Long memory dynamics for multivariate dependence under heavy tails

Janus, P.; Koopman, Siem Jan; Lucas, André

doi:10.1016/j.jempfin.2014.09.007

Cited by 48 publications

(16 citation statements)

References 49 publications

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“…Dynamics more complicated than those specified in Eq. (3) can also be added to the specification; see for example Janus et al [2011] for fractionally integrated dynamics, Creal et al [2013] for higher-order dynamics, and Harvey and Luati [2014] for both higher-order dynamics and structural time series dynamics. For our current purposes, however, the GAS(1,1) dynamics suffice.…”

Section: Score-driven Ewmamentioning

confidence: 99%

Score-driven exponentially weighted moving averages and Value-at-Risk forecasting

Lucas

Zhang

2016

International Journal of Forecasting

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Abstract A simple methodology is presented for modeling time variation in volatilities and other higher-order moments using a recursive updating scheme similar to the familiar RiskMetrics TM approach. We update parameters using the score of the forecasting distribution. This allows the parameter dynamics to adapt automatically to any nonnormal data features and robustifies the subsequent estimates. The new approach nests several of the earlier extensions to the exponentially weighted moving average (EWMA) scheme. In addition, it can easily be extended to higher dimensions and alternative forecasting distributions. The method is applied to Value-at-Risk forecasting with (skewed) Student's t distributions and a time-varying degrees of freedom and/or skewness parameter. We show that the new method is competitive to or better than earlier methods in forecasting volatility of individual stock returns and exchange rate returns. Terms of use: Documents in

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Section: Score-driven Ewmamentioning

confidence: 99%

Score-driven exponentially weighted moving averages and Value-at-Risk forecasting

Lucas

Zhang

2016

International Journal of Forecasting

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“…This is the more surprising given the typical fat-tailed nature of such data. Incidental large observations may easily corrupt the estimated dynamic pattern of the underlying covariance matrix if distributions with relatively thin tails are used; see Creal, Koopman, and Lucas (2011), Janus, Koopman, and Lucas (2014), Harvey (2013), and Lucas, Schwaab, and Zhang (2014). The matrix-F distribution provides a coherent approach to address such sensitivities.…”

Section: Introductionmentioning

confidence: 99%

New HEAVY Models for Fat-Tailed Realized Covariances and Returns

Opschoor

Janus

Lucas

et al. 2017

Journal of Business & Economic Statistics

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We develop a new score-driven model for the joint dynamics of fat-tailed realized covariance matrix observations and daily returns. The score dynamics for the unobserved true covariance matrix are robust to outliers and incidental large observations in both types of data by assuming a matrix-F distribution for the realized covariance measures and a multivariate Student's t distribution for the daily returns. The filter for the unknown covariance matrix has a computationally efficient matrix formulation, which proves beneficial for estimation and simulation purposes. We formulate parameter restrictions for stationarity and positive definiteness. Our simulation study shows that the new model is able to deal with high-dimensional settings (50 or more) and captures unobserved volatility dynamics even if the model is misspecified. We provide an empirical application to daily equity returns and realized covariance matrices up to 30 dimensions. The model statistically and economically outperforms competing multivariate volatility models out-of-sample. Supplementary materials for this article are available online.

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“…It does not matter whether they are real-valued, integer-valued, (0, 1)-bounded or strictly positive, as long as there is a conditional density for which the score function and the Hessian are well-defined. The practical relevance of the GAS framework has been illustrated in the case of financial risk forecasting (see e.g., Harvey and Sucarrat (2014) for market risk, Oh and Patton (2016) for systematic risk, and Creal, Schwaab, Koopman, and Lucas (2014) for credit risk analysis), dependence modeling (see e.g., Harvey and Thiele (2016) and Janus, Koopman, and Lucas (2014)), and spatial econometrics (see e.g., Blasques, Koopman, Lucas, and Schaumburg (2016b) and Catania and Billé (2017)). For a more complete overview of the work on GAS models, we refer the reader to the GAS community page at http://www.gasmodel.com/.…”

Section: Introductionmentioning

confidence: 99%

Generalized Autoregressive Score Models in R: The GAS Package

Ardia¹,

Boudt²,

Catania³

2019

J. Stat. Soft.

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This paper presents the R package GAS for the analysis of time series under the generalized autoregressive score (GAS) framework of Creal, Koopman, and Lucas (2013) and Harvey (2013). The distinctive feature of the GAS approach is the use of the score function as the driver of time-variation in the parameters of non-linear models. The GAS package provides functions to simulate univariate and multivariate GAS processes, to estimate the GAS parameters and to make time series forecasts. We illustrate the use of the GAS package with a detailed case study on estimating the time-varying conditional densities of financial asset returns.

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Long memory dynamics for multivariate dependence under heavy tails

Cited by 48 publications

References 49 publications

Score-driven exponentially weighted moving averages and Value-at-Risk forecasting

Score-driven exponentially weighted moving averages and Value-at-Risk forecasting

New HEAVY Models for Fat-Tailed Realized Covariances and Returns

Generalized Autoregressive Score Models in R: The GAS Package

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