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OverviewA Simple Method for Predicting Covariance Matrices of Financial Returns makes three contributions. First, it proposes a new method for predicting the time-varying covariance matrix of a vector of financial returns, building on a specific covariance estimator suggested by Engle in 2002. The second contribution proposes a new method for evaluating a covariance predictor, by considering the regret of the log-likelihood over some time period such as a quarter. The third contribution is an extensive empirical study of covariance predictors. The authors compare their method to other popular predictors, including rolling window, exponentially weighted moving average (EWMA) and generalized autoregressive conditional heteroscedastic (GARCH) type methods. After an introduction, Section 2 describes some common predictors, including the one that this method builds on. Section 3 introduces the proposed covariance predictor. Section 4 discusses methods for validating covariance predictors that measure both overall performance and reactivity to market changes. Section 5 describes the data used in the authors’ first empirical studies and the results are provided in Section 6. The authors then discuss some extensions of and variations on the method, including realized covariance prediction (Section 7), handling large universes via factor models (Section 8), obtaining smooth covariance estimates (Section 9), and using the authors’ covariance model to generate simulated returns (Section 10). Full Product DetailsAuthor: Kasper Johansson , Mehmet G. Ogut , Markus Pelger , Thomas SchmelzerPublisher: now publishers Inc Imprint: now publishers Inc Weight: 0.151kg ISBN: 9781638283089ISBN 10: 1638283087 Pages: 98 Publication Date: 21 November 2023 Audience: Professional and scholarly , Professional & Vocational Format: Paperback Publisher's Status: Active Availability: In Print This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us. Table of Contents1. Introduction 2. Some Common Covariance Predictors 3. Combined Multiple Iterated EWMAs 4. Evaluating Covariance Predictors 5. Data Sets and Experimental Setup 6. Results 7. Realized Covariance 8. Large Universes 9. Smooth Covariance Predictions 10. Simulating Returns 11. Conclusions Acknowledgements ReferencesReviewsAuthor InformationTab Content 6Author Website:Countries AvailableAll regions |