Singular Spectrum Analysis for Value at Risk in Stochastic Volatility models

2021 ◽  
Author(s):  
Josu Arteche ◽  
Javier García‐Enríquez
Keyword(s):  
At Risk ◽  
Stochastic Volatility ◽  
Spectrum Analysis ◽  
Value At Risk ◽  
Singular Spectrum Analysis ◽  
Stochastic Volatility Models ◽  
Singular Spectrum ◽  
Volatility Models

VALUE-AT-RISK COMPUTATIONS IN STOCHASTIC VOLATILITY MODELS USING SECOND-ORDER WEAK APPROXIMATION SCHEMES

2014 ◽  
Vol 17 (01) ◽  
pp. 1450004
Author(s):  
EVA LÜTKEBOHMERT ◽  
LYDIENNE MATCHIE
Keyword(s):  
At Risk ◽  
Stochastic Volatility ◽  
Value At Risk ◽  
Second Order ◽  
Stochastic Volatility Model ◽  
Approximation Schemes ◽  
Weak Approximation ◽  
Volatility Models ◽  
Volatility Model ◽  
Model Correlation

We explore the class of second-order weak approximation schemes (cubature methods) for the numerical simulation of joint default probabilities in credit portfolios where the firm's asset value processes are assumed to follow the multivariate Heston stochastic volatility model. Correlation between firms' asset processes is reflected by the dependence on a common set of underlying risk factors. In particular, we consider the Ninomiya–Victoir algorithm and we study the application of this method for the computation of value-at-risk and expected shortfall. Numerical simulations for these quantities for some exogenous portfolios demonstrate the numerical efficiency of the method.

VaR/CVaR ESTIMATION UNDER STOCHASTIC VOLATILITY MODELS

2014 ◽  
Vol 17 (02) ◽  
pp. 1450009 ◽  
Author(s):  
CHUAN-HSIANG HAN ◽  
WEI-HAN LIU ◽  
TZU-YING CHEN
Keyword(s):  
At Risk ◽  
Stochastic Volatility ◽  
Value At Risk ◽  
Extreme Event ◽  
Stochastic Volatility Model ◽  
Conditional Value At Risk ◽  
Transform Method ◽  
Historical Simulation ◽  
Volatility Models ◽  
Volatility Model

This paper proposes an improved procedure for stochastic volatility model estimation with an application to Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR) estimation. This improved procedure is composed of the following instrumental components: Fourier transform method for volatility estimation, and importance sampling for extreme event probability estimation. The empirical analysis is based on several foreign exchange series and the S&P 500 index data. In comparison with empirical results by RiskMetrics, historical simulation, and the GARCH(1,1) model, our improved procedure outperforms on average.

MODELING THE DYNAMICS OF INTERNATIONAL AGRICULTURAL COMMODITY PRICES: A COMPARISON OF GARCH AND STOCHASTIC VOLATILITY MODELS

2018 ◽  
Vol 13 (03) ◽  
pp. 1850010 ◽  
Author(s):  
LU YANG ◽  
SHIGEYUKI HAMORI
Keyword(s):  
Stochastic Volatility ◽  
Value At Risk ◽  
Commodity Prices ◽  
Jump Process ◽  
Stochastic Volatility Model ◽  
Asymmetric Effect ◽  
Agricultural Commodity ◽  
Stochastic Volatility Models ◽  
Volatility Models ◽  
Volatility Model

In this study, we employ generalized autoregressive conditional heteroscedastic (GARCH) and stochastic volatility models to investigate the dynamics of wheat, corn, and soybean prices. We find that the stochastic volatility model provides the highest persistence of the volatility estimation in all cases. In addition, based on the monthly data, we find that the jump process and asymmetric effect do not exist in agricultural commodity prices. Finally, by estimating Value at risk (VaR) for these agricultural commodities, we find that the upsurge in agricultural prices in 2008 may have been caused by financialization.

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