Journal ArticleDOI
Realized volatility models and alternative Value-at-Risk prediction strategies
TLDR
In this paper, the authors assess the value-at-risk (VaR) forecasting performance of recently proposed realized volatility models combined with alternative parametric and semi-parametric quantile estimation methods, and find that statistical accuracy and regulatory compliance is essentially improved when they use quantile methods which account for the fat tails and the asymmetry of the innovations distribution.About:
This article is published in Economic Modelling.The article was published on 2014-06-01. It has received 55 citations till now. The article focuses on the topics: Realized variance & Value at risk.read more
Citations
More filters
Journal ArticleDOI
Economic policy uncertainty and the Chinese stock market volatility: Novel evidence
TL;DR: In this article, the impact of global economic policy uncertainty (GEPU) on Chinese stock market volatility was investigated, and the effects of directional GEPU based on the changing directions of GEPU and Chinese economic uncertainty (EPU) were explored.
Journal ArticleDOI
Modeling long memory volatility using realized measures of volatility: A realized HAR GARCH model
Zhuo Huang,Hao Liu,Tianyi Wang +2 more
TL;DR: A parsimonious variant of the Realized GARCH model is developed by introducing the HAR specification of Corsi (2009) into the volatility dynamics, and a comparison of the theoretical and sample autocorrelation functions shows that the new model specification better captures the long memory dynamics of volatility.
Journal ArticleDOI
Combining Value-at-Risk forecasts using penalized quantile regressions
TL;DR: In this article, the authors proposed penalized quantile regressions for the combination of value-at-risk forecasts for stocks comprising the Dow Jones Industrial Average Index, which was applied to combining the Value-At-Risk forecasts of a wide range of frequently used risk models.
Proceedings ArticleDOI
Econometric Forecasting Models Based on Forecast Combination Methods
TL;DR: In this work econometric forecasting models which increase the accuracy of the forecast are introduced on the basis of the methodology of combined forecast and wavelet analysis.
References
More filters
Journal ArticleDOI
Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation
TL;DR: In this article, a new class of stochastic processes called autoregressive conditional heteroscedastic (ARCH) processes are introduced, which are mean zero, serially uncorrelated processes with nonconstant variances conditional on the past, but constant unconditional variances.
Journal ArticleDOI
Generalized autoregressive conditional heteroskedasticity
Tim Bollerslev,Tim Bollerslev +1 more
TL;DR: In this paper, a natural generalization of the ARCH (Autoregressive Conditional Heteroskedastic) process introduced in 1982 to allow for past conditional variances in the current conditional variance equation is proposed.
Journal ArticleDOI
Conditional heteroskedasticity in asset returns: a new approach
TL;DR: In this article, an exponential ARCH model is proposed to study volatility changes and the risk premium on the CRSP Value-Weighted Market Index from 1962 to 1987, which is an improvement over the widely-used GARCH model.
Journal ArticleDOI
On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks
TL;DR: In this article, a modified GARCH-M model was used to find a negative relation between conditional expected monthly return and conditional variance of monthly return, using seasonal patterns in volatility and nominal interest rates to predict conditional variance.
Posted Content
Comparing Predictive Accuracy
TL;DR: The authors describes the advantages of these studies and suggests how they can be improved and also provides aids in judging the validity of inferences they draw, such as multiple treatment and comparison groups and multiple pre- or post-intervention observations.