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Article
Publication date: 1 May 2006

Chu‐Hsiung Lin and Shan‐Shan Shen

This paper aims to investigate how effectively the value at risk (VaR) estimated using the student‐t distribution captures the market risk.

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Abstract

Purpose

This paper aims to investigate how effectively the value at risk (VaR) estimated using the student‐t distribution captures the market risk.

Design/methodology/approach

Two alternative VaR models, VaRt and VaR‐x models, are presented and compared with the benchmark model (VaR‐n model). In this study, we consider the Student‐t distribution as a fit to the empirical distribution for estimating the VaR measure, namely, VaRt method. Since the Student‐t distribution is criticized for its inability to capture the asymmetry of distribution of asset returns, we use the extreme value theory (EVT)‐based model, VaR‐x model, to take into account the asymmetry of distribution of asset returns. In addition, two different approaches, excess‐kurtosis and tail‐index techniques, for determining the degrees of freedom of the Student‐t distribution in VaR estimation are introduced.

Findings

The main finding of the study is that using the student‐t distribution for estimating VaR can improve the VaR estimation and offer accurate VaR estimates, particularly when tail index technique is used to determine the degrees of freedom and the confidence level exceeds 98.5 percent.

Originality/value

The main value is to demonstrate in detail how well the student‐t distribution behaves in estimating VaR measure for stock market index. Moreover, this study illustrates the easy process for determining the degrees of freedom of the student‐t, which is required in VaR estimation.

Details

The Journal of Risk Finance, vol. 7 no. 3
Type: Research Article
ISSN: 1526-5943

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Book part
Publication date: 15 April 2020

Cindy S. H. Wang and Shui Ki Wan

This chapter extends the univariate forecasting method proposed by Wang, Luc, and Hsiao (2013) to forecast the multivariate long memory model subject to structural breaks. The…

Abstract

This chapter extends the univariate forecasting method proposed by Wang, Luc, and Hsiao (2013) to forecast the multivariate long memory model subject to structural breaks. The approach does not need to estimate the parameters of this multivariate system nor need to detect the structural breaks. The only procedure is to employ a VAR(k) model to approximate the multivariate long memory model subject to structural breaks. Therefore, this approach reduces the computational burden substantially and also avoids estimation of the parameters of the multivariate long memory model, which can lead to poor forecasting performance. Moreover, when there are multiple breaks, when the breaks occur close to the end of the sample or when the breaks occur at different locations for the time series in the system, our VAR approximation approach solves the issue of spurious breaks in finite samples, even though the exact orders of the multivariate long memory process are unknown. Insights from our theoretical analysis are confirmed by a set of Monte Carlo experiments, through which we demonstrate that our approach provides a substantial improvement over existing multivariate prediction methods. Finally, an empirical application to the multivariate realized volatility illustrates the usefulness of our forecasting procedure.

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Book part
Publication date: 13 December 2013

Todd E. Clark and Michael W. McCracken

This article surveys recent developments in the evaluation of point and density forecasts in the context of forecasts made by vector autoregressions. Specific emphasis is placed…

Abstract

This article surveys recent developments in the evaluation of point and density forecasts in the context of forecasts made by vector autoregressions. Specific emphasis is placed on highlighting those parts of the existing literature that are applicable to direct multistep forecasts and those parts that are applicable to iterated multistep forecasts. This literature includes advancements in the evaluation of forecasts in population (based on true, unknown model coefficients) and the evaluation of forecasts in the finite sample (based on estimated model coefficients). The article then examines in Monte Carlo experiments the finite-sample properties of some tests of equal forecast accuracy, focusing on the comparison of VAR forecasts to AR forecasts. These experiments show the tests to behave as should be expected given the theory. For example, using critical values obtained by bootstrap methods, tests of equal accuracy in population have empirical size about equal to nominal size.

Details

VAR Models in Macroeconomics – New Developments and Applications: Essays in Honor of Christopher A. Sims
Type: Book
ISBN: 978-1-78190-752-8

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Article
Publication date: 2 October 2017

Dilip Kumar and Srinivasan Maheswaran

This paper aims to propose a framework based on the unbiased extreme value volatility estimator (namely, the AddRS estimator) to compute and predict the long position and the…

275

Abstract

Purpose

This paper aims to propose a framework based on the unbiased extreme value volatility estimator (namely, the AddRS estimator) to compute and predict the long position and the short position value-at-risk (VaR) and stressed expected shortfall (ES). The precise prediction of VaR and ES measures has important implications toward financial institutions, fund managers, portfolio managers, regulators and business practitioners.

Design/methodology/approach

The proposed framework is based on the Giot and Laurent (2004) approach and incorporates characteristics like long memory, fat tails and skewness. The authors evaluate its VaR and ES forecasting performance using various backtesting approaches for both long and short positions on four global indices (S&P 500, CAC 40, Indice BOVESPA [IBOVESPA] and S&P CNX Nifty) and compare the results with that of various alternative models.

Findings

The findings indicate that the proposed framework outperforms the alternative models in predicting the long and the short position VaR and stressed ES. The findings also indicate that the VaR forecasts based on the proposed framework provide the least total loss for various long and short position VaR, and this supports the superior properties of the proposed framework in forecasting VaR more accurately.

Originality/value

The study contributes by providing a framework to predict more accurate VaR and stressed ES measures based on the unbiased extreme value volatility estimator.

Details

Studies in Economics and Finance, vol. 34 no. 4
Type: Research Article
ISSN: 1086-7376

Keywords

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Book part
Publication date: 28 October 2019

Angelo Corelli

Abstract

Details

Understanding Financial Risk Management, Second Edition
Type: Book
ISBN: 978-1-78973-794-3

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Book part
Publication date: 25 February 2016

Johannes Ludwig

Contrary to the implications of economic theory, consumption inequality in the United States did not react to the increases in income inequality during the last three decades…

Abstract

Contrary to the implications of economic theory, consumption inequality in the United States did not react to the increases in income inequality during the last three decades. This paper investigates if a change in the type of income inequality – from permanent to transitory – or a change in the ability to insure income shocks is responsible for this. A measure of household consumption is imputed into the Panel Study of Income Dynamics to create panel data on income and consumption for the period 1980–2010. The minimum distance investigation of covariance relationships shows that both explanations work together: the share of transitory shocks increases over time, but the capability to insure against permanent and transitory income shocks also improves. Together, these phenomena can explain the lack of an increase in consumption inequality.

Details

Inequality: Causes and Consequences
Type: Book
ISBN: 978-1-78560-810-0

Keywords

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Book part
Publication date: 9 September 2020

Ying L. Becker, Lin Guo and Odilbek Nurmamatov

Value at risk (VaR) and expected shortfall (ES) are popular market risk measurements. The former is not coherent but robust, whereas the latter is coherent but less interpretable…

Abstract

Value at risk (VaR) and expected shortfall (ES) are popular market risk measurements. The former is not coherent but robust, whereas the latter is coherent but less interpretable, only conditionally backtestable and less robust. In this chapter, we compare an innovative artificial neural network (ANN) model with a time series model in the context of forecasting VaR and ES of the univariate time series of four asset classes: US large capitalization equity index, European large cap equity index, US bond index, and US dollar versus euro exchange rate price index for the period of January 4, 1999, to December 31, 2018. In general, the ANN model has more favorable backtesting results as compared to the autoregressive moving average, generalized autoregressive conditional heteroscedasticity (ARMA-GARCH) time series model. In terms of forecasting accuracy, the ANN model has much fewer in-sample and out-of-sample exceptions than those of the ARMA-GARCH model.

Details

Advances in Pacific Basin Business, Economics and Finance
Type: Book
ISBN: 978-1-83867-363-5

Keywords

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Article
Publication date: 28 January 2014

Harald Kinateder and Niklas Wagner

– The paper aims to model multiple-period market risk forecasts under long memory persistence in market volatility.

760

Abstract

Purpose

The paper aims to model multiple-period market risk forecasts under long memory persistence in market volatility.

Design/methodology/approach

The paper proposes volatility forecasts based on a combination of the GARCH(1,1)-model with potentially fat-tailed and skewed innovations and a long memory specification of the slowly declining influence of past volatility shocks. As the square-root-of-time rule is known to be mis-specified, the GARCH setting of Drost and Nijman is used as benchmark model. The empirical study of equity market risk is based on daily returns during the period January 1975 to December 2010. The out-of-sample accuracy of VaR predictions is studied for 5, 10, 20 and 60 trading days.

Findings

The long memory scaling approach remarkably improves VaR forecasts for the longer horizons. This result is only in part due to higher predicted risk levels. Ex post calibration to equal unconditional VaR levels illustrates that the approach also enhances efficiency in allocating VaR capital through time.

Practical implications

The improved VaR forecasts show that one should account for long memory when calibrating risk models.

Originality/value

The paper models single-period returns rather than choosing the simpler approach of modeling lower-frequency multiple-period returns for long-run volatility forecasting. The approach considers long memory in volatility and has two main advantages: it yields a consistent set of volatility predictions for various horizons and VaR forecasting accuracy is improved.

Details

The Journal of Risk Finance, vol. 15 no. 1
Type: Research Article
ISSN: 1526-5943

Keywords

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Article
Publication date: 19 June 2018

Jay Junghun Lee

Prior literature suggests that stock prices lead earnings in reflecting value-relevant information because accounting income incorporates information discretely to satisfy…

481

Abstract

Purpose

Prior literature suggests that stock prices lead earnings in reflecting value-relevant information because accounting income incorporates information discretely to satisfy recognition principles while stock prices incorporate it continuously. The purpose of this paper is to derive an analytical model that relates the time lag of earnings to the incremental informativeness of future anticipated earnings in equity prices after controlling for current realized earnings.

Design/methodology/approach

This study models the extent to which forward-looking information about future earnings is capitalized into current stock returns. Specifically, this study derives an analytical future earnings response coefficient (FERC) model that regresses current stock returns on both current and future earnings surprises, and examines the properties of the regression coefficients on current earnings (i.e. current earnings response coefficient, CERC) and future earnings (i.e. FERC).

Findings

The analytical FERC model shows that the pricing coefficient on future earnings (FERC) is positive in the presence of stock prices leading earnings. More importantly, the pricing coefficient on future earnings (FERC) increases with the recognition lag, but the pricing coefficient on current earnings (CERC) decreases with the lag. The results suggest that recognition principles that intend to enhance the reliability of earnings inadvertently lower the timeliness of earnings and, thus, shift the investors’ demand for value-relevant information from current realized earnings to future anticipated earnings.

Originality/value

This study makes two major contributions. First, it fills the gap between the lack of an analytical model and the abundance of empirical findings in previous FERC studies. As the recognition lag of earnings increases, stock investors shift the pricing weight on value-relevant information from current realized earnings to future anticipated earnings. Second, it provides support for the validity of the FERC model as an empirical model that examines the lack of earnings timeliness. As the timeliness of earnings relative to stock prices declines, the FERC increases but the CERC decreases.

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Book part
Publication date: 27 May 2024

Angelo Corelli

Abstract

Details

Understanding Financial Risk Management, Third Edition
Type: Book
ISBN: 978-1-83753-253-7

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