On the Estimation of Security Price Volatilities from Historical Data

Garman, Mark B.; Klass, Michael J.

doi:10.1086/296072

Cited by 1,260 publications

(847 citation statements)

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“…see Parkinson, 1980). Mandelbrot (1971) proposes the range to evaluate the existence of long-term dependence on asset prices; Garman and Klass (1980) show that high-low price-range data contain more information regarding volatility than opening to closing prices. Beckers (1983) applies the range estimator to incorporate past information for different variance measures.…”

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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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%

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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“…Variance decomposition analysis of the VAR model allows us to identify spillovers of return and volatility shocks from the indigenous shocks. In order to measure volatility we use efficient range-based volatility estimate that was first proposed by Garman and Klass (1980).…”

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confidence: 99%

Return and volatility spillovers among the East Asian equity markets

Mody

2010

Journal of Asian Economics

222

109

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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. Terms of use: Documents in AbstractThis article examines the extent of contagion and interdependence across the East Asian equity markets since early 1990s and compares the ongoing crisis with earlier episodes. Using the forecast error variance decomposition from a vector autoregression, we derive return and volatility spillover indices over the rolling sub-sample windows. We show that there is substantial difference between the behavior of the East Asian return and volatility spillover indices over time. While the return spillover index reveals increased integration among the East Asian equity markets, the volatility spillover index experiences significant bursts during major market crises, including the East Asian crisis. The fact that both return and volatility spillover indices reached their respective peaks during the current global financial crisis attests to the severity of the current episode.

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“…"Stochastic volatility" models (Barndorff-Nielsen et al [26], Chib et al [75], Ghysels et al [145], Harvey and Shephard [163], Jacquier et al [174], Shephard [280], Taylor [288]), "implied volatility" models (Day and Lewis [88], Latane and Rendleman [195], Schmalensee and Trippi [272]), "historical volatility" models (Beckers [30], Garman and Klass [140], Kunitomo [190], Parkinson [263], Rogers and Satchell [269]) and "realized volatility" models are examples from the financial econometric literature of estimating volatility of asset returns.…”

Section: Other Methods Of Volatility Modelingmentioning

confidence: 99%

Autoregressive Conditional Heteroscedasticity (ARCH) Models: A Review

Degiannakis

Xekalaki

2004

Quality Technology & Quantitative Management

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Autoregressive Conditional Heteroscedasticity (ARCH) models have successfully been employed in order to predict asset return volatility. Predicting volatility is of great importance in pricing financial derivatives, selecting portfolios, measuring and managing investment risk more accurately. In this paper, a number of univariate and multivariate ARCH models, their estimating methods and the characteristics of financial time series, which are captured by volatility models, are presented. The number of possible conditional volatility formulations is vast. Therefore, a systematic presentation of the models that have been considered in the ARCH literature can be useful in guiding one's choice of a model for exploiting future volatility, with applications in financial markets.

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On the Estimation of Security Price Volatilities from Historical Data

Cited by 1,260 publications

References 6 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

Return and volatility spillovers among the East Asian equity markets

Autoregressive Conditional Heteroscedasticity (ARCH) Models: A Review

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