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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 …

Persistent link: https://www.econbiz.de/10010263760

Combination of multivariate volatility forecasts

Amendola, Alessandra; Storti, Giuseppe - 2009

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

Currency hedging strategies in strategic benchmarks and the global and Euro sovereign financial crises

Caporin, Massimiliano; Jimenez-Martin, Juan-Angel; … - In: Journal of International Financial Markets, … 31 (2014) C, pp. 159-177

This paper investigates dynamic currency hedging benefits, with a further focus on the impact of currency hedging before and during the recent financial crises originated from the subprime and the Euro sovereign bonds. We take the point of view of a Euro-based institutional investor who...

Persistent link: https://www.econbiz.de/10011041518

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 Value-at-Risk Under Temporal and Portfolio Aggregation

Kole, Erik - 2017

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

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 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

Liquidity Premium, Liquidity-Adjusted Return and Volatility, and a Unified Modern Portfolio Theory : illustrated with Crypto Assets

Deng, Qi; Zhou, Zhong-Guo - 2023

We establish innovative measures of liquidity premium Beta on both asset and portfolio levels, and corresponding liquidity-adjusted return and volatility, for selected crypto assets. We develop a liquidity-adjusted ARMA-GARCH/EGARCH representation to model the liquidity-adjusted return for...

Persistent link: https://www.econbiz.de/10014349884

Predicting the Global Minimum Variance Portfolio

Reh, Laura - 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/10012847269

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