Bootstrap Prediction in Univariate Volatility Models with Leverage Effect

2013 ◽  
Author(s):  
Carlos CCsar Truccos Maza ◽  
Luiz Koodi Hotta
Keyword(s):  
Leverage Effect ◽  
Volatility Models

scholarly journals Leverage Effect for Volatility with Generalized Laplace Error

2014 ◽  
Vol 29 (2) ◽  
Author(s):  
Farrukh Javed ◽  
Krzysztof Podgórski
Keyword(s):  
Good News ◽  
Leverage Effect ◽  
Economic Time Series ◽  
News Shocks ◽  
Fundamental Properties ◽  
The Past ◽  
Volatility Models ◽  
Asymmetric Power ◽  
Power Models ◽  
Economic Time

AbstractWe propose a new model that accounts for the asymmetric response of volatility to positive (`good news') and negative (`bad news') shocks in economic time series – the so-called leverage effect. In the past, asymmetric powers of errors in the conditionally heteroskedastic models have been used to capture this effect. Our model is using the gamma difference representation of the generalized Laplace distributions that efficiently models the asymmetry. It has one additional natural parameter, the shape, that is used instead of power in the asymmetric power models to capture the strength of a long-lasting effect of shocks. Some fundamental properties of the model are provided including the formula for covariances and an explicit form for the conditional distribution of `bad' and `good' news processes given the past – the property that is important for statistical fitting of the model. Relevant features of volatility models are illustrated using S&P 500 historical data.

scholarly journals Think again: volatility asymmetry and volatility persistence

2018 ◽  
Vol 23 (1) ◽  
Author(s):  
Dirk G. Baur ◽  
Thomas Dimpfl
Keyword(s):  
Quantile Regression ◽  
Realized Volatility ◽  
Leverage Effect ◽  
Variance Estimator ◽  
Volatility Persistence ◽  
Volatility Models ◽  
Volatility Asymmetry ◽  
High Volatility ◽  
Volatility Regimes ◽  
Volatility Feedback

Abstract We use a leveraged quantile heterogeneous autoregressive model of realized volatility to illustrate that volatility persistence and the asymmetric “leverage” effect are high volatility phenomena. More specifically, we find that (i) low volatility is not persistent, but high volatility all the more, even featuring properties of explosive processes; and (ii) asymmetry of volatility is only a high volatility phenomenon and there is no asymmetry in low volatility regimes. Our results turn out to be robust to the choice of the realized variance estimator, in particular with respect to jumps. The analysis illustrates that quantile regression can provide information that is hidden in commonly used GARCH or realized volatility models. The quantile regression results can also be linked to the weak empirical evidence of the leverage effect and the volatility feedback effect.

scholarly journals A note on calculating expected shortfall for discrete time stochastic volatility models

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Michael Grabchak ◽  
Eliana Christou
Keyword(s):  
Monte Carlo ◽  
Stochastic Volatility ◽  
Discrete Time ◽  
Monte Carlo Methods ◽  
Expected Shortfall ◽  
Leverage Effect ◽  
Stochastic Volatility Models ◽  
Volatility Models ◽  
Empirical Analyses

AbstractIn 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.

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