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Combination of multivariate volatility forecasts
Amendola, Alessandra; Storti, Giuseppe - 2009
the Generalized Method of Moments (GMM). It is shown how the procedure can be generalized to deal with large dimensional … systems by means of a two-step strategy. The finite sample properties of the GMM estimator of the combination weights are … combination ; GMM ; portfolio optimization …
Persistent link: https://www.econbiz.de/10003796201
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Combination of multivariate volatility forecasts
Amendola, Alessandra; Storti, Giuseppe - Sonderforschungsbereich 649: Ökonomisches Risiko, … - 2009
the Generalized Method of Moments (GMM). It is shown how the procedure can be generalized to deal with large dimensional … systems by means of a two-step strategy. The finite sample properties of the GMM estimator of the combination weights are …
Persistent link: https://www.econbiz.de/10005207941
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A non-elliptical orthogonal GARCH model for portfolio selection under transaction costs
Paolella, Marc S.; Polak, Pawel; Walker, Patrick S. - 2019
Covariance matrix forecasts for portfolio optimization have to balance sensitivity to new data points with stability in order to avoid excessive rebalancing. To achieve this, a new robust orthogonal GARCH model for a multivariate set of non-Gaussian asset returns is proposed. The conditional...
Persistent link: https://www.econbiz.de/10012134234
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Forecasting Stock Market Volatility and Application to Volatility Timing Portfolios
Chun, Dohyun; Cho, Hoon; Ryu, Doojin - 2022
This study predicts stock market volatility and applies them to the standard problem in finance, namely, asset allocation. Based on machine learning and model averaging approaches, we integrate the drivers’ predictive information to forecast market volatilities. Using various evaluation...
Persistent link: https://www.econbiz.de/10013404229
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Testing directional forecast value in the presence of serial correlation
Blaskowitz, Oliver J.; Herwartz, Helmut - 2008
Common approaches to test for the economic value of directional forecasts are based on the classical Chi-square test for independence, Fisher’s exact test or the Pesaran and Timmerman (1992) test for market timing. These tests are asymptotically valid for serially independent observations....
Persistent link: https://www.econbiz.de/10010271838
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Testing directional forecast value in the presence of serial correlation
Blaskowitz, Oliver; Herwartz, Helmut - 2008
Common approaches to test for the economic value of directional forecasts are based on the classical Chi-square test for independence, Fisher’s exact test or the Pesaran and Timmerman (1992) test for market timing. These tests are asymptotically valid for serially independent observations....
Persistent link: https://www.econbiz.de/10003796145
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Combination ofmultivariate volatilityforecasts
Amendola, Alessandra; Storti, Giuseppe - Sonderforschungsbereich Ökonomisches Risiko <Berlin> - 2009
the Generalized Method of Moments (GMM). Itis shown how the procedure can be generalized to deal with large dimensional … systemsby means of a two-step strategy. The finite sample properties of the GMM estimatorof the combination weights are …
Persistent link: https://www.econbiz.de/10005865451
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Can We Beat the Random Walk? The Case of Survey-Based Exchange Rate Forecasts in Chile
Pincheira, Pablo M. - 2018
We examine the accuracy of survey-based expectations of the Chilean exchange rate relative to the US dollar. Our out-of-sample analysis reveals that survey-based forecasts outperform the Driftless Random Walk (DRW) in terms of Mean Squared Prediction Error at several forecasting horizons. This...
Persistent link: https://www.econbiz.de/10012906841
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Forecasting Expected Shortfall : Should We Use a Multivariate Model for Stock Market Factors?
Fortin, Alain-Philippe - 2019
Is univariate or multivariate modelling more effective when forecasting the market risk of stock portfolios? We examine this question in the context of forecasting the one-week-ahead Expected Shortfall of a portfolio invested in the Fama-French and momentum factors. Apply ingextensive tests and...
Persistent link: https://www.econbiz.de/10012898954
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Large dynamic covariance matrices: enhancements based on intraday data
De Nard, Gianluca; Engle, Robert F.; Ledoit, Olivier; … - 2021 - This version: June 2021
Multivariate GARCH models do not perform well in large dimensions due to the so-called curse of dimensionality. The recent DCC-NL model of Engle et al. (2019) is able to overcome this curse via nonlinear shrinkage estimation of the unconditional correlation matrix. In this paper, we show how...
Persistent link: https://www.econbiz.de/10012584099