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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

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

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

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

Forecasting Value-at-Risk Under Temporal and Portfolio Aggregation

Kole, Erik - 2017

Persistent link: https://www.econbiz.de/10012970357

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

A Dynamic Conditional Approach to Portfolio Weights Forecasting

Cipollini, Fabrizio - 2020

We build the time series of optimal realized portfolio weights from high-frequency data and we suggest a novel Dynamic Conditional Weights (DCW) model for their dynamics. DCW is benchmarked against popular model-based and model-free specifications in terms of weights forecasts and portfolio...

Persistent link: https://www.econbiz.de/10012835791

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

Are Unconventional Monetary Policies a Priced Risk Factor for Hedge Fund Strategies?

Guidolin, Massimo - 2020

We test whether the unconventional monetary policy (UMP) announcements by the Federal Reserve and the European Central Bank represent a risk factor for the hedge fund industry as a whole and for ten commonly used strategies in particular. Using modified event studies and Markov switching models,...

Persistent link: https://www.econbiz.de/10012828359

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

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