The Model Confidence Set

Hansen, Peter Reinhard; Lunde, Asger; Nason, James M.

doi:10.2139/ssrn.522382

Cited by 355 publications

(687 citation statements)

References 47 publications

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“…9 We rank the methods based on a loss function, and test whether loss differentials are significant in the style of Diebold and Mariano (1995); Giacomini and White (2006). We also use the loss function to determine the Model Confidence Set of Hansen et al (2011) for different sets of methods.…”

Section: Discussionmentioning

confidence: 99%

“…We check whether a particular method outperforms another by comparing the values of the asymmetric tick-loss function as in Giacomini and Komunjer (2005) based on the test of Diebold and Mariano (1995) in the framework of Giacomini and White (2006), and construct Model Confidence Sets as proposed by Hansen et al (2011) to assess the importance of a particular choice. These horse races are based on more than five thousand ten-day VaR forecasts for the period 1994-2014.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Forecasting Value-at-Risk under Temporal and Portfolio Aggregation*

Kole

Markwat²,

Opschoor

et al. 2017

Journal of Financial Econometrics

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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. Terms of use: Documents in December 2015 AbstractWe examine the impact of temporal and portfolio aggregation on the quality of Valueat-Risk (VaR) forecasts over a horizon of ten trading days for a well-diversified portfolio of stocks, bonds and alternative investments. The VaR forecasts are constructed based on daily, weekly or biweekly returns of all constituent assets separately, gathered into portfolios based on asset class, or into a single portfolio. We compare the impact of aggregation to that of choosing a model for the conditional volatilities and correlations, the distribution for the innovations and the method of forecast construction. We find that the degree of temporal aggregation is most important. Daily returns form the best basis for VaR forecasts. Modelling the portfolio at the asset or asset class level works better than complete portfolio aggregation, but differences are smaller. The differences from the model, distribution and forecast choices are also smaller compared to temporal aggregation.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Forecasting Value-at-Risk under Temporal and Portfolio Aggregation*

Kole

Markwat²,

Opschoor

et al. 2017

Journal of Financial Econometrics

View full text Add to dashboard Cite

show abstract

“…To verify whether we are able to statistically distinguish parameter-driven models from observation-driven models, we continue with an analysis based on model confidence sets (MCS) as recently proposed by Hansen, Lunde, and Nason (2011). The design of a MCS is such that it contains the best model in terms of a chosen loss function with a certain level of confidence.…”

Section: Analysis Based On Model Confidence Setsmentioning

confidence: 99%

Predicting Time-Varying Parameters with Parameter-Driven and Observation-Driven Models

Koopman

Scharth

2012

SSRN Journal

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“…To test significant differences of competing models, we use the Model Confidence Set (MSC) methodology of Hansen et al (2011). Given a set of forecasting models, M 0 , we identify the model confidence set M * 1−α ⊂ M 0 , which is the set of models that contain the "best" forecasting model given a level of confidence α.…”

Section: Statistical Evaluation Of the Forecastsmentioning

confidence: 99%

“…We test the ANN against widely used HAR and ARFIMA models using the recently proposed frameworks of Model Confidence Set (MCS) from Hansen et al (2011) and Superior Predictive Ability (SPA) from Hansen (2005) with several popular loss functions used in the literature. Moreover, we use realized variance (RV), realized kernel (RK), two-scale realized variance (TSRV), bipower variation (BV), median realized volatility (MedRV), and the recently proposed jump-adjusted wavelet two-scale realized variance 3 (JWTSRV) measures of volatility.…”

Section: Introductionmentioning

confidence: 99%

Coupling High-Frequency Data with Nonlinear Models in Multiple-Step-Ahead Forecasting of Energy Markets' Volatility

Baruník

Křehlík

2014

SSRN Journal

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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: Terms of use: Documents inIn the past decade, the popularity of realized measures and various linear models for volatility forecasting has attracted attention in the literature on the price variability of energy markets. However, results that would guide practitioners to a specifc estimator and model when aiming for the best forecasting accuracy are missing. This paper contributes to the ongoing debate with a comprehensive evaluation of multiple-step-ahead volatility forecasts of energy markets using several popular high-frequency measures and forecasting models. To capture the complex patterns hidden to linear models commonly used to forecast realized volatility, this paper also contributes to the literature by coupling realized measures with artificial neural networks as a forecasting tool. Forecasting performance is compared across models as well as realized measures of crude oil, heating oil, and natural gas volatility during three qualitatively distinct periods covering the precrisis period, recent global turmoil of markets in 2008, and the most recent post-crisis period. We conclude that coupling realized measures with arti_cial neural networks results in both statistical and economic gains, reducing the tendency to over-predict volatility uniformly during all tested periods. Our analysis favors the median realized volatility, as it delivers the best performance and is a computationally simple alternative for practitioners.

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The Model Confidence Set

Cited by 355 publications

References 47 publications

Forecasting Value-at-Risk under Temporal and Portfolio Aggregation*

Forecasting Value-at-Risk under Temporal and Portfolio Aggregation*

Predicting Time-Varying Parameters with Parameter-Driven and Observation-Driven Models

Coupling High-Frequency Data with Nonlinear Models in Multiple-Step-Ahead Forecasting of Energy Markets' Volatility

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