Forecasting realized volatility using a long-memory stochastic volatility model: estimation, prediction and seasonal adjustment

Deo, Rohit S.; Hurvich, Clifford M.; Lu, Yi

doi:10.1016/j.jeconom.2005.01.003

“…They showed strong empirical evidence in favor of their proposal. Deo et al (2006) considered a long-memory stochastic volatility model and Koopman et al (2005) proposed a model combining unobserved components and longmemory. In a recent work, Hillebrand and Medeiros (2008) suggested a model that combines long memory with different types of nonlinearity.…”

Section: Some Stylized Facts In Financial Time Series and Univariate mentioning

confidence: 99%

Realized Volatility: A Review

McAleer

¹

,

Medeiros

²

2008

Econometric Reviews

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“…The model is estimated using a maximum likelihood method, and forecasting is performed by extrapolating the estimated model. Deo et al (2006), Andersen et al (2003) show that forecasting log realized volatility based on a simple ARFIMA (1, d, 0) specification is a very good competitor to other time-series methods of forecasting realized volatility. We estimate a simple ARFIMA(1, d, 0).…”

Section: Long-memory Autoregressive Fractionally Integrated Moving Avmentioning

confidence: 99%

Coupling High-Frequency Data with Nonlinear Models in Multiple-Step-Ahead Forecasting of Energy Markets' Volatility

Baruník

¹

,

Křehlík

²

2014

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Abstract: Terms of use: Documents inIn the past decade, the popularity of realized measures and various linear models for volatility forecasting has attracted attention in the literature on the price variability of energy markets. However, results that would guide practitioners to a specifc estimator and model when aiming for the best forecasting accuracy are missing. This paper contributes to the ongoing debate with a comprehensive evaluation of multiple-step-ahead volatility forecasts of energy markets using several popular high-frequency measures and forecasting models. To capture the complex patterns hidden to linear models commonly used to forecast realized volatility, this paper also contributes to the literature by coupling realized measures with artificial neural networks as a forecasting tool. Forecasting performance is compared across models as well as realized measures of crude oil, heating oil, and natural gas volatility during three qualitatively distinct periods covering the precrisis period, recent global turmoil of markets in 2008, and the most recent post-crisis period. We conclude that coupling realized measures with arti_cial neural networks results in both statistical and economic gains, reducing the tendency to over-predict volatility uniformly during all tested periods. Our analysis favors the median realized volatility, as it delivers the best performance and is a computationally simple alternative for practitioners.

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“…Note that this discretized multiplicative construction for the volatility was introduced by [3] and frequently used afterward (see, e.g., [10,15,17]). The jumps part was later added by [8].…”

Section: Intraday Seasonality Dynamicsmentioning

confidence: 99%

“…As a piecewise constant function implies that the system under study undergoes structural changes (i.e., shocks) at every break point, it is an unlikely candidate for the market's seasonality. We take the perspective of [15], who use frequency leakage in the power spectrum of the absolute return to argue that the seasonality is not (piecewise) constant but slowly varying. Although they allow non-integer frequencies, their generalized Fourier flexible form parameters are still constant for the whole sample.…”

Section: The Adaptive Volatility Modelmentioning

confidence: 99%

See 1 more Smart Citation

Non-Parametric Estimation of Intraday Spot Volatility: Disentangling Instantaneous Trend and Seasonality

Vatter

¹

,

Wu

²

,

Chavez‐Demoulin

³

et al. 2013

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Abstract: We provide a new framework for modeling trends and periodic patterns in high-frequency financial data. Seeking adaptivity to ever-changing market conditions, we enlarge the Fourier flexible form into a richer functional class: both our smooth trend and the seasonality are non-parametrically time-varying and evolve in real time. We provide the associated estimators and use simulations to show that they behave adequately in the presence of jumps and heteroskedastic and heavy-tailed noise. A study of exchange rate returns sampled from 2010 to 2013 suggests that failing to factor in the seasonality's dynamic properties may lead to misestimation of the intraday spot volatility. Terms of use: Documents in

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Forecasting realized volatility using a long-memory stochastic volatility model: estimation, prediction and seasonal adjustment

Cited by 156 publications

References 10 publications

Realized Volatility: A Review

Realized Volatility: A Review

Coupling High-Frequency Data with Nonlinear Models in Multiple-Step-Ahead Forecasting of Energy Markets' Volatility

Non-Parametric Estimation of Intraday Spot Volatility: Disentangling Instantaneous Trend and Seasonality

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