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Jump variation estimation with noisy high frequency financial data via wavelets
Zhang, Xin; Kim, Donggyu; Wang, Yazhen - In: Econometrics : open access journal 4 (2016) 3, pp. 1-26
This paper develops a method to improve the estimation of jump variation using high frequency data with the existence of market microstructure noises. Accurate estimation of jump variation is in high demand, as it is an important component of volatility in finance for portfolio allocation,...
Persistent link: https://www.econbiz.de/10011568279
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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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Conditional variance forecasts for long-term stock returns
Mammen, Enno; Nielsen, Jens Perch; Scholz, Michael; … - In: Risks : open access journal 7 (2019) 4/113, pp. 1-22
squared return prediction errors gives an adequate approximation of the unobserved realised conditional variance for both the …
Persistent link: https://www.econbiz.de/10012127861
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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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Empirical evidence on the importance of aggregation, asymmetry, and jumps for volatility prediction
Duong, Diep; Swanson, Norman R. - 2013
Ding, Granger and Engle (1993) and Ding and Granger (1996). Our prediction experiments use high frequency price returns … variations help less in the prediction of future realized volatility, than past "small" jump power variations. Additionally, we …, are strongly correlated with future volatility, and that past downside jump variations matter in prediction. Finally …
Persistent link: https://www.econbiz.de/10009771770
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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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Volatility forecasting : downside risk, jumps and leverage effect
Audrino, Francesco; Hu, Yujia - In: Econometrics : open access journal 4 (2016) 1, pp. 1-24
We provide empirical evidence of volatility forecasting in relation to asymmetries present in the dynamics of both return and volatility processes. Using recently-developed methodologies to detect jumps from high frequency price data, we estimate the size of positive and negative jumps and...
Persistent link: https://www.econbiz.de/10011504739
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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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Modeling and Forecasting Realized Range Volatility
Caporin, Massimiliano - 2011
In this paper, we estimate, model and forecast Realized Range Volatility, a new realized measure and estimator of the quadratic variation of financial prices. This estimator 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/10013130487