A Comparison of Mixed GARCH-Jump Models with Skewed t-Distribution for Asset Returns

2005 ◽  
Author(s):  
Jung-Suk Yu ◽  
Elton Daal
Keyword(s):  
Asset Returns ◽  
T Distribution ◽  
Skewed T Distribution

Skewed t-distribution for Hyperspectral Anomaly Detection based on Autoencoder

2021 ◽  
pp. 1-1
Author(s):  
Koray Kayabol ◽  
Ensar Burak Aytekin ◽  
Sertac Arisoy ◽  
Ercan Engin Kuruoglu
Keyword(s):  
Anomaly Detection ◽  
T Distribution ◽  
Skewed T Distribution

scholarly journals Measuring risk based on skewed t distribution approach

2021 ◽  
Vol 1943 (1) ◽  
pp. 012143
Author(s):  
E Sulistianingsih ◽  
D Rosadi ◽  
Abdurakhman
Keyword(s):  
T Distribution ◽  
Skewed T Distribution

Value-at-Risk Forecasting using Realized Volatility Models and GARCH-type Models

2013 ◽  
Vol 21 (2) ◽  
pp. 135-167
Author(s):  
Chan-Soo Jeon
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Realized Volatility ◽  
Distribution Model ◽  
Distribution Models ◽  
Volatility Models ◽  
Risk Forecasting ◽  
T Distribution ◽  
Daily Returns ◽  
Skewed T Distribution

The aim of this paper is to compare the performance of VaR (value-at-risk) using Realized Volatility Models (which use intraday returns) with VaR the performance of GARCH-type Models (which use daily returns) with three different distribution innovations (normal distribution, t-distribution, skewed t-distribution). In this paper, we empirically examine VaR forecast of korean stock market using KOSPI and KOSDAQ. Empirical results indicate that the Realized Volatility models is superior to the GARCH-type models in forecasting VaR. We also find Var forecast by skewed t-distribution model are more accurate than those using the normal and t-distribution models. Thus, VaR using Realized Volatility models and skewed t-distribution enhances the performance of risk management in Korean financial markets.

scholarly journals Simulation-Based Optimal Portfolio Selection Strategy—Evidence from Asian Markets

2018 ◽  
Vol 5 (5) ◽  
pp. 1
Author(s):  
Longqing Li
Keyword(s):  
At Risk ◽  
Portfolio Optimization ◽  
Portfolio Selection ◽  
Value At Risk ◽  
Optimal Portfolio ◽  
Conditional Value At Risk ◽  
Selection Strategy ◽  
Optimal Portfolio Selection ◽  
T Distribution ◽  
Skewed T Distribution

Recently portfolio optimization has become widely popular in risk management, and the common practice is to use mean-variance or Value-at-Risk (VaR), despite the VaR being incoherent risk measure because of the lack of subadditivity. This has led to the emergence of the conditional value-at-risk (CVaR) approach, consequently, a gradual development of mean-CVaR portfolio optimization. To seek an optimal portfolio selection strategy and increase the robustness of the result, the paper studies the performance of portfolio optimization in Asian markets using a Monte-Carlo simulation tool, creates a variety of randomly selected portfolios that consists of Asian ADRs listed in NYSE from 2011 to 2016, and applies both optimization frameworks with different skewed fat-tailed distributions, including the Generalized Hyperbolic (GH) and skewed-T distribution. The main result shows that the Generalized Hyperbolic distribution produces the lowest risk under a given rate of return, while the skewed-T distribution creates a diversification allocation outcome similar to that of historical simulation.

scholarly journals Simulation-Based Optimal Portfolio Selection Strategy—Evidence from Asian Markets

2018 ◽  
Vol 5 (5) ◽  
pp. 1
Author(s):  
Longqing Li
Keyword(s):  
At Risk ◽  
Portfolio Optimization ◽  
Portfolio Selection ◽  
Value At Risk ◽  
Optimal Portfolio ◽  
Conditional Value At Risk ◽  
Selection Strategy ◽  
Optimal Portfolio Selection ◽  
T Distribution ◽  
Skewed T Distribution

Recently portfolio optimization has become widely popular in risk management, and the common practice is to use mean-variance or Value-at-Risk (VaR), despite the VaR being incoherent risk measure because of the lack of subadditivity. This has led to the emergence of the conditional value-at-risk (CVaR) approach, consequently, a gradual development of mean-CVaR portfolio optimization. To seek an optimal portfolio selection strategy and increase the robustness of the result, the paper studies the performance of portfolio optimization in Asian markets using a Monte-Carlo simulation tool, creates a variety of randomly selected portfolios that consists of Asian ADRs listed in NYSE from 2011 to 2016, and applies both optimization frameworks with different skewed fat-tailed distributions, including the Generalized Hyperbolic (GH) and skewed-T distribution. The main result shows that the Generalized Hyperbolic distribution produces the lowest risk under a given rate of return, while the skewed-T distribution creates a diversification allocation outcome similar to that of historical simulation.

scholarly journals Multiscale Stochastic Volatility Model with Heavy Tails and Leverage Effects

2021 ◽  
Vol 14 (5) ◽  
pp. 225
Author(s):  
Zhongxian Men ◽  
Tony S. Wirjanto ◽  
Adam W. Kolkiewicz
Keyword(s):  
Stochastic Volatility ◽  
Heavy Tails ◽  
Foreign Currency ◽  
Asset Returns ◽  
Stochastic Volatility Model ◽  
Stochastic Volatility Models ◽  
Asymmetric Effects ◽  
Volatility Models ◽  
Volatility Model ◽  
T Distribution

This paper studies multiscale stochastic volatility models of financial asset returns. It specifies two components in the log-volatility process and allows for leverage/asymmetric effects from both components while return innovation terms follow a heavy/fat tailed Student t distribution. The two components are shown to be important in capturing persistent dependence in return volatility, which is often absent in applications of stochastic volatility models which incorporate leverage/asymmetric effects. The models are applied to asset returns from a foreign currency market and an equity market. The model fits are assessed, and the proposed models are shown to compare favorably to the one-component asymmetric stochastic volatility models with Gaussian and Student t distributed innovation terms.

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