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2015 | OriginalPaper | Buchkapitel

74. Range Volatility: A Review of Models and Empirical Studies

verfasst von : Ray Yeutien Chou, Hengchih Chou, Nathan Liu

Erschienen in: Handbook of Financial Econometrics and Statistics

Verlag: Springer New York

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Abstract

The literature on range volatility modeling has been rapidly expanding due to its importance and applications. This chapter provides alternative price range estimators and discusses their empirical properties and limitations. Besides, we review some relevant financial applications for range volatility, such as value-at-risk estimation, hedge, spillover effect, portfolio management, and microstructure issues.

In this chapter, we survey the significant development of range-based volatility models, beginning with the simple random walk model up to the conditional autoregressive range (CARR) model. For the extension to range-based multivariate volatilities, some approaches developed recently are adopted, such as the dynamic conditional correlation (DCC) model, the double smooth transition conditional correlation (DSTCC) GARCH model, and the copula method. At last, we introduce different approaches to build bias-adjusted realized range to obtain a more efficient estimator.

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Vorheriges Kapitel Creation and Control of Bubbles: Managers Compensation Schemes, Risk Aversion, and Wealth and Short Sale Constraints

Nächstes Kapitel Business Models: Applications to Capital Budgeting, Equity Value, and Return Attribution

See Garman and Klass (1980), Beckers(1983), Ball and Torous (1984), Wiggins (1991), Rogers and Satchell (1991), Kunitomo (1992), Yang and Zhang (2000), Alizadeh et al. (2002), Brandt and Diebold (2006), Brandt and Jones (2006), Chou (2005, 2006), Cheung (2007), Martens and van Dijk (2007), Chou and Wang (2007), Floros (2009), Chou et al. (2009), and Chou and Liu (2010, 2011).

Please refer to Engle and Russell (1998), Engle and Gallo (2006), and Manganelli (2005).

For asset i, z _i,t ^* = r _i,t/λ _i,t ^* , where λ _i,t ^* = adj _i × λ _i,t and \( ad{j}_i=\frac{{\overline{\sigma}}_i}{\overline{{\widehat{\lambda}}_i}} \). The scaled expected range λ _i,t ^* is computed by a product of λ _i,t and the adjusted coefficient adj _i which is the ratio of the unconditional standard deviations \( {\overline{\sigma}}_i \) for the return series to the sample mean \( \overline{{\widehat{\lambda}}_i} \) of the estimated conditional range.

Silvennoien and Terasvirta (2008, 2009) proposed the smooth transition conditional correlation GARCH (STCC-GARCH) model and the double smooth transition conditional correlation GARCH (DSTCC-GARCH) model.

In some studies, range was used as one of the estimators to improve the explanatory power of models.

In general, it mainly comes from bid-ask bounce and varies with the sampling frequency.

For low-frequency data, Alizadeh et al. (2002) showed that the range estimator is efficient and free of microstructure noise.

The Samuelson (time-to-delivery) effect means that volatility increases when a futures contract approaches its delivery date. Ripple and Moosa (2009) also used the realized range to test the effect of maturity, trading volume, and open interest on crude oil futures. In contrast, Karali and Thurman (2010) just used the daily range to prove the Samuelson effect.

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Titel: Range Volatility: A Review of Models and Empirical Studies
verfasst von: Ray Yeutien Chou
Hengchih Chou
Nathan Liu
Verlag: Springer New York
Buch: Handbook of Financial Econometrics and Statistics
Print ISBN: 978-1-4614-7749-5

Electronic ISBN: 978-1-4614-7750-1

Copyright-Jahr: 2015
DOI: https://doi.org/10.1007/978-1-4614-7750-1_74