Forecasting Financial Volatilities with Extreme Values: The Conditional Autoregressive Range (CARR) Model

Chou, Ray Y.

doi:10.1353/mcb.2005.0027

Cited by 320 publications

(270 citation statements)

References 49 publications

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“…Brandt and Jones (2006) proposed a range-based EGARCH model, using a link between the range and intra-day volatility, showing that the their model had favourable out-of-sample volatility forecasting performance. Chou (2005) proposes the Conditional Autoregressive Range (CARR) model for the high and low range of asset prices.…”

Section: Proposed Range-based Caviar Modelsmentioning

confidence: 99%

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Forecasting Value-at-Risk using nonlinear regression quantiles and the intra-day range

Chen

Gerlach

Hwang

et al. 2012

International Journal of Forecasting

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Value-at-Risk (VaR) is commonly used for financial risk measurement. It has recently become even more important, especially during the 2008-09 global financial crisis. We propose some novel nonlinear threshold conditional autoregressive VaR (CAViaR) models that incorporate intra-day price ranges. Model estimation and inference are performed using the Bayesian approach via the link with the Skewed-Laplace distribution. We examine how a range of risk models perform during the 2008-09 financial crisis, and evaluate how the crisis affects the performance of risk models via forecasting VaR. Empirical analysis is conducted on five Asia-Pacific Economic Cooperation stock market indices as well as two exchange rate series. We examine violation rates, back-testing criteria, market risk charges and quantile loss function values to measure and assess the forecasting performance of a variety of risk models. The proposed threshold CAViaR model, incorporating range information, is shown to forecast VaR more efficiently than other models, across the series considered, which should be useful for financial practitioners.

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Section: Proposed Range-based Caviar Modelsmentioning

confidence: 99%

“…In the same spirit as Chou (2005) and Chen et al (2008), we extend the CAViaR models in (2), (4), (5) and incorporate the intra-day high-low price range into the following models:…”

Section: Proposed Range-based Caviar Modelsmentioning

confidence: 99%

Forecasting Value-at-Risk using nonlinear regression quantiles and the intra-day range

Chen

Gerlach

Hwang

et al. 2012

International Journal of Forecasting

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“…In all cases, the additional parameter ς in the Weibull distribution shows statistically significant departures from the value of 1 implied by the exponential distribution. The values of ς are close to (or slightly larger than) those in the CARR model of Chou (2005) for high/low ranges of asset prices; the values of ς in ACD applications are typically smaller than 1. The dispersion test signals no underdispersion, but the Pearson test still indicates inappropriateness of the Weibull density.…”

Section: Gnp Growthmentioning

confidence: 57%

Dynamic modeling under linear-exponential loss

Anatolyev

2009

Economic Modelling

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We develop a methodology of parametric modeling of time series dynamics when the underlying loss function is linear-exponential (Linex). We propose to directly model the dynamics of the conditional expectation that determines the optimal predictor. The procedure hinges on the exponential quasi maximum likelihood interpretation of the Linex loss and nicely fits the multiple error modeling framework. Many conclusions relating to estimation, inference and forecasting follow from results already available in the econometric literature. The methodology is illustrated using data on United States GNP growth and Treasury bill returns.

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“…The MEM is a generalization of the ACD model (Engle and Russell (1998)); other examples of MEMs are the CARR model by Chou (2005) or the model by Manganelli (2005). Engle and Gallo (2006) have specified a simultaneous MEM where three different measures of volatility (absolute returns, daily range and realized volatility) are jointly modelled introducing lagged values of all variables in each equation for the conditional expectations.…”

Section: Introductionmentioning

confidence: 99%

Semiparametric Vector Mem

Cipollini

Engle

Gallo

2012

J of Applied Econometrics

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In financial time series analysis we encounter several instances of non-negative valued processes (volumes, trades, durations, realized volatility, daily range, and so on) which exhibit clustering and can be modeled as the product of a vector of conditionally autoregressive scale factors and a multivariate iid innovation process (vector Multiplicative Error Model).Two novel points are introduced in this paper relative to previous suggestions: a more general specification which sets this vector MEM apart from an equation by equation specification; and the adoption of a GMM-based approach which bypasses the complicated issue of specifying a general multivariate non-negative valued innovation process.A vMEM for volumes, number of trades and realized volatility reveals empirical support for a dynamically interdependent pattern of relationships among the variables on a number of NYSE stocks. * This paper develops some ideas introduced in Cipollini, Engle and Gallo (2006) where estimation was based in the framework of estimating functions. Without implicating, we acknowledge comments by Nour Meddahi and Kevin Sheppard which led us to present the Estimating Functions approach in a more familiar GMM notation. We acknowledge financial support from the Italian MIUR under grant PRIN 2006131140 004.

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Forecasting Financial Volatilities with Extreme Values: The Conditional Autoregressive Range (CARR) Model

Cited by 320 publications

References 49 publications

Forecasting Value-at-Risk using nonlinear regression quantiles and the intra-day range

Forecasting Value-at-Risk using nonlinear regression quantiles and the intra-day range

Dynamic modeling under linear-exponential loss

Semiparametric Vector Mem

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