• International Area Studies Review
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• v.12 no.2
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• pp.3-26
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• 2008

This article investigates the usefulness of the skewed Student-t distribution in modeling the long memory volatility property that might be present in the daily returns of two Australian financial series; the ASX200 stock index and AUD/USD exchange rate. For this purpose we assess the performance of FIGARCH and FIAPARCH Value-at-Risk (VaR) models based on the normal, Student-t, and skewed Student-t distribution innovations. Our results support the argument that the skewed Student-t distribution models produce more accurate VaR estimates of Australian financial markets than the normal and Student-t distribution models. Thus, consideration of skewness and excess kurtosis in asset return distributions provides appropriate criteria for model selection in the context of long memory volatility models in Australian stock and foreign exchange markets.

• Environmental and Resource Economics Review
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• v.16 no.4
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• pp.947-978
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• 2007

In this paper, we investigated a Value-at-Risk approach to the volatility of two crude oil markets (Brent and Dubai). We also assessed the performance of various VaR models (RiskMetrics, GARCH, IGARCH and FIGARCH models) with the normal and skewed Student-t distribution innovations. The FIGARCH model outperforms the GARCH and IGARCH models in capturing the long memory property in the volatility of crude oil markets returns. This implies that the long memory property is prevalent in the volatility of crude oil returns. In addition, from the results of VaR analysis, the FIGARCH model with the skewed Student-t distribution innovation predicts critical loss more accurately than other models with the normal distribution innovation for both long and short positions. This finding indicates that the skewed Student-t distribution innovation is better for modeling the skewness and excess kurtosis in the distribution of crude oil returns. Overall, these findings might improve the measurement of the dynamics of crude oil prices and provide an accurate estimation of VaR for buyers and sellers in crude oil markets.

• The Korean Journal of Applied Statistics
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• v.30 no.5
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• pp.775-791
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• 2017

We frequently encounter binary data in real life. Logistic, Probit, Cauchit, Complementary log-log models are often used for binary data analysis. In order to analyze binary data, Liu (2004) proposed a Robit model, in which the inverse of cdf of the Student's t distribution is used as a link function. Kim et al. (2008) also proposed a generalized t-link model to make the binary regression model more flexible. The more flexible skewed distributions allow more flexible link functions in generalized linear models. In the sense, we propose a binary data regression model using skewed generalized t distributions introduced in Theodossiou (1998). We implement R code of the proposed models using the glm function included in R base and R sgt package. We also analyze Pima Indian data using the proposed model in R.