Volatility forecasting for risk management

Brooks, Chris; Persand, Gita

doi:10.1002/for.841

Cited by 141 publications

(85 citation statements)

References 23 publications

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“…As suggested by Brooks and Persand (2003), loss functions based on Value-at-Risk are a natural alternative to the standard statistical loss functions while evaluating the predictive performance of a model estimated on financial data. The VaR measures the market risk of a portfolio quantified in monetary terms, and arising from market fluctuations at a given significance level.…”

Section: Value-at-riskmentioning

confidence: 99%

Volatility forecasting for crude oil futures

Marzo

Zagaglia

2010

Applied Economics Letters

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This paper studies the forecasting properties of linear GARCH models for closing-day futures prices on crude oil, first position, traded in the New York Mercantile Exchange from January 1995 to November 2005. In order to account for fat tails in the empirical distribution of the series, we compare models based on the normal, Student's t and Generalized Exponential distribution. We focus on out-of-sample predictability by ranking the models according to a large array of statistical loss functions. The results from the tests for predictive ability show that the GARCH-G model fares best for short horizons from one to three days ahead. For horizons from one week ahead, no superior model can be identified. We also consider out-ofsample loss functions based on Value-at-Risk that mimic portfolio managers and regulators' preferences. EGARCH models display the best performance in this case.

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Section: Value-at-riskmentioning

confidence: 99%

Volatility forecasting for crude oil futures

Marzo

Zagaglia

2010

Applied Economics Letters

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“…There are several ways of calculating the VaR of a portfolio, but here we mention only the most popular, which is termed as the variance-covariance approach and it is due to Morgan (1996). The VaR of a portfolio has a single value (under a specific model), which according to Brooks and Persand (2003) is…”

Section: Model Diagnostics and Model Comparisonmentioning

confidence: 99%

Multivariate stochastic volatility with Bayesian dynamic linear models

Triantafyllopoulos

2008

Journal of Statistical Planning and Inference

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This paper develops a Bayesian procedure for estimation and forecasting of the volatility of multivariate time series. The foundation of this work is the matrix-variate dynamic linear model, for the volatility of which we adopt a multiplicative stochastic evolution, using Wishart and singular multivariate beta distributions. A diagonal matrix of discount factors is employed in order to discount the variances element by element and therefore allowing a flexible and pragmatic variance modelling approach. Diagnostic tests and sequential model monitoring are discussed in some detail. The proposed estimation theory is applied to a four-dimensional time series, comprising spot prices of aluminium, copper, lead and zinc of the London metal exchange. The empirical findings suggest that the proposed Bayesian procedure can be effectively applied to financial data, overcoming many of the disadvantages of existing volatility models.

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“…Furthermore, a Monte Carlo simulation has been shown to produce useful estimates of intraday VaR using tick-by-tick data (Dionne et al, 2009;Brooks and Persand, 2003).…”

Section: Introduction -Motivation and Review Of Literaturementioning

confidence: 99%

Forecasting value-at-risk and expected shortfall using fractionally integrated models of conditional volatility: International evidence

Degiannakis

Floros

Dent

2013

International Review of Financial Analysis

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The present study compares the performance of the long memory FIGARCH model, with that of the short memory GARCH specification, in the forecasting of multi-period ahead, 10-day-ahead and 20-day-ahead forecasting horizons relative to the short memory GARCH specification. Additionally, the results suggest that underestimation of the true VaR figure becomes less prevalent as the forecasting horizon increases. Furthermore, the GARCH model has a lower quadratic loss between actual returns and ES forecasts, for the majority of the indices considered for the 10-day and 20-day forecasting horizons. Therefore, a long memory volatility model compared to a short memory GARCH model does not appear to improve the VaR and ES forecasting accuracy, even for longer forecasting horizons. Finally, the rolling-sampled estimated FIGARCH parameters change less smoothly over time compared to the GARCH models. Hence, the parameters' time-variant characteristic cannot be entirely due to the news information arrival process of the market; a portion must be due to the FIGARCH modelling process itself.

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Volatility forecasting for risk management

Cited by 141 publications

References 23 publications

Volatility forecasting for crude oil futures

Volatility forecasting for crude oil futures

Multivariate stochastic volatility with Bayesian dynamic linear models

Forecasting value-at-risk and expected shortfall using fractionally integrated models of conditional volatility: International evidence

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