Energy and Food Security Context

A Novel Agricultural Commodity Price Forecasting Model Based on Fuzzy Information Granulation and MEA-SVM Model

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