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Range-based volatility estimation and forecasting
Benčík, Daniel - 2014
In this paper, we analyze new possibilities in predicting daily ranges, i.e. differences between daily high and low prices. We empirically assess efficiency gains in volatility estimation when using range-based estimators as opposed to simple daily ranges and explore the use of these more...
Persistent link: https://www.econbiz.de/10010461231
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Bayesian predictive distributions of oil returns using mixed data sampling volatility models
Virbickaite, Audrone; Nguyen, Hoang; Minh-Ngoc Tran - 2023
such as Generalized Autoregressive Conditional Heteroskedastic (GARCH), Generalized Autoregressive Score (GAS), and … relevant financial/macroeconomic news into asset price movements. For inference and prediction, we employ an innovative … inclusion of exogenous variables is beneficial for GARCH-type models while offering only a marginal improvement for GAS and SV …
Persistent link: https://www.econbiz.de/10014252427
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Forecasting volatility with Copula-based time series models
Sokolinskiy, Oleg; Dijk, Dick van - 2011
Persistent link: https://www.econbiz.de/10009720755
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Censored posterior and predictive likelihood in Bayesian left-tail prediction for accurate value at risk estimation
Gatarek, Lukasz; Hoogerheide, Lennart; Hooning, Koen; … - 2013
Persistent link: https://www.econbiz.de/10009756308
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Exchange-rates volatility in Nigeria : application of GARCH models with exogenous break
Bala, Dahiru A.; Asemota, Joseph O. - In: CBN journal of applied statistics 4 (2013) 1, pp. 89-116
This paper examines exchange-rate volatility with GARCH models using monthly exchange-rate return series from 1985:1 to 2011:7 for Naira/US dollar return and from 2004:1 to 2011:7 for Naira/British Pounds and Naira/Euro returns. The study compare estimates of variants of GARCH models with break...
Persistent link: https://www.econbiz.de/10011476095
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Right on target, or is it? : the role of distributional shape in variance targeting
Anatolyev, Stanislav; Khrapov, Stanislav - In: Econometrics : open access journal 3 (2015) 3, pp. 610-632
Estimation of GARCH models can be simplified by augmenting quasi-maximum likelihood (QML) estimation with variance targeting, which reduces the degree of parameterization and facilitates estimation. We compare the two approaches and investigate, via simulations, how non-normality features of the...
Persistent link: https://www.econbiz.de/10011410634
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Censored Posterior and Predictive Likelihood in Bayesian Left-Tail Prediction for Accurate Value at Risk Estimation
Gatarek, Lukasz T. - 2014
Accurate prediction of risk measures such as Value at Risk (VaR) and Expected Shortfall (ES) requires precise … techniques in applications of Value-at-Risk prediction in GARCH models …
Persistent link: https://www.econbiz.de/10013064150
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Realized Range Volatility Forecasting : Dynamic Features and Predictive Variables
Caporin, Massimiliano - 2013
In this paper, we estimate, model and forecast Realized Range Volatility, a realized measure and estimator of the quadratic variation of financial prices. This quantity was early introduced in the literature and it is based on the high-low range observed at high frequency during the day. We...
Persistent link: https://www.econbiz.de/10013076452
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Time-Varying Realized GARCH Models for Tracking Measurement Error Bias in Volatility Forecasting
Gerlach, Richard H. - 2020
. The merits of the proposed specifications are assessed by means of an application to the prediction of Value at Risk (VaR …
Persistent link: https://www.econbiz.de/10012839665
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Latent Common Return Volatility Factors : Capturing Elusive Predictive Accuracy Gains When Forecasting Volatility
Cheng, Mingmian - 2017
In this paper, we use factor-augmented HAR-type models to predict the daily integrated volatility of asset returns. Our approach is based on a proposed two-step dimension reduction procedure designed to extract latent common volatility factors from a large dimensional and high-frequency returns...
Persistent link: https://www.econbiz.de/10012952724