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Volatility Forecast Comparison Using Imperfect Volatility Proxies
 JOURNAL OF ECONOMETRICS
, 2010
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Making and evaluating point forecasts
 Journal of the American Statistical Association
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On the forecasting accuracy of multivariate GARCH models
"... Abstract This paper addresses the selection of multivariate GARCH models in terms of variance matrix forecasting accuracy with a focus on relatively large scale problems. We consider 10 assets from NYSE and NASDAQ and we compare 125 model based onestepahead conditional variance forecasts using th ..."
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Cited by 22 (3 self)
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Abstract This paper addresses the selection of multivariate GARCH models in terms of variance matrix forecasting accuracy with a focus on relatively large scale problems. We consider 10 assets from NYSE and NASDAQ and we compare 125 model based onestepahead conditional variance forecasts using the model confidence set (MCS) and the Superior Predicitive Ability (SPA) tests over a period of 10 years. Model performances are evaluated using four statistical loss functions which account for different types and degrees of asymmetry with respect to over/under predictions. When considering the full sample, MCS results are strongly driven by short periods of high market instability during which multivariate GARCH models appear to be inaccurate. Over relatively unstable periods, i.e. dotcom bubble, the set of superior models is composed of more sophisticated specifications such as orthogonal and dynamic conditional correlation (DCC), both with leverage effect in the conditional variances. However, unlike the DCC models, our results show that the orthogonal specifications tend to underestimate the conditional variance. Over calm periods, a simple assumption like constant conditional correlation and symmetry in the conditional variances cannot be rejected. Finally, during the 20072008 financial crisis, accounting for nonstationarity in the conditional variance process generates superior forecasts. The SPA test suggests that, independently from the period, the best models do not provide significantly better forecasts than the DCC model of Engle
2012): “Multivariate HighFrequencyBased Volatility (HEAVY) Models
 Journal of Applied Econometrics
"... This paper introduces a new class of multivariate volatility models that utilizes highfrequency data. We discuss the modelsdynamics and highlight their di¤erences from multivariate GARCH models. We also discuss their covariance targeting speci
cation and provide closedform formulas for multistep ..."
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Cited by 19 (2 self)
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This paper introduces a new class of multivariate volatility models that utilizes highfrequency data. We discuss the modelsdynamics and highlight their di¤erences from multivariate GARCH models. We also discuss their covariance targeting speci
cation and provide closedform formulas for multistep forecasts. Estimation and inference strategies are outlined. Empirical results suggest that the HEAVY model outperforms the multivariate GARCH model outofsample, with the gains being particularly signi
cant at short forecast horizons. Forecast gains are obtained for both forecast variances and correlations.
Financial Risk Measurement for Financial Risk Management
, 2011
"... Current practice largely follows restrictive approaches to market risk measurement, such as historical simulation or RiskMetrics. In contrast, we propose flexible methods that exploit recent developments in financial econometrics and are likely to produce more accurate risk assessments, treating bot ..."
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Cited by 11 (3 self)
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Current practice largely follows restrictive approaches to market risk measurement, such as historical simulation or RiskMetrics. In contrast, we propose flexible methods that exploit recent developments in financial econometrics and are likely to produce more accurate risk assessments, treating both portfoliolevel and assetlevel analysis. Assetlevel analysis is particularly challenging because the demands of realworld risk management in financial institutions – in particular, realtime risk tracking in very highdimensional situations – impose strict limits on model complexity. Hence we stress powerful yet parsimonious models that are easily estimated. In addition, we emphasize the need for deeper understanding of the links between market risk and macroeconomic fundamentals, focusing primarily on links among equity return volatilities, real growth, and real growth volatilities. Throughout, we strive not only to deepen our scientific understanding of market risk, but also crossfertilize the academic and practitioner communities, promoting improved market risk measurement
On loss functions and ranking forecasting performances of multivariate volatility models. Working paper
, 2009
"... A large number of parameterizations have been proposed to model conditional variance dynamics in a multivariate framework. However, little is known about the ranking of multivariate volatility models in terms of their forecasting ability. The ranking of multivariate volatility models is inherently ..."
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Cited by 10 (2 self)
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A large number of parameterizations have been proposed to model conditional variance dynamics in a multivariate framework. However, little is known about the ranking of multivariate volatility models in terms of their forecasting ability. The ranking of multivariate volatility models is inherently problematic because when the unobservable volatility is substituted by a proxy, the ordering implied by a loss function may result to be biased with respect to the intended one. We point out that the size of the distortion is strictly tied to the level of the accuracy of the volatility proxy. We propose a generalized necessary and sufficient functional form for a class of nonmetric distance measures of the Bregman type, suited to vector and matrix spaces, which ensure consistency of the ordering when the target is observed with noise. An application to three foreign exchange rates, where we compare the forecasting performance of 24 multivariate GARCH specifi
Nuisance parameters, composite likelihoods and a panel of GARCH models’, Statistica Sinica . forthcoming
, 2010
"... We investigate the properties of the composite likelihood (CL) method for (T ×NT) GARCH panels. The defining feature of a GARCH panel with timeseries length T is that, while nuisance parameters are allowed to vary across NT series, other parameters of interest are assumed to be common. CL pools inf ..."
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Cited by 5 (1 self)
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We investigate the properties of the composite likelihood (CL) method for (T ×NT) GARCH panels. The defining feature of a GARCH panel with timeseries length T is that, while nuisance parameters are allowed to vary across NT series, other parameters of interest are assumed to be common. CL pools information across the panel instead of using information available in a single series only. Simulations and empirical analysis illustrate that when T is reasonably large CL performs well. However, due to the presence of nuisance parameters, CL is subject to the “incidental parameter ” problem for small T.
Quasi Maximum Likelihood Estimator, Regular
, 2015
"... We develop a new method for generating dynamics of conditional correlation matrices between asset returns. These correlation matrices will be parameterized by a subset of their partial correlations, whose structure will be described by an undirected graph called “vine”. Since such partial correlatio ..."
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We develop a new method for generating dynamics of conditional correlation matrices between asset returns. These correlation matrices will be parameterized by a subset of their partial correlations, whose structure will be described by an undirected graph called “vine”. Since such partial correlation processes can be specified separately, our approach provides very flexible and potentially parsimonious multivariate processes. We introduce the socalled “vineGARCH ” class of processes and describe a quasimaximum likelihood (QML) estimation procedure. Compared to other usual techniques, particularly for the Dynamic Conditional Correlation family, inference is simpler and can be led equation per equation. We compare our models with some DCCtype specifications through some simulated experiments and we evaluate their empirical performances by exploiting a database of daily stock returns.
A Service of zbw Forecasting Volatility with CopulaBased Time Series Models Based Time Series Models
"... StandardNutzungsbedingungen: 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, ..."
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StandardNutzungsbedingungen: 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 OpenContentLizenzen (insbesondere CCLizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Abstract This paper develops a novel approach to modeling and forecasting realized volatility (RV) measures based on copula functions. Copulabased time series models can capture relevant characteristics of volatility such as nonlinear dynamics and longmemory type behavior in a flexible yet parsimonious way. In an empirical application to daily volatility for S&P500 index futures, we find that the copulabased RV (CRV) model outperforms conventional forecasting approaches for oneday ahead volatility forecasts in terms of accuracy and efficiency. Among the copula specifications considered, the Gumbel CRV model achieves the best forecast performance, which highlights the importance of asymmetry and upper tail dependence for modeling volatility dynamics. Although we find substantial variation in the copula parameter estimates over time, conditional copulas do not improve the accuracy of volatility forecasts. Terms of use: Documents in