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An approximate long-memory range-based approach for value at risk estimation · Study

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ArticleAn approximate long-memory range-based approach for value at risk estimation

Xiaochun Meng2018

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Articlegreen2018

An approximate long-memory range-based approach for value at risk estimation

33 citations·0/0 files stored·International Journal of Forecasting

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Financial Risk and Volatility Modeling Stochastic processes and financial applications Stock Market Forecasting Methods

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References

1993On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks 2007Strictly Proper Scoring Rules, Prediction, and Estimation 1994Comparing Predictive Accuracy 2008Introductory Econometrics for Finance 1998Answering the Skeptics: Yes, Standard Volatility Models do Provide Accurate Forecasts 2008A Simple Approximate Long-Memory Model of Realized Volatility 1996Fractionally integrated generalized autoregressive conditional heteroskedasticity 2001The distribution of realized stock return volatility

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2021Portfolio Value-at-Risk and expected-shortfall using an efficient simulation approach based on Gaussian Mixture Model

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Year2018

Typearticle

Published2018-03-30

VenueInternational Journal of Forecasting

ISSN-L0169-2070

Languageen

Citations33

Files0 public / 0

ShelfFinancial Risk and Volatility Modeling

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doi10.1016/j.ijforecast.2017.11.007

openalex_workW2795201140

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