Research on Stylized Facts and Risk Models for Chinese Agricultural Futures Market

TL;DR: Li et al. as mentioned in this paper built different volatility models (GARCH, APARCH, FIGARCH, FIAPARCH) for the conditional volatility of returns for Chinese agricultural futures indexes, and choose the skewed Student-t distribution, which is more appropriate to depict the typical features of financial assets returns.

Abstract: Agricultural futures market is an important part of modern financial market systems.Almost every government and industry highly values its functions of guiding,hedging and stabilizing markets.Therefore,the market has been growing.The rapid development of China market is the driving force of economic growth. However,little work has been done to detect volatility features and risk characteristics of China market.The main objective of this paper are to(1) build different volatility models(GARCH;APARCH;FIGARCH;FIAPARCH) for the conditional volatility of returns for Chinese agricultural futures indexes,(2) choose the skewed Student-t distribution,which is more appropriate to depict the typical features of financial assets returns,and(3) fully investigate and describe distribution features of Chinese agricultural futures returns. We compute agricultural futures’ VaR(Value at Risk) in different models and adopt both unconditional coverage testing and conditional coverage testing.The application scope and different VaR models are constructed to offer the best VaR measurement for Chinese agricultural futures market.Because of the asymmetrical characteristics of financial asset return distributions,derivatives with the same underlying asset have different VaRs when taking a long-term or short-term position.Therefore,it is meaningful to investigate the right tail and left tail separately in asymmetric return distribution.In this paper,we will test different models in both long and short positions to elaborate their effectiveness and practicability in Chinese agricultural futures market. Our main findings are summarized as follows: (1) The return of Chinese agricultural futures market exhibits relative significant Leptokurtic,fat tailed and skewed distributions,and volatility clustering effect.However,there is no evidence that the volatility of agricultural futures market presents leverage effect like Chinese stock market;(2) Using skewed Student-t distribution can help improve the accuracy of VaR and ES estimation in Chinese agricultural futures market.However,adding the leverage effect and long-memory in volatility models does not benefit the accuracy of VaR and ES estimation;(3) When considering both efficiency of model estimations and accuracy of calculations,we think GARCH-SST model is an excellent choice for risk measurement in Chinese agricultural futures market. This paper has important practical and social implications.We make the case that some statistical characteristics are the stylized facts and GARCH-SST model is a reasonable choice to forecast VaRs of Chinese agricultural futures market.These techniques and empirical results can offer useful theoretic reference and practical approaches for risk measurement in Chinese agricultural futures market.Furthermore,the findings of stylized statistical characteristics of Chinese agricultural futures market have important regulatory sense in position limits and margin ratio,et al.