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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

Machine-learning-based semiparametric time series conditional variance : estimation and forecasting

Dang, Justin; Ullah, Aman - In: Journal of risk and financial management : JRFM 15 (2022) 1, pp. 1-12

This paper proposes a new combined semiparametric estimator of the conditional variance that takes the product of a parametric estimator and a nonparametric estimator based on machine learning. A popular kernel-based machine learning algorithm, known as the kernel-regularized least squares...

Persistent link: https://www.econbiz.de/10012814196

Sparse and robust factor modelling

Croux, Christophe; Exterkate, Peter - 2011

Persistent link: https://www.econbiz.de/10009720758

Splines for Financial Volatility

Audrino, Francesco; Buhlmann, Peter - 2007

We propose a flexible GARCH-type model for the prediction of volatility in financial time series. The approach relies on the idea of using multivariate B-splines of lagged observations and volatilities. Estimation of such a B-spline basis expansion is constructed within the likelihood framework...

Persistent link: https://www.econbiz.de/10014051065

Extremal Quantiles and Value-at-Risk

Chernozhukov, Victor; Du, Songzi - 2007

This article looks at the theory and empirics of extremal quantiles in economics, in particular value-at-risk. The theory of extremes has gone through remarkable developments and produced valuable empirical findings in the last 20 years. In the discussion, we put a particular focus on...

Persistent link: https://www.econbiz.de/10014053485

Estimating Dynamic Conditional Spread Densities to Optimise Daily Storage Trading of Electricity

Abramova, Ekaterina; Bunn, Derek W. - 2019

This paper formulates dynamic density functions, based upon skewed-t and similar representations, to model and forecast electricity price spreads between different hours of the day. This supports an optimal day ahead storage and discharge schedule, and thereby facilitates a bidding strategy for...

Persistent link: https://www.econbiz.de/10014107616

Matrix Box-Cox models for multivariate realized volatility

Weigand, Roland - 2014

estimator and discuss bias-corrected point and density forecasting by simulation. The methods are applied to stock market data …

Persistent link: https://www.econbiz.de/10010344500

Empirical Pricing Kernel Estimation Using a Functional Gradient Descent Algorithm Based on Splines

Meier, Pirmin - 2012

We propose a new methodology to estimate the empirical pricing kernel implied from option data. In contrast to most of the studies in the literature that use an indirect approach, i.e. first estimating the physical and risk-neutral densities and obtaining the pricing kernel in a second step, we...

Persistent link: https://www.econbiz.de/10013108080

Predicting the Global Minimum Variance Portfolio

Reh, Laura - 2020

Persistent link: https://www.econbiz.de/10012847269

Volatility Estimation and Forecasts Based on Price Durations

Hong, Seok Young - 2020

duration estimators can be used for the estimation and forecasting of the integrated variance of an underlying semi … estimators. We provide simulation and forecasting evidence that price duration estimators can extract relevant information from …-implied variance estimators, when considered in isolation or as part of a forecasting combination setting …

Persistent link: https://www.econbiz.de/10012855793