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研究生:鍾朝偉
研究生(外文):Chao-Wei Chung
論文名稱:大豆、玉米不對稱波動之風險值探討
論文名稱(外文):Analytical Value-at-Risk in the Asymmetric Volatility between Corn and Soybean
指導教授:黃琮琪黃琮琪引用關係
指導教授(外文):Tsorng-Chyi Hwang
口試委員:吳榮杰陳淑恩葉春淵陳昇鴻
口試委員(外文):Rhung-Jieh WooShwu-En ChenChun-Yuan YeSheng-Hong Chen
口試日期:2015-07-26
學位類別:博士
校院名稱:國立中興大學
系所名稱:應用經濟學系所
學門:社會及行為科學學門
學類:經濟學類
論文種類:學術論文
論文出版年:2015
畢業學年度:103
語文別:英文
論文頁數:57
中文關鍵詞:風險值槓桿效果蒙地卡羅-馬可夫鍊法隨機波動-t 分配模型
外文關鍵詞:Value at Risk (VaR)Leverage effectMarkov Chain Monte Carlo Estimation Methods (MCMC)Stochastic Volatility with Student-t Errors (SV-t) Model
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Currently, Value at Risk (VaR) is one of the most important measures of risk. It is the percentile of the profit and loss distribution of a portfolio over a specified period. Through the rapid development in the world, we can negotiate portfolio risk and loss via the dynamic VaR method. In this paper, we propose a stochastic volatility with Student-t errors (SV-t) model that maximizes the expected returns subject to a VaR constraint to depict the risk of heterodasticity and leptokurtic accurately. We also propose the most efficient and best method, the Markov Chain Monte Carlo (MCMC) estimation method. Using spot price data from the Chicago Board of Trade (CBOT), we empirically find that there are more integrated and skewed data because a growing number of the multinational powerful world markets exchanges have emerged. In this paper, we also find a leverage effect, asymmetric heavy-tailed errors, and jump components exist in the returns of the corn and soybean markets. Corn in particular is empirically found to have a larger leverage effect than soybeans, indicating that the corn risk is greater than soybean risk to avoid increased damaged and asymmetric information Therefore, when shocks influence these markets, the corn and soybean markets are found to serially outperform each other in terms of the leverage effect, at least in the short term. Until now few papers have discussed these aspects and have found different derivative effects of fluctuations between corn and soybean commodity markets in agriculture development. To the best of my knowledge, no article has discussed volatility and risk by the SV-t model and applied it to the VaR in time series between corn and soybean agricultural commodity markets. This paper is less restrictive and more realistic, which could lead to a more asymmetric information response through agricultural economic activities. However, both corn and soybean price VaR values are more than 5%, indicating possible underestimates of returns from portfolio operations. Therefore, we might underestimate the market risk; we doubt the speculators’ surplus and tillage are increasing because of the price increase in keeping the price volatility of corn and soybean. It is suggested that more portfolio returns of soybean and corn futures market operations may be available

Table of Contents

List of Tables and Figures ii
Summary iii

Chapter 1 Introduction
1.1 Research Motivation and Purpose 1
1.2 Research Methods 5
1.3 Research Sources and Scope 7
Chapter 2 General Background Information
2.1 Structural Changes in Production and Prices of Corn and Soybean 9
2.2 Volatility Changes of Corn and Soybean 13
Chapter 3 Literature Review and Model Structure
3.1 Value- at- Risk (VaR) 16
3.2 Stochastic Volatility with Student-t errors (SV-t) model 23
3.3 Markov Chain Monte Carlo (MCMC) estimation method. 31

Chapter 4 Empirical Evidence
4.1 Results of SV-t between Corn and Soybean 39
4.2 Results of VaR between Corn and Soybean 47
Chapter 5 Conclusion 49
Reference 52



List of Tables
Table 1: MCMC estimation results of the SV-t model for simulated data in the corn market
Table 2: MCMC estimation results of the SV-t model for simulated data in the soybean market





List of Figures
Figure 1: GDP Growth rate trends in US & Euro Area (2006-2014)
Figure 2: Consumer confidence trends in US & Euro Area (2006-2014)
Figure 3: Corn and soybean price trends (Jan.2006-Sep.2014)
Figure 4: Corn and soybean volatility trends (Jan.2006-Sep.2014)
Figure 5: Corn and soybean VaR trends (Jan.2006-Sep.2014)


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