Abstract In this paper we consider the problem of estimating expected shortfall (ES) for discrete time stochastic volatility (SV) models. Specifically, we develop Monte Carlo methods to evaluate ES for a variety of commonly used SV models. This includes both models where the innovations are independent of the volatility and where there is dependence. This dependence aims to capture the well-known leverage effect. The performance of our Monte Carlo methods is analyzed through simulations and empirical analyses of four major US indices
Stochastic volatility (SV) models provide useful tools to describe the evolution of asset returns, w...
We first propose a reduced-form model in discrete time for S&P 500 volatility showing that the forec...
Basel II requires Value at Risk (VaR) as a standardized risk measure for calculating market risk. Ho...
This paper is concerned with specification for modelling financial leverage effect in the context of...
In den letzten beiden Jahrzehnten hat in der ökonometrischen Finanzmarktforschung der Ansatz der Sto...
This paper proposes the efficient and fast Markov chain Monte Carlo estimation methods for the stoch...
This paper tests the parametric estimation method for Value at Risk and Expected Shortfall estimatio...
Intra-day sources of data have proven effective for dynamic volatility and tail risk estimation. Exp...
We analyze three different methods that can approximate the expected shortfall of a financial portfo...
With the regulatory requirements for risk management, Value at Risk (VaR) has become an essential to...
Published in Journal of Econometrics, August 2005, 127 (2), 165-178. https://doi.org/10.1016/j.jecon...
Bayesian inference is developed and applied for an extended Nelson–Siegel term structure model captu...
This paper investigates three formulations of the leverage effect in a stochastic volatility model w...
This paper examines the stochastic volatility model suggested by Heston (1993). We employ a time-ser...
Abstract: In this paper we propose a sequential Monte Carlo algorithm to estimate a stochastic volat...
Stochastic volatility (SV) models provide useful tools to describe the evolution of asset returns, w...
We first propose a reduced-form model in discrete time for S&P 500 volatility showing that the forec...
Basel II requires Value at Risk (VaR) as a standardized risk measure for calculating market risk. Ho...
This paper is concerned with specification for modelling financial leverage effect in the context of...
In den letzten beiden Jahrzehnten hat in der ökonometrischen Finanzmarktforschung der Ansatz der Sto...
This paper proposes the efficient and fast Markov chain Monte Carlo estimation methods for the stoch...
This paper tests the parametric estimation method for Value at Risk and Expected Shortfall estimatio...
Intra-day sources of data have proven effective for dynamic volatility and tail risk estimation. Exp...
We analyze three different methods that can approximate the expected shortfall of a financial portfo...
With the regulatory requirements for risk management, Value at Risk (VaR) has become an essential to...
Published in Journal of Econometrics, August 2005, 127 (2), 165-178. https://doi.org/10.1016/j.jecon...
Bayesian inference is developed and applied for an extended Nelson–Siegel term structure model captu...
This paper investigates three formulations of the leverage effect in a stochastic volatility model w...
This paper examines the stochastic volatility model suggested by Heston (1993). We employ a time-ser...
Abstract: In this paper we propose a sequential Monte Carlo algorithm to estimate a stochastic volat...
Stochastic volatility (SV) models provide useful tools to describe the evolution of asset returns, w...
We first propose a reduced-form model in discrete time for S&P 500 volatility showing that the forec...
Basel II requires Value at Risk (VaR) as a standardized risk measure for calculating market risk. Ho...