scholarly journals Value-at-risk for the USD/ZAR exchange rate: The Variance-Gamma model

2015 ◽  
Vol 18 (4) ◽  
pp. 551-566 ◽  
Author(s):  
Lionel Establet Kemda ◽  
Chun-Kai Huang ◽  
Knowledge Chinhamu
Keyword(s):  
At Risk ◽  
Exchange Rates ◽  
Value At Risk ◽  
Financial Stability ◽  
Financial Time Series ◽  
Information Criterion ◽  
Ratio Test ◽  
Gamma Model ◽  
Variance Gamma ◽  
Variance Gamma Model

A country’s level of exchange risk is closely linked to its financial stability, on a macro-economic scale. South African exchange rates, in particular, have a significant impact on imports, inflation, consumer prices and monetary policies. Consequently, it is imperative for economists and investors to assess accurately the associated exchange risks. Exchange rates, like most financial time series, are leptokurtic and contradict the classical Gaussian assumption. We therefore introduce subclasses of the generalised hyperbolic distribution as alternative models and contrast these with the normal distribution. We conclude that the variance-gamma model is the most robust for describing the log-returns of daily USD/ZAR exchange rates and their related Value-at-Risk (VaR) estimates. The model selection methodologies utilised in our analyses include the robust Kolmogorov-Smirnov test and the Akaike information criterion. Backtesting on the adequacy of VaR estimates is also performed using the Kupiec likelihood ratio test.

scholarly journals Value-at-risk for the USD/ZAR exchange rate : the variance-gamma model

2015 ◽  
Vol 18 (4) ◽  
pp. 551-556
Author(s):  
Kemda, Lionel Establet ◽  
Huang, Chun-Kai ◽  
Chinhamu, Knowledge
Keyword(s):  
At Risk ◽  
Exchange Rate ◽  
Value At Risk ◽  
Gamma Model ◽  
Variance Gamma ◽  
Variance Gamma Model

On risk measuring in the variance-gamma model

2018 ◽  
Vol 35 (1-2) ◽  
pp. 23-33 ◽  
Author(s):  
Roman V. Ivanov
Keyword(s):  
Value At Risk ◽  
Hypergeometric Functions ◽  
Risk Measures ◽  
Random Variables ◽  
Gamma Model ◽  
Variance Gamma ◽  
Stochastic Volatilities ◽  
Investment Returns ◽  
Variance Gamma Model ◽  
Analytical Expressions

AbstractIn this paper, we discuss the problem of calculating the primary risk measures in the variance-gamma model. A portfolio of investments in a one-period setting is considered. It is supposed that the investment returns are dependent on each other. In terms of the variance-gamma model, we assume that there are relations in both groups of the normal random variables and the gamma stochastic volatilities. The value at risk, the expected shortfall and the entropic monetary risk measures are discussed. The obtained analytical expressions are based on values of hypergeometric functions.

scholarly journals A Variance Gamma model for Rugby Union matches

2020 ◽  
Vol 17 (1) ◽  
pp. 67-75
Author(s):  
John Fry ◽  
Oliver Smart ◽  
Jean-Philippe Serbera ◽  
Bernhard Klar
Keyword(s):  
Three Dimensional ◽  
Rugby Union ◽  
Three Dimensional Model ◽  
Gamma Model ◽  
Variance Gamma ◽  
Poisson Models ◽  
History Of ◽  
Bonus Points ◽  
Variance Gamma Model ◽  
Special Case

Abstract Amid much recent interest we discuss a Variance Gamma model for Rugby Union matches (applications to other sports are possible). Our model emerges as a special case of the recently introduced Gamma Difference distribution though there is a rich history of applied work using the Variance Gamma distribution – particularly in finance. Restricting to this special case adds analytical tractability and computational ease. Our three-dimensional model extends classical two-dimensional Poisson models for soccer. Analytical results are obtained for match outcomes, total score and the awarding of bonus points. Model calibration is demonstrated using historical results, bookmakers’ data and tournament simulations.

The β-variance gamma model

2010 ◽  
Vol 14 (3) ◽  
pp. 263-282 ◽  
Author(s):  
Wim Schoutens ◽  
Geert Van Damme
Keyword(s):  
Gamma Model ◽  
Variance Gamma ◽  
Variance Gamma Model

What do the value-at-risk measure and the respective legislative framework really offer to financial stability? Critical views and pro-cyclicality

2020 ◽  
Vol 17 (1) ◽  
pp. 39-60
Author(s):  
Evangelos Vasileiou ◽  
Themistoclis Pantos
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Financial Stability ◽  
Financial Risk ◽  
Risk Measure ◽  
Counterparty Risk ◽  
Financial Industry ◽  
Historical Simulation ◽  
Statistical Measures ◽  
Legislative Framework

In this paper, we examine how value at risk (VaR) contributes to the financial market's stability. We apply the Guidelines on Risk Measurement and the Calculation of Global Exposure and Counterparty Risk for UCITS of the Committee of European Securities Regulators (CESR 2010) to the main indices of the 12 stock markets of the countries that have used the euro as their official currency since its initial circulation. We show that gaps in the legislative framework give incentives to investment funds to adopt conventional models for the VaR estimation in order to avoid the increased costs that the advanced models involve. For this reason, we apply the commonly used historical simulation VaR (HVaR) model, which is: (i) taught at most finance classes; (ii) widely applied in the financial industry; and (iii) accepted by CESR (2010). The empirical evidence shows the HVaR does not really contribute to financial stability, and the legislative framework does not offer the appropriate guidance. The HVaR model is not representative of the real financial risk, and does not give any signal for trends in the near future. The HVaR is absolutely backward-looking and this increases the stock market's overreaction. The fact that the suggested confidence level in CESR (2010) is set at 99 percent leads to hidden pro-cyclicality. Scholars and researchers should focus on issues such as the abovementioned, otherwise the VaR estimations will become, sooner or later, just a formality, and such conventional statistical measures rarely contribute to financial stability.

scholarly journals Fitting the Variance-Gamma Model: A Goodness-of-Fit Check for Emerging Markets

2013 ◽  
Vol 27 (2) ◽  
pp. 1-10
Author(s):  
Ahmet Göncü ◽  
Mehmet Oğuz Karahan ◽  
Tolga Umut Kuzubaş
Keyword(s):  
Emerging Markets ◽  
Goodness Of Fit ◽  
Gamma Model ◽  
Variance Gamma ◽  
Variance Gamma Model
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