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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/10011340612
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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; Tran, Minh-Ngoc - 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/10014331159
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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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Censored Posterior and Predictive Likelihood in Bayesian Left-Tail Prediction for Accurate Value at Risk Estimation
Gatarek, Lukasz; Hoogerheide, Lennart; Hooning, Koen; … - 2013
Accurate prediction of risk measures such as Value at Risk (VaR) and Expected Shortfall (ES) requires precise … posterior and traditional Bayesian Model Averaging techniques in applications of Value-at-Risk prediction in GARCH models. …
Persistent link: https://www.econbiz.de/10010326148
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Predicting Time-Varying Parameters with Parameter-Driven and Observation-Driven Models
Koopman, Siem Jan; Lucas, Andre; Scharth, Marcel - 2012
autoregressive score (GAS) models have similar predictive accuracy to correctly specified parameter-driven models. In most cases … alternatives. We also find that GAS models outperform many familiar observation-driven models in terms of forecasting accuracy. The …
Persistent link: https://www.econbiz.de/10010326198
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Forecasting Volatility with Copula-Based Time Series Models
Sokolinskiy, Oleg; van Dijk, Dick - 2011
This paper develops a novel approach to modeling and forecasting realized volatility (RV) measures based on copula functions. Copula-based time series models can capture relevant characteristics of volatility such as nonlinear dynamics and long-memory type behavior in a flexible yet parsimonious...
Persistent link: https://www.econbiz.de/10010326314
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Empirical evidence on the importance of aggregation, asymmetry, and jumps for volatility prediction
Duong, Diep; Swanson, Norman R. - 2013
Many recent modelling advances in finance topics ranging from the pricing of volatility-based derivative products to asset management are predicated on the importance of jumps, or discontinuous movements in asset returns. In light of this, a number of recent papers have addressed volatility...
Persistent link: https://www.econbiz.de/10010334248
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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/10011651975
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Forecasting the Volatility of Nikkei 225 Futures
Asai, Manabu; McAleer, Michael - 2017
For forecasting volatility of futures returns, the paper proposes an indirect method based on the relationship between futures and the underlying asset for the returns and time-varying volatility. For volatility forecasting, the paper considers the stochastic volatility model with asymmetry and...
Persistent link: https://www.econbiz.de/10011662515