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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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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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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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Large dynamic covariance matrices: enhancements based on intraday data
De Nard, Gianluca; Engle, Robert F.; Ledoit, Olivier; … - 2022 - This version: January 2022
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/10013040932
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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
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Large dynamic covariance matrices: enhancements based on intraday data
De Nard, Gianluca; Engle, Robert F.; Ledoit, Olivier; … - 2020
Modeling and forecasting dynamic (or time-varying) covariance matrices has many important applications in finance, such as Markowitz portfolio selection. A popular tool to this end are multivariate GARCH models. Historically, such models did not perform well in large dimensions due to the...
Persistent link: https://www.econbiz.de/10012253083
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Momentum Without Crashes
Chitsiripanich, Soros; Paolella, Marc S.; Polak, Pawel; … - 2022
We construct a momentum factor that identifies cross-sectional winners and losers based on a weighting scheme that incorporates all the price data, over the entire lookback period, as opposed to only the first and last price points of the window. The weighting scheme is derived from the...
Persistent link: https://www.econbiz.de/10014236192
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Forecasting value-at-risk under temporal and portfolio aggregation
Kole, Erik; Markwat, Thijs; Opschoor, Anne; Dijk, Dick van - 2015
We examine the impact of temporal and portfolio aggregation on the quality of Value-at-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...
Persistent link: https://www.econbiz.de/10011431503
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Exponential high-frequency-based-volatility (EHEAVY) models
Xu, Yongdeng - 2022
This paper proposes an Exponential HEAVY (EHEAVY) model. The model specifies the dynamics of returns and realized measures of volatility in an exponential form, which guarantees the positivity of volatility without restrictions on parameters and naturally allows the asymmetric effects. It...
Persistent link: https://www.econbiz.de/10013177995
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Predicting the global minimum variance portfolio
Reh, Laura; Krüger, Fabian; Liesenfeld, Roman - 2020
We propose a novel dynamic approach to forecast the weights of the global minimum variance portfolio (GMVP). The GMVP weights are the population coefficients of a linear regression of a benchmark return on a vector of return differences. This representation enables us to derive a consistent loss...
Persistent link: https://www.econbiz.de/10012243462