Optimally harnessing inter-day and intra-day information for daily value-at-risk prediction
Ana-Maria Fuertes,Jose Olmo +1 more
TLDR
In this article, quantile regression theory is used to obtain a combination of individual potentially-biased VaR forecasts that is optimal because, by construction, it meets the correct out-of-sample conditional coverage criterion ex post.About:
This article is published in International Journal of Forecasting.The article was published on 2013-01-01 and is currently open access. It has received 37 citations till now. The article focuses on the topics: Value at risk & Quantile regression.read more
Citations
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Frontiers in VaR forecasting and backtesting
María Rosa Nieto,Esther Ruiz +1 more
TL;DR: There are vast numbers of alternative methods for constructing and evaluating value at risk (VaR) forecasts as mentioned in this paper, which are used as a testing ground when fitting alternative models for representing the dynamic evolution of time series of financial returns.
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Realized volatility models and alternative Value-at-Risk prediction strategies
TL;DR: 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.
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Forecast combinations for value at risk and expected shortfall
TL;DR: In this article, the authors used a set of scoring functions for the joint estimation or backtesting of the value at risk and expected shortfall (ES) forecasts, and showed that combining outperforms the individual methods for the 1% and 5% probability levels.
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Overnight stock returns and realized volatility
Katja Ahoniemi,Markku Lanne +1 more
TL;DR: In this article, the S&P 500 realized volatility estimator that optimally incorporates overnight information is more accurate in-sample than estimators that do not incorporate overnight information for individual stocks, and there is considerably less variation in the selection of the best out-of-sample forecasting model when only the most accurate insample RV estimators are considered.
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Overnight information flow and realized volatility forecasting
Neda Todorova,Michael Soucek +1 more
TL;DR: In this paper, the authors compared various approaches for incorporating the overnight information flow for forecasting realized volatility of the Australian index ASX 200 and seven very liquid Australian shares from March 2007 to January 2014, and found that considering overnight information separately rather than adding it to the daily realized volatility estimates leads consistently to better out-of-sample results despite the higher number of involved parameters.
References
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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.
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Comparing Predictive Accuracy
TL;DR: In this article, explicit tests of the null hypothesis of no difference in the accuracy of two competing forecasts are proposed and evaluated, and asymptotic and exact finite-sample tests are proposed, evaluated and illustrated.
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An introduction to long‐memory time series models and fractional differencing
TL;DR: Generation and estimation of these models are considered and applications on generated and real data presented, showing potentially useful long-memory forecasting properties.
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Combining forecasts: A review and annotated bibliography
TL;DR: In this article, the authors provide a review and annotated bibliography of that literature, including contributions from the forecasting, psychology, statistics, and management science literatures, providing a guide to the literature for students and researchers and to help researchers locate contributions in specific areas, both theoretical and applied.
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CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles *
TL;DR: In this article, the authors propose a new approach to quantile estimation which does not require any of the extreme assumptions invoked by existing methodologies (such as normality or i.i.d. returns).