Range Volatility: A Review of Models and Empirical Studies

Chou, Ray Y.; Chou, Heng-Chih; Liu, Nathan

doi:10.1007/978-1-4614-7750-1_74

Cited by 24 publications

(26 citation statements)

References 87 publications

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“…Regarding an asset, the range of log prices, R t , is defined as the difference between the highest daily price H t and the lowest daily price L t in a logarithm type, in the trading day t. This may be calculated according to Chou et al (2015): It is worth noticing that different range estimators may be considered as those suggested by Parkinson (1980) or Garman and Klass (1980), which also includes opening and closing prices to estimate range. However, herein range-based volatility, just as in (6) is chosen due to its ability to describe volatility dynamics, as claimed by Christoffersen (2002), and also because this is the same measure used in the CARR model.…”

Section: Range-based Volatility Modelsmentioning

confidence: 99%

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Value-at-risk modeling and forecasting with range-based volatility models: empirical evidence

Maciel

Ballini

2017

Rev. contab. finanç.

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This article considers range-based volatility modeling for identifying and forecasting conditional volatility models based on returns. It suggests the inclusion of range measuring, defined as the difference between the maximum and minimum price of an asset within a time interval, as an exogenous variable in generalized autoregressive conditional heteroscedasticity (GARCH) models. The motivation is evaluating whether range provides additional information to the volatility process (intraday variability) and improves forecasting, when compared to GARCH-type approaches and the conditional autoregressive range (CARR) model. The empirical analysis uses data from the main stock market indexes for the U.S. and Brazilian economies, i.e. S&P 500 and IBOVESPA, respectively, within the period from January 2004 to December 2014. Performance is compared in terms of accuracy, by means of value-at-risk (VaR) modeling and forecasting. The out-of-sample results indicate that range-based volatility models provide more accurate VaR forecasts than GARCH models.

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Section: Range-based Volatility Modelsmentioning

confidence: 99%

“…Over the last decade, there has been considerable growth in the use of range-based volatility models in finance (Chou, Chou, & Liu, 2010;Chou, Chou, & Liu, 2015). However, most of the literature evaluates the models in terms of forecasting accuracy, instead of financial applications using volatility forecasts.…”

Section: Introductionmentioning

confidence: 99%

Value-at-risk modeling and forecasting with range-based volatility models: empirical evidence

Maciel

Ballini

2017

Rev. contab. finanç.

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“…Xie and Wu (2017) proposed a CARR model with gamma disturbance (GCARR) and found that the GCARR model fitted the dynamics of price range better than the commonly used CARR model with Weibull disturbance. For a comprehensive review on range-based volatility, see Chou, Chou, and Liu (2015). Applications of range-based volatility in the empirical literature can be found in Chou and Liu (2010), Miao, Wu, and Su (2013), Xie and Wang (2013), and Dimitrios, Spyros, and Apostolos (2013).…”

Section: Introductionmentioning

confidence: 99%

“…Liu, Wei, Ma, and Wahab (2017) suggested forecasting the realized range-based volatility using the dynamic model average approach. For a comprehensive review on range-based volatility, see Chou et al (2015). For a comprehensive review on range-based volatility, see Chou, Chou, and Liu (2015).…”

Section: Introductionmentioning

confidence: 99%

“…Applications of range-based volatility in the empirical literature can be found in Chou and Liu (2010), Miao, Wu, and Su (2013), Xie and Wang (2013), and Dimitrios, Spyros, and Apostolos (2013). For a comprehensive review on range-based volatility, see Chou, Chou, and Liu (2015). For a comprehensive review on range-based volatility, see Chou et al (2015).…”

Section: Introductionmentioning

confidence: 99%

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Financial volatility modeling: The feedback asymmetric conditional autoregressive range model

Xie

2018

Journal of Forecasting

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An implied assumption in the asymmetric conditional autoregressive range (ACARR) model is that upward range is independent of downward range. This paper scrutinizes this assumption on a broad variety of stock indices. Instead of independence, we find significant cross‐interdependence between the upward range and the downward range. Regression test shows that the cross‐interdependence cannot be explained by leverage effect. To include the cross‐interdependence, a feedback asymmetric conditional autoregressive range (FACARR) model is proposed. Empirical studies are performed on a variety of stock indices, and the results show that the FACARR model outperforms the ACARR model with high significance for both in‐sample and out‐of‐sample forecasting.

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A three‐dimensional asymmetric power HEAVY model

et al. 2020

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This article proposes the three-dimensional HEAVY system of daily, intradaily, and range-based volatility equations. We augment the bivariate model with a third volatility metric, the Garman-Klass estimator, and enrich the trivariate system with power transformations and asymmetries. Most importantly, we derive the theoretical properties of the multivariate asymmetric power model and explore its finite-sample performance through a simulation experiment on the size and power properties of the diagnostic tests employed.

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Range Volatility: A Review of Models and Empirical Studies

Cited by 24 publications

References 87 publications

Value-at-risk modeling and forecasting with range-based volatility models: empirical evidence

Value-at-risk modeling and forecasting with range-based volatility models: empirical evidence

Financial volatility modeling: The feedback asymmetric conditional autoregressive range model

A three‐dimensional asymmetric power HEAVY model

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