Comparing the accuracy of multivariate density forecasts in selected regions of the copula support

Diks, Cees; Panchenko, Valentyn; Sokolinskiy, Oleg; Dijk, Dick van

doi:10.1016/j.jedc.2014.08.021

Cited by 22 publications

(7 citation statements)

References 23 publications

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“…Giot and Laurent (2003) showed that a model with skew t innovations outperforms those with symmetric distributions in VaR estimation; Huang et al (2015) suggested that better portfolio performance is achieved with a time-varying copula, particularly the Clayton copula. Diks et al (2010) and Diks et al (2014) found that for daily exchange rate returns, the t copula is favored over its counterparts, while in the government bond market, a mixture of t and Clayton copulas performs best for the daily changes of yields on government bonds of the G7 countries.…”

Section: Introductionmentioning

confidence: 99%

Comparing density forecasts in a risk management context

Diks

Fang

2020

International Journal of Forecasting

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We compare multivariate and univariate approaches to assessing the accuracy of competing density forecasts of a portfolio return in the downside part of the support. We argue that the common practice to perform multivariate forecast comparisons can be problematic in the context of assessing portfolio risk, since better multivariate forecasts do not necessarily correspond to better aggregate portfolio return forecasts. This is illustrated by examples involving (skew) elliptical distributions and an application to daily returns of a number of US stock prices. Additionally, time-varying test statistics and Value-at-Risk forecasts provide empirical evidence for regime changes over the last decades.

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Section: Introductionmentioning

confidence: 99%

Comparing density forecasts in a risk management context

Diks

Fang

2020

International Journal of Forecasting

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“…This underlines that we are really comparing the forecasting quality of the copula part. Second, we focus the evaluation on the joint lower region of the copula support by using the conditional likelihood (cl) scoring rule proposed by Diks et al (2014),…”

Section: Multivariate Density Forecastsmentioning

confidence: 99%

Closed-Form Multi-Factor Copula Models with Observation-Driven Dynamic Factor Loadings

et al. 2019

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We develop new multi-factor dynamic copula models with time-varying factor loadings and observation-driven dynamics. The new models are highly flexible, scalable to high dimensions, and ensure positivity of covariance and correlation matrices. A closed-form likelihood expression allows for straightforward parameter estimation and likelihood inference. We apply the new model to a large panel of 100 U.S. stocks over the period 2001-2014. The proposed multi-factor structure is much better than existing (singlefactor) models at describing stock return dependence dynamics in high-dimensions. The new factor models also improve one-step-ahead copula density forecasts and global minimum variance portfolio performance. Finally, we investigate different mechanisms to allocate firms into groups and find that a simple industry classification outperforms alternatives based on observable risk factors, such as size, value or momentum.

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“…This issue is however important and non-trivial, with an ongoing strand of research on copula selection and goodness-of-fit testing (see, e.g., Diks, Panchenko, and van Dijk (2010) and Diks et al (2014) that optimizes copula choice using out-of-sample forecasting criterion) and we refer the reader to these related literature.…”

Section: Copula Choicementioning

confidence: 99%

Estimating dynamic copula dependence using intraday data

Grossmass

Poon

2014

Studies in Nonlinear Dynamics &Amp; Econometrics

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Abstract:We estimate the dynamic daily dependence between assets by applying the Semiparametric Copula-Based Multivariate Dynamic (SCOMDY) model on intraday data. Using tick data of three stock returns of the period before and during the credit crisis, we find that our dependence estimator better captures the steep increase in dependence during the onset of the crisis as compared to other commonly used time-varying copula methods. Like other high-frequency estimators, we find that the dependence estimator exhibits long memory and forecast it using a HAR model. We show that for out-of-sample forecasts, our dependence estimator performs better than the constant estimator and other commonly used time-varying copula dependence estimators.

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Comparing the accuracy of multivariate density forecasts in selected regions of the copula support

Cited by 22 publications

References 23 publications

Comparing density forecasts in a risk management context

Comparing density forecasts in a risk management context

Closed-Form Multi-Factor Copula Models with Observation-Driven Dynamic Factor Loadings

Estimating dynamic copula dependence using intraday data

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