Similar Search Results

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 - 2017

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/10012970357

Forecasting value-at-risk under temporal and portfolio aggregation

Kole, Erik; Markwat, Thijs; Opschoor, Anne; Dijk, Dick van - 2015

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 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

Boosting Cryptocurrency Return Prediction

Filippou, Ilias; Rapach, David; Thimsen, Christoffer - 2021

We use boosted decision trees to generate daily out-of-sample forecasts of excess returns for Bitcoin and Ethereum, the two best-known and largest cryptocurrencies. The decision trees incorporate information from 39 predictors, including variables relating to cryptocurrency fundamentals,...

Persistent link: https://www.econbiz.de/10013213970

Sparse Multivariate Modeling for Stock Returns Predictability

Bernardi, Mauro; Bianchi, Daniele; Bianco, Nicolas - 2021

We develop a new variational Bayes estimation method for large-dimensional sparse vector autoregressive models with exogenous predictors. Unlike existing Markov chain Monte Carlo (MCMC) and variational Bayes (VB) algorithms, our approach is not based on a structural form representation of the...

Persistent link: https://www.econbiz.de/10013239660

Currency Hedging Strategies, Strategic Benchmarks and the Global and Euro Sovereign Financial Crises

Caporin, Massimiliano - 2013

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/10013074792

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