Narrow search
Showing 1 - 10 of 42,747
Cover Image
Robust estimation and forecasting of the long-term seasonal component of electricity spot prices
Nowotarski, Jakub; Tomczyk, Jakub; Weron, Rafał - In: Energy Economics 39 (2013) C, pp. 13-27
We present the results of an extensive study on estimation and forecasting of the long-term seasonal component (LTSC … are significantly better in terms of forecasting spot prices up to a year ahead than the commonly used monthly dummies and …
Persistent link: https://www.econbiz.de/10011039659
Cover Image
Modelling and forecasting rig rates on the Norwegian Continental Shelf
Skjerpen, Terje; Storrøsten, Halvor Briseid; … - 2015
Knowledge about rig markets is crucial for understanding the global oil market. In this paper we first develop a simple bargaining model for rig markets. Then we examine empirically the most important drivers for rig rate formation of floaters operating at the Norwegian Continental Shelf in the...
Persistent link: https://www.econbiz.de/10011968602
Cover Image
Electricity demand analysis and forecasting: A panel cointegration approach
El-Shazly, Alaa - In: Energy Economics 40 (2013) C, pp. 251-258
This article analyzes the demand for electricity and provides out-of-sample forecasting at the sectoral level using a … one would expect if the forecasting relationship is stationary. The long-run parameter estimates are then used to conduct … ex-ante forecasting under plausible assumptions for policy making. …
Persistent link: https://www.econbiz.de/10010718767
Cover Image
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/10012250683
Cover Image
Machine-learning-based semiparametric time series conditional variance: Estimation and forecasting
Dang, Justin; Ullah, Aman - In: Journal of Risk and Financial Management 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/10013201342
Cover Image
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
Cover Image
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
Cover Image
Forecasting Monetary Rules in South Africa
Naraidoo, Ruthira; Paya, Ivan - Department of Economics, Faculty of Economic and … - 2010
as nonparametric and semiparametric models in forecasting the nominal interest rate setting that describes the South …:01 to 2004:12, we compare the out-of-sample forecasting ability of the models over the period 2005:01 to 2008:12. Our …
Persistent link: https://www.econbiz.de/10008513007
Cover Image
Forecasting Implied Volatility Surfaces
Audrino, Francesco; Colagelo, Dominik - School of Economics and Political Science, Universität … - 2007
We propose a new semi-parametric model for the implied volatility surface, which incorporates machine learning algorithms. Given a starting model, a tree-boosting algorithm sequentially minimizes the residuals of observed and estimated implied volatility. To overcome the poor predicting power of...
Persistent link: https://www.econbiz.de/10005453978
Cover Image
Forecasting Monetary Policy Rules in South Africa
Naraidoo, Ruthira; Paya, Ivan - Economic Research Southern Africa (ERSA) - 2010
rule specifications as well as nonparametric and semiparametric models in forecasting the nominal interest rate setting … semiparametric models in forecasting the interest rates as the forecasting horizon lengthens. …
Persistent link: https://www.econbiz.de/10008643862