Forecasting VaR using realized EGARCH model with skewness and kurtosis

“…Over a century ago, Thiele introduced cumulants (79), a concept now fundamental to the field of statistics (32,33,46,45,39,40), which has justifiably percolated throughout the scientific community (35,36,37,38,80). In this work, we introduce graph cumulants, their generalization to networks (fig.…”

Section: Discussionmentioning

confidence: 99%

“…The first two orders, the mean and variance, correspond to the center of mass and spread of a distribution, and are taught in nearly every introductory statistics course (34). The next two orders have likewise been given unique names (skew and kurtosis), and are useful to describe data that deviate from normality, appearing in a variety of applications, such as finance (35), economics (36), and psychology (37). Generalizations of these notions have proven similarly useful: for example, joint cumulants (e.g., covariance) are the natural choice for measuring correlations in multivariate random variables.…”

mentioning

confidence: 99%

Introducing Graph Cumulants: What is the Variance of Your Social Network?

Gunderson,

Bravo-Hermsdorff

2020

Preprint

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“…The improved model performs better in terms of parameter estimation and volatility forecasting. Considering the presence of time-varying skewness and kurtosis, Wu, Xia, and Zhang (2019) proposed the Realized EGARCH-SK model by building skewness and kurtosis equations into the Realized EGARCH model. Besides, some scholars believe that simple volatility models usually perform better than complex ones.…”

Section: Introductionmentioning

confidence: 99%

Forecasting volatility with outliers in Realized GARCH models

Cai

Peng

2020

Journal of Forecasting

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The Realized generalized autoregressive conditional heteroskedasticity (GARCH) model proposed by Hansen is often applied to forecast volatility in high-frequency financial data. It is frequently found, however, that the distribution of the estimated residuals from Realized GARCH models has peak fat-tail characteristics. Considering this feature may be a result of neglected additive outliers (AOs) and innovative outliers (IOs), this paper proposes the Realized GARCH model with additive outlier and innovative outlier (Realized GARCH-AI model) for forecasting volatility. This model can detect and correct abnormal returns and realized volatility by estimating the coefficients of volatility models and calculating the outlier test statistics. In the process of simulation, this paper considers different outlier cases in the GARCH model and the Realized GARCH model, and evaluates the performance of the proposed procedure through the accuracy of parameter estimation under different critical values. We find that the critical values will affect the results of outlier detection and correction. When the value is in a suitable range, the proposed procedure based on high-frequency data can obtain unbiased parameter estimation, and the estimation result is close to those of the intervention model containing outlier information. Finally, we use the MCS test proposed by Hansen et al. (Econometrica, 2011, 79(2), 453-497) to study the volatility prediction accuracy, value at risk, and expected shortfall prediction ability of the new model. The empirical analysis demonstrates that the proposed model produces better prediction effects than the traditional Realized GARCH model.

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Forecasting VaR using realized EGARCH model with skewness and kurtosis

Cited by 22 publications

References 18 publications

Multidimensional risk spillovers among crude oil, the US and Chinese stock markets: Evidence during the COVID-19 epidemic

Multidimensional risk spillovers among crude oil, the US and Chinese stock markets: Evidence during the COVID-19 epidemic

Introducing Graph Cumulants: What is the Variance of Your Social Network?

Forecasting volatility with outliers in Realized GARCH models

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