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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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High Frequency vs. Daily Resolution : The Economic Value of Forecasting Volatility Models
Lilla, Francesca - 2016
Forecasting-volatility models typically rely on either daily or high frequency (HF) data and the choice between these two categories is not obvious. In particular, the latter allows to treat volatility as observable but they suffer of many limitations. HF data feature microstructure problem,...
Persistent link: https://www.econbiz.de/10014124325
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Forecasting Realized Volatility : Does Anything Beat Linear Models?
Branco, Rafael; Rubesam, Alexandre; Zevallos, Mauricio - 2022
We evaluate the performance of several linear and nonlinear machine learning models in forecasting the realized volatility (RV) of ten global stock market indices in the period from January 2000 to December 2021. We train models using a dataset which includes past values of the RV and additional...
Persistent link: https://www.econbiz.de/10014076641
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The Limits to Volatility Predictability : Quantifying Forecast Accuracy Across Horizons
Zakamulin, Valeriy; Li, Xingyi - 2018
Volatility forecasting is crucial for portfolio management, risk management, and pricing of derivative securities. Still, little is known about how far ahead one can forecast volatility. First, in this paper we introduce the notions of the spot and forward predicted volatilities and propose to...
Persistent link: https://www.econbiz.de/10014111954
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More Power for Out-Of-Sample Tests of Predictability
Pincheira, Pablo M. - 2017
environment is one in which the econometrician wants to compare the population Mean Squared Prediction Errors (MSPE) of two models …
Persistent link: https://www.econbiz.de/10012962463
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Does Higher-Frequency Data Always Help to Predict Longer-Horizon Volatility?
Charoenwong, Ben - 2017
No. Conditional autocorrelation in realized shocks due to misspecification in expected return process affects the relative performance of longer-horizon volatility predictions of models using different frequencies of data. This is because, for multi-step forecasts of volatility, small violations...
Persistent link: https://www.econbiz.de/10012969447
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On Heteroskedasticity and Regimes in Volatility Forecasting
Cipollini, Fabrizio - 2017
In this paper we discuss some deep implications of the recent paper by Bollerslev et al. (2016) (BPQ). In BPQ the volatility dynamics modeled as a HAR is augmented by a term involving quarticity in order to correct measurement errors in realized variance. We show that the model is...
Persistent link: https://www.econbiz.de/10012947755
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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