Incorporating higher moments into value‐at‐risk forecasting

Polanski, Arnold; Stoja, Evarist

doi:10.1002/for.1155

Cited by 33 publications

(23 citation statements)

References 22 publications

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“…These fi ndings confi rm the importance of (even) higher co-moments in the density forecasting of fi nancial data, as observed previously for unidimensional variables (Guermat and Harris, 2002;Polanski and Stoja, 2009a). For example, for nominal α = 1% and 2%, the frequency of observations are %x = 2.4% and 3.7%, respectively.…”

Section: Out-of-sample Density Forecasting: Accuracysupporting

confidence: 86%

Dynamic Density Forecasts for Multivariate Asset Returns

Polanski

Stoja

2009

SSRN Journal

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We propose a simple and flexible framework for forecasting the joint density of asset returns. The multinormal distribution is augmented with a polynomial in (time‐varying) non‐central co‐moments of assets. We estimate the coefficients of the polynomial via the method of moments for a carefully selected set of co‐moments. In an extensive empirical study, we compare the proposed model with a range of other models widely used in the literature. Employing a recently proposed as well as standard techniques to evaluate multivariate forecasts, we conclude that the augmented joint density provides highly accurate forecasts of the ‘negative tail’ of the joint distribution. Copyright © 2010 John Wiley & Sons, Ltd.

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Section: Out-of-sample Density Forecasting: Accuracysupporting

confidence: 86%

Dynamic Density Forecasts for Multivariate Asset Returns

Polanski

Stoja

2009

SSRN Journal

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“…From this analysis, we can conclude that the fat-tailed and skewness distributions for conditional volatility outperform symmetric distribution in forecasting VaR. This result is in line with those presented by Xu and Wirjanto [8], Polanski and Stoja [9] and Chen et al [10]. The nature of the business models of these subsectors is different, each one presenting very different risk profiles.…”

Section: B the Value At Risk Of The Portfolio Representativesupporting

confidence: 79%

Differences in Measuring Market Risk in Four Subsectors of the Digital Economy

Muela¹,

Juan²,

Gómez³

et al. 2015

IJIMAI

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-This paper defends the wisdom of not considering the Digital Economy to be one homogeneous sector. Our hypothesis is that it is best to consider it the result of adding four different subsectors. We test whether indeed the economic and financial performance of a portfolio of listed companies in each of the four subsectors presents relevant differences. We use the value at risk measure to estimate market risk of the four subsectors of the digital economy. The riskiest subsector is Mobile/Internet Contents & Services followed by SW&IT Services and Application Software. On the contrary, the Telecom sector is by far the safest one. These results support the hypothesis that the Digital Economy is not a homogeneous sector.

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“…The next stage is to introduce more complex models of conditional variance and additional assumptions for the conditional distribution of residuals, assuming statistical significance of higher moments of random variables represented by returns observed on the metal market [Orhan, Koksal 2012;Polański, Stoja 2010].…”

Section: Discussionmentioning

confidence: 99%

Volatility and risk models on the metal market

Krężołek¹

2015

Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu

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Incorporating higher moments into value‐at‐risk forecasting

Cited by 33 publications

References 22 publications

Dynamic Density Forecasts for Multivariate Asset Returns

Dynamic Density Forecasts for Multivariate Asset Returns

Differences in Measuring Market Risk in Four Subsectors of the Digital Economy

Volatility and risk models on the metal market

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