Does Modeling Jumps Help? A Comparison of Realized Volatility Models for Risk Prediction

2012 ◽  
Author(s):  
Yin Liao
Keyword(s):  
Risk Prediction ◽  
Realized Volatility ◽  
Volatility Models

Realized volatility models and alternative Value-at-Risk prediction strategies

2014 ◽  
Vol 40 ◽  
pp. 101-116 ◽  
Author(s):  
Dimitrios P. Louzis ◽  
Spyros Xanthopoulos-Sisinis ◽  
Apostolos P. Refenes
Keyword(s):  
At Risk ◽  
Risk Prediction ◽  
Value At Risk ◽  
Realized Volatility ◽  
Volatility Models

scholarly journals QML INFERENCE FOR VOLATILITY MODELS WITH COVARIATES

2018 ◽  
Vol 35 (1) ◽  
pp. 37-72 ◽  
Author(s):  
Christian Francq ◽  
Le Quyen Thieu
Keyword(s):  
Realized Volatility ◽  
Garch Models ◽  
Likelihood Estimator ◽  
Asymptotic Results ◽  
Monte Carlo Experiments ◽  
True Value ◽  
Volatility Models ◽  
Information Sets ◽  
Asymmetric Garch ◽  
Quasi Maximum Likelihood

The asymptotic distribution of the Gaussian quasi-maximum likelihood estimator (QMLE) is obtained for a wide class of asymmetric GARCH models with exogenous covariates. The true value of the parameter is not restricted to belong to the interior of the parameter space, which allows us to derive tests for the significance of the parameters. In particular, the relevance of the exogenous variables can be assessed. The results are obtained without assuming that the innovations are independent, which allows conditioning on different information sets. Monte Carlo experiments and applications to financial series illustrate the asymptotic results. In particular, an empirical study demonstrates that the realized volatility can be a helpful covariate for predicting squared returns.

Time-Varying Parameter Realized Volatility Models

2016 ◽  
Vol 36 (5) ◽  
pp. 566-580 ◽  
Author(s):  
Yudong Wang ◽  
Zhiyuan Pan ◽  
Chongfeng Wu
Keyword(s):  
Realized Volatility ◽  
Time Varying ◽  
Volatility Models ◽  
Time Varying Parameter

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.

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