A Stochastic Factor Model for Risk Management of Commodity Derivatives

Guo, Zi-Yi

doi:10.20472/efc.2017.007.004

Cited by 8 publications

(3 citation statements)

References 13 publications

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“…In this article, we are interested in the Hong Kong stock market. Similar results have been obtained in other financial markets in Guo (2017a).…”

Section: Introductionsupporting

confidence: 90%

GARCH Models with Fat-Tailed Distributions and the Hong Kong Stock Market Returns

Guo¹

2017

IJBM

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As one of the world's largest securities markets, the Hong Kong stock market plays a significant role in facilitating the development of Chinese economy. In this paper, we investigate a suite of widely-used models, the GARCH models in risk management of the Hong Kong stock market returns. To account for conditional volatilities, we consider a new type of fat-tailed distribution, the normal reciprocal inverse Gaussian distribution (NRIG), and compare its empirical performance with two other popular types of fat-tailed distribution, the Student's t distribution and the normal inverse Gaussian distribution (NIG). We show that the NRIG distribution performs slightly better than the other two types of distribution. Also, our results indicate that it is important to introduce both GJR-terms and the NRIG distribution to improve the models' performance. Our results illustrate that the asymmetric GARCH NRIG model has practical advantages in quantitative risk management, and serves as a very useful tool for industry participants.

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“…In this article, we are interested in the Hong Kong stock market. Similar results have been obtained in other financial markets in Guo (2017a).…”

Section: Introductionsupporting

confidence: 90%

GARCH Models with Fat-Tailed Distributions and the Hong Kong Stock Market Returns

Guo¹

2017

IJBM

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“…First, it might be interesting to allow non-constant volatilities of shocks of n  in our setting and take account of some stylized facts commonly observed in finance data, such as volatility clustering and fat tails as in Guo (2017aGuo ( , 2017b. Second, to simply our simulation we fixed parameter  and obviously this simplification could be relaxed.…”

Section: Resultsmentioning

confidence: 99%

Dynamic Stochastic Factors, Risk Management and the Energy Futures

Guo¹,

Luo²

2017

IBR

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The world crude oil prices have dropped dramatically, and consequently the oil market has become very volatile and risky in the last several years. Since energy markets play very important roles in the international economy and have led several global economic crises, risk management of energy products prices becomes very important for both academicians and market participants. Schwartz and Smith's model (2000) is applied to calculate risk measures of Brent oil futures contracts and light sweet crude oil (WTI) futures contracts. The model includes a long-term factor and a short-term factor. We show that the two factors explain the Samuelson effect well and the model present well goodness of fit. Our back testing results demonstrate that the models provide satisfactory risk measures for listed crude oil futures contracts. JEL classifications: C58; G13; G32

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“…In this paper, we follow the model framework in Guo (2017a) and compare the empirical performance of the Student's t distribution and the NRIG distribution. The remaining sections www.hrmars.com of the paper are organized as follows.…”

Section: Literature Reviewmentioning

confidence: 99%

Conditional Heavy Tails, Volatility Clustering and Asset Prices of the Precious Metal

Ma¹,

Ding²,

Yang³

et al. 2017

IJARBSS

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The world palladium demand has increased steadily and dramatically in the years of 21th century. However, its spot price still has not reached the peak level observed in January 17, 2001. In 2008, a single-day increase of the palladium spot price has exceeded 37%, which witnesses significant risk for investments in the world palladium market. In this paper, we apply the GARCH model with heavy-tailed distributions into the palladium spot returns series for risk management purpose. We compare empirical performance of the Student's t distribution and the normal reciprocal inverse Gaussian (NRIG) distribution. Our results show the newlydeveloped distribution, the NRIG, cannot outperform the older fashion one, the Student's t distribution. Nevertheless, our results do demonstrate that it is important to incorporate conditional heavy tails for precious metals' spot return modelling.

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A Stochastic Factor Model for Risk Management of Commodity Derivatives

Abstract: Abstract:In

Cited by 8 publications

References 13 publications

GARCH Models with Fat-Tailed Distributions and the Hong Kong Stock Market Returns

GARCH Models with Fat-Tailed Distributions and the Hong Kong Stock Market Returns

Dynamic Stochastic Factors, Risk Management and the Energy Futures

Conditional Heavy Tails, Volatility Clustering and Asset Prices of the Precious Metal

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