Realized Volatility: A Review

McAleer, Michael; Medeiros, Marcelo C.

doi:10.1080/07474930701853509

Cited by 444 publications

(227 citation statements)

References 121 publications

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“…Whereas pioneering studies in the realized volatility literature recognized the benefits from using high-frequency data in terms of increased accuracy (Merton, 1980;Zhou, 1996), recent work 3 propose several estimators to improve the efficiency, robustness to market microstructure effects, and the ability to estimate the variation due to the continuous part of the price process separately from the variation due to the jump part of the price process. See Andersen et al (2006), McAleer and Medeiros (2008), or BarndorffNielsen and Shephard (2007) for excellent reviews of the realized volatility literature.…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%

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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“…When designing an HFT model, an important challenge that a model designer is faced with is the claim that return autocorrelations in HFT can have both genuine and spurious elements (Anderson, 2011;Anderson et al, 2013). The latter is attributed to market microstructure noise (McAleer and Medeiros, 2008), mainly resulting from non-synchronous trading effect and bid-ask bounce. In view of this, a core consideration in designing HFT models is to manage the tension between moving to higher price frequencies, hoping to benefit from possible price correlations, but at the same time be able to manage the increasing noise levels which give rise to perceived price movements and volatility (see Andersen and Bollerslev, 1997;Rechenthin and Street, 2013).…”

Section: High Frequency Data and Technical Indicatorsmentioning

confidence: 99%

Improving risk-adjusted performance in high frequency trading using interval type-2 fuzzy logic

Vella

2016

Expert Systems with Applications

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Highlights• We investigate the viability of Type-2 fuzzy systems in high frequency trading.• We propose Type-2 models based on a generalisation of the popular ANFIS model.• Type-2 models score significant risk adjusted performance improvements over Type-1.• Benefits of Type-2 models increase with higher trading frequencies. AbstractIn this paper, we investigate the ability of higher order fuzzy systems to handle increased uncertainty, mostly induced by the market microstructure noise inherent in a high frequency trading (HFT) scenario. Whilst many former studies comparing type-1 and type-2 Fuzzy Logic Systems (FLSs) focus on error reduction or market direction accuracy, our interest is predominantly risk-adjusted performance and more in line with both trading practitioners and upcoming regulatory regimes. We propose an innovative approach to design an interval type-2 model which is based on a generalisation of the popular type-1 ANFIS model. The significance of this work stems from the contributions as a result of introducing type-2 fuzzy sets in intelligent trading algorithms, with the objective to improve the risk-adjusted performance with minimal increase in the design and computational complexity. Overall, the proposed ANFIS/T2 model scores significant performance improvements when compared to both standard ANFIS and Buy-and-Hold methods. As a further step, we identify a relationship between the increased trading performance benefits of the proposed type-2 model and higher levels of microstructure noise. The results resolve a desirable need for practitioners, researchers and regulators in the design of expert and intelligent systems for better management of risk in the field of HFT.

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“…A survey of realized variance estimators can be found in Bandi and Russel [4] and McAleer and Medeiros [34].…”

Section: Realized Volatilitymentioning

confidence: 99%

The Testing of Causal Stock Returns-Trading Volume Dependencies with the Aid of Copulas

Gurgul¹,

Mestel²,

Syrek³

2013

manage

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Realized Volatility: A Review

Cited by 444 publications

References 121 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

Improving risk-adjusted performance in high frequency trading using interval type-2 fuzzy logic

The Testing of Causal Stock Returns-Trading Volume Dependencies with the Aid of Copulas

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