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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

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

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

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

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

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

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

Volatility Forecasting : Downside Risk, Jumps and Leverage Effect

Audrino, Francesco - 2011

We provide new empirical evidence on volatility forecasting in relation to asymmetries present in the dynamics of both return and volatility processes. Leverage and volatility feedback effects of the S&P 500 price and volatility dynamics are examined using recently developed methodologies to...

Persistent link: https://www.econbiz.de/10013119824

Good Volatility, Bad Volatility : Signed Jumps and the Persistence of Volatility

Patton, Andrew J. - 2013

Abstract Using recently proposed estimators of the variation of positive and negative returns (“realized semivariances”), and high frequency data for the S&P 500 index and 105 individual stocks, this paper sheds new light on the predictability of equity price volatility. We show that future...

Persistent link: https://www.econbiz.de/10013092293

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

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