Published papers

  • Christoph E. Weiss and Eran Raviv and Gernot Roetzer The R Journal, 2018.

Forecast Combinations in R using the ForecastComb Package

This paper introduces the R package ForecastComb. The aim is to provide researchers and practitioners with a comprehensive implementation of the most common ways in which forecasts can be combined. The package in its current version covers 15 popular estimation methods for creating a combined forecasts – including simple methods, regression-based methods, and eigenvector-based methods. It also includes useful tools to deal with common challenges of forecast combination (e.g., missing values in component forecasts, or multicollinearity), and to rationalize and visualize the combination results.

16 mins presentation of the paper

  • Eran Raviv, Kees E. Bouwman, and Dick van Dijk. Energy Economics, 2015.

Working paper version: Forecasting day-ahead electricity prices: utilizing hourly prices, May-2013 .

The daily average price of electricity represents the price of electricity to be delivered over the full next day and serves as a key reference price in the electricity market. It is an aggregate that equals the average of hourly prices for delivery during each of the 24 individual hours. This paper demonstrates that the disaggregated hourly prices contain useful predictive information for the daily average price. Multivariate models for the full panel of hourly prices significantly outperform univariate models of the daily average price, with reductions in Root Mean Squared Error of up to 16%. Substantial care is required in order to achieve these forecast improvements. Rich multivariate models are needed to exploit the relations between different hourly prices, but the risk of overfitting must be mitigated by using dimension reduction techniques, shrinkage and forecast combinations.

  • Eran Raviv, Economic Letters, 2015.

Working paper version: Prediction Bias Correction for Dynamic Term Structure Models (Code)

When the yield curve is modelled using an affine factor model, residuals may still contain relevant information and do not adhere to the familiar white noise assumption. This paper proposes a pragmatic way to improve out of sample performance for yield curve forecasting. The proposed adjustment is illustrated via a pseudo out-of-sample forecasting exercise implementing the widely used Dynamic Nelson Siegel model. Large improvement in forecasting performance is achieved throughout the curve for different forecasting horizons. Results are robust to different time periods, as well as to different model specifications.

    • Jakub Nowotarski, Eran Raviv, Stefan Trueck, Rafal Weron. Energy Economics, 2014. 

Working paper version: An Empirical Comparison of Alternate Schemes for Combining Electricity Spot Price Forecasts

In this paper we investigate the use of forecast averaging for electricity spot prices. While there is an increasing body of literature on the use of forecast combinations, there is only a small number of applications of these techniques in the area of electricity markets. In this comprehensive empirical study we apply seven averaging and one selection scheme and perform a backtesting analysis on day-ahead electricity prices in three major European and US markets. Our findings support the additional benefit of combining forecasts for deriving more accurate predictions, however, the performance is not uniform across the considered markets. Interestingly, equally weighted pooling of forecasts emerges as a viable robust alternative compared with other schemes that rely on estimated combination weights. Overall, we provide empirical evidence that also for the extremely volatile electricity markets, it is beneficial to combine forecasts from various models for the prediction of day-ahead electricity prices. In addition, we empirically demonstrate that not all forecast combination schemes are recommended.

Working (or just waiting) papers

  • Eran Raviv and Dick van Dijk

Forecasting with Many Predictors: Allowing for Non-linearity Nov-2014. Note this is what they call a “zombie paper”. The paper is in dire need for a major revision which we never properly took on. I have removed it from my SSRN page, but I think the paper still has value (the idea itself, literature review etc) and made it now (Nov 2020) available for download here.

While there is an extensive literature concerning forecasting with many predictors, there are but few attempts to allow for non-linearity in such a “data-rich environment”. Using macroeconomic data, we show that substantial gains in forecast accuracy can be achieved by including both squares and first level interactions of the original variables in a predictive regression model. In case the number of original variables is reasonably large this requires specific econometric considerations though, as the number of parameters to be estimated may greatly exceed the number of available observations. We propose a two-stage “screen and clean” procedure that enables estimation and forecasting in this “ultrahigh-dimensional” setting. In the first stage, we perform univariate regressions to screen for truly interesting effects, controlling the False Discovery Rate. In the second step, we perform a standard bridge regression.

  • Kees E. Bouwman, Eran Raviv, and Dick van Dijk

An Arithmetic Modeling Framework for the Term Structure of Electricity Prices May-2012.

We propose a tractable class of arbitrage-free models for the term structure of electricity prices, where spot and forward prices are a linear function of latent factors. The modeling approach offers much flexibility in the specification of the factor dynamics by only restricting their risk-neutral drift. We derive a canonical form where the parameters determining the factor loadings for the forward prices can be separated from the parameters describing the factor dynamics. The factor loading parameters can be consistently estimated by directly fitting the cross-section of forward prices. The modeling framework is applied to a panel of daily prices on forward contracts from the Nordpool electricity market, using affine factor dynamics. We find that forward prices (i) are mainly driven by changes in the level, slope and curvature of the forward curve; (ii) exhibit time-varying volatilities; and (iii) incorporate time-varying forward premia.


ForecastCombinations R package, with manual, and a short presentation.

International conference presentations

  • 06/2011 – 17th International Conference on Computing in Economics and Finance, San Francisco. 
  • 06/2012 – 35th IAEE International Conference, Perth. 
  • 08/2012 – 66th European Meeting of the Econometric Society, Málaga. 
  • 10/2012 – International Energy Finance conference,Trondheim.
  • 06/2013 – International Symposium on Forecasting, Seoul.
  • 07/2013 – SIRE, Finance and Commodities, St Andrews.
  • 09/2013 – The S3 Interdisciplinary Seminar, Wroclaw.
  • 04/2014 – European workshop on electricity price forecasting, Université Paris-Dauphine.
  • 05/2016 – R/Finance 2016: Applied Finance with R, Chicago
  • Stopped update