Volatilities of different time resolutions — Analyzing the dynamics of market components

Müller, Ulrich; Dacorogna, Michel M.; Davé, Rakhal D.; Olsen, Richard B.; Pictet, Olivier V.; Weizsäcker, Jacob E. von

doi:10.1016/s0927-5398(97)00007-8

Cited by 570 publications

(304 citation statements)

References 15 publications

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“…Müller et al (1997), Arneodo et al (1998) and Lynch and Zumbach. (2003) show that volatility over long time intervals has a strong influence on volatility at shorter time intervals but that volatility over short time intervals does not have an effect on longer intervals.…”

Section: Prediction Modelsmentioning

confidence: 98%

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

Baruník

Křehlík

2014

SSRN Journal

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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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Section: Prediction Modelsmentioning

confidence: 98%

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

Baruník

Křehlík

2014

SSRN Journal

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“…Figure 1: Asymmetric lead-lag correlation of fine and coarse volatilities of a half-hourly USD/DEM series 2nd moment can be derived and to show using a Markov chain approach that the necessary condition presented in (Müller et al, 1995) is also sufficient for the existence of a stationary HARCH(k) process with a finite second moment. This is done first for the HARCH(2) process and then generalized to HARCH(k).…”

Section: Correlationmentioning

confidence: 99%

“…The confidence limits represent the 95% confidence interval of a Gaussian random walk. The HARCH process has been developed for describing the behavior of financial time series, with price quotes from the foreign exchange market being the best-studied example; see (Müller et al, 1995). Financial time series exhibit clusters of high and low volatility, i. e. autoregressive conditional heteroskedasticity.…”

Section: Correlationmentioning

confidence: 99%

“…This synthetic data exhibits the same asymmetric behavior of lead-lag correlation as the empirical USD/DEM data in Figure 1, whereas the other ARCH-type processes lead to symmetric lead-lag correlograms. More details on this study can be found in (Müller et al, 1995).…”

Section: Correlationmentioning

confidence: 99%

“…In (Müller et al, 1995), the Heterogeneous Auto-Regressive Conditional Heteroskedastic process (HARCH) has been introduced. This HARCH process has been developed as an improvement of traditional ARCH-type models in order to describe the behavior of financial time series, including some newly detected properties such as the long memory of volatility and the asymmetry between volatilities with different degrees of resolution in time.…”

Section: Introductionmentioning

confidence: 99%

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How Heavy are the Tails of a Stationary HARCH(k) Process? A Study of the Moments

Embrechts

Samorodnitsky

Dacorogna³

et al. 1998

Stochastic Processes and Related Topics

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Probabilistic properties of HARCH(k) processes, as special stochastic volatility models, are investigated. We present necessary and sufficient conditions for existence of a stationary version of a HARCH(k) process with finite (2£ )th moments, Our approach is based on the general Markov chain techniques of (Meyn and Tweedie, 1990). The conditions are explicit in the case of second moments, and also in the case of 4th moments of the HARCH(2) process.We also deduce explicit necessary and explicit sufficient conditions for higher order moments of general HARCH(k) models. We start by studying the HARCH(2) process (in which case our results are the most explicit) and then generalize the results to a general HARCH(k) process.

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Intra-day volatility components in FTSE-100 stock index futures

2000

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Recent research has suggested that intra‐day volatility may contain both short‐run and long‐run components due to the existence of heterogeneous information flows or heterogeneous market agents (Andersen & Bollerslev, 1997a, 1997b; Müller et al., 1997). We report direct evidence for the existence of such a volatility decomposition in intra‐day UK FTSE‐100 futures returns data at frequencies of one hour and higher using the permanent–transitory component variance model of Engle and Lee (1993). Moreover, the transitory component identified exhibits rapid decay, volatility at the half‐day frequency being completely dominated by the highly persistent permanent component. The model also is able to capture all dependency within the data at frequencies of one hour and lower. © 2000 John Wiley & Sons, Inc. Jrl Fut Mark 20:425–444, 2000

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Volatilities of different time resolutions — Analyzing the dynamics of market components

Cited by 570 publications

References 15 publications

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

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

How Heavy are the Tails of a Stationary HARCH(k) Process? A Study of the Moments

Intra-day volatility components in FTSE-100 stock index futures

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