Fat Tails Value at Risk and the Palladium Returns

Authors

  • Jianhua Ding

Keywords:

skewed t distribution; goodness of fit; riskmanagement

Abstract

The past decade has witnessed the rapid growing of the world palladium market. Thus, it is even more important to develop effective quantitative tools for risk management of palladium assets at this moment. In this paper, we investigate five different types of widely-used statistical distributions and employ the industry standard risk measurement, Value at Risk, for risk management of daily palladium spot returns. We first apply four different criteria to compare the goodness of fit of the five distributions, and then calculate the VaRs based on the parameters estimated from the first step. Our results indicate the Skewed t distribution has the best insample fitting and generate VaR values closest to the nonparametric historical VaR values.

How to Cite

Fat Tails Value at Risk and the Palladium Returns. (2018). Global Journal of Management and Business Research, 18(B3), 11-16. https://journalofbusiness.org/index.php/GJMBR/article/view/2490

References

B Adrangi, A Chatrath (2002) The dynamics of palladium and platinum prices. 19(2), 179-195.

H Akaike (1973) Information theory and an extension of the maximum likelihood principle. 267-281.

Benjamin Auer (2015) Superstitious seasonality in precious metals markets? Evidence from GARCH models with time-varying skewness and kurtosis. 47(27), 2844-2859.

O Barndorff -Nielsen (1977) Exponentially decreasing distributions for the logarithm of particle size. 353, 401-419.

Pedro Bueno, Emilio Fortes, Konstantinos Vlachoski (2017) Speculative Investment and Risk Management of Bitcoin Exchange Rate Returns. 5, 347-355.

Guglielmo Caporale, Fabio Spagnolo, Nicola Spagnolo (2017) Macro News and Commodity Returns. 22(1), 68-80.

R Cont (2001) Empirical properties of asset returns: stylized facts and statistical issues. 1, 223-236.

John Diaz (2016) Do Scarce Precious Metals Equate to Safe Harbor Investments? The Case of Platinum and Palladium. 2016, 1-7.

José Figueroa‐lópez, Steven Lancette, Kiseop Lee, Yanhui Mi (2011) Estimation of NIG and VG Models for High Frequency Financial Data. 1-25.

Zi-Yi Guo (2017) Heavy-Tailed Distributions and Risk Management of Equity Market Tail Events. 4, 31-41.

Z Guo (2017) GARCH models with fat-tailed distributions and the Hong Kong stock market returns. 12, 28-37.

B Hansen (1994) Autoregressive conditional density estimation. 35, 705-730.

S Hammoudeh, F Malik, M Aleer (2011) Risk management of precious metals. 51, 435-441.

F Helmert (1876) Über die Wahrscheinlichkeit der Potenzsummen der Beobachtungs fehler und uber einige damit in Zusammenhang stehende Fragen. 21, 192-218.

C Huber-Carol, N Balakrishnan, M Nikulin, M Mesbah (2002) Goodness-of-Fit Tests and Model Validity.

K Kayaba, Y Hirano, M Baba, N Matsui, N Ueda (2017) Normal reciprocal inverse Gaussian distribution and the stock market returns in Japan.

C Pierdzioch, M Risse, S Rohloff (2016) Are precious metals a hedge against exchange-rate movements? An empirical exploration using Bayesian additive regression trees. 38, 27-38.

K Prause (1999) The generalized hyperbolic model: estimation, financial derivatives, and risk measures.

D Taeger, S Kuhnt (2014) Goodness-of-fit tests.

Fat Tails Value at Risk and the Palladium Returns

Published

2018-05-18

How to Cite

Fat Tails Value at Risk and the Palladium Returns. (2018). Global Journal of Management and Business Research, 18(B3), 11-16. https://journalofbusiness.org/index.php/GJMBR/article/view/2490