Volatility Forecasting With Range-Based EGARCH Models

Brandt, Michael W.; Jones, Christopher S.

doi:10.1198/073500106000000206

Cited by 231 publications

(130 citation statements)

References 30 publications

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“…The focus should be put on the relationship between short-term conditional volatility and range volatility. A starting point could be the range volatility-based GARCH models such as the E-GARCH model used in Brandt and Jones (2006) and the conditional autoregressive range model used in Chiang and Wang (2011). In any case, the volatility spillover indices are a useful addition to the hitherto GARCH-centered analysis of volatility relationships.…”

Section: Discussionmentioning

confidence: 99%

Directional Volatility Spillovers Between Agricultural, Crude Oil, Real Estate, and Other Financial Markets

Grosche

Heckelei

2016

Food Price Volatility and Its Implications for Food Security and Policy

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Section: Discussionmentioning

confidence: 99%

Directional Volatility Spillovers Between Agricultural, Crude Oil, Real Estate, and Other Financial Markets

Grosche

Heckelei

2016

Food Price Volatility and Its Implications for Food Security and Policy

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“…the difference between the highest and the lowest price within a day, has also been found to be significant by many authors, see e.g. Brandt and Jones (2006) and Engle and Gallo (2006). Recently, Barndorff-Nielsen et al (2010) proposed the realized semivariance as the sum of square negative returns to capture the impact on volatility of downward price pressures.…”

Section: Robustness To Other Volatility Measuresmentioning

confidence: 99%

Discrete-Time Volatility Forecasting With Persistent Leverage Effect and the Link With Continuous-Time Volatility Modeling

Corsi

Renò

2012

Journal of Business & Economic Statistics

274

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Citation: Corsi, F. and Reno, R. (2012). Discrete-time volatility forecasting with persistent leverage effect and the link with continuous-time volatility modeling. Journal of Business and Economic Statistics, 30(3), pp. 368-380. doi: 10.1080/07350015.2012.663261 This is the accepted version of the paper.This version of the publication may differ from the final published version. Abstract We first propose a reduced form model in discrete time for S&P500 volatility showing that the forecasting performance can be significantly improved by introducing a persistent leverage effect with a long-range dependence similar to that of volatility itself. We also find a strongly significant positive impact of lagged jumps on volatility, which however is absorbed more quickly. We then estimate continuous-time stochastic volatility models which are able to reproduce the statistical features captured by the discrete-time model. We show that a single-factor model driven by a fractional Brownian motion is unable to reproduce the volatility dynamics observed in the data, while a multi-factor Markovian model fully replicates the persistence of both volatility and leverage effect. The impact of jumps can be associated with a common jump component in price and volatility. Permanent

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“…Gallant et al (1999) and Alizadeh, Brandt and Diebold (2002) incorporate the range into the stochastic volatility model. 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%

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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Volatility Forecasting With Range-Based EGARCH Models

Cited by 231 publications

References 30 publications

Directional Volatility Spillovers Between Agricultural, Crude Oil, Real Estate, and Other Financial Markets

Directional Volatility Spillovers Between Agricultural, Crude Oil, Real Estate, and Other Financial Markets

Discrete-Time Volatility Forecasting With Persistent Leverage Effect and the Link With Continuous-Time Volatility Modeling

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

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