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on Forecasting |
| By: | Jesus Lago; Fjo De Ridder; Peter Vrancx; Bart De Schutter |
| Abstract: | Motivated by the increasing integration among electricity markets, in this paper we propose three different methods to incorporate market integration in electricity price forecasting and to improve the predictive performance. First, we propose a deep neural network that considers features from connected markets to improve the predictive accuracy in a local market. To measure the importance of these features, we propose a novel feature selection algorithm that, by using Bayesian optimization and functional analysis of variance, analyzes the effect of the features on the algorithm performance. In addition, using market integration, we propose a second model that, by simultaneously predicting prices from two markets, improves even further the forecasting accuracy. Finally, we present a third model to predict the probability of price spikes; then, we use it as an input in the other two forecasters to detect spikes. As a case study, we consider the electricity market in Belgium and the improvements in forecasting accuracy when using various French electricity features. In detail, we show that the three proposed models lead to improvements that are statistically significant. Particularly, due to market integration, predictive accuracy is improved from 15.7% to 12.5% sMAPE (symmetric mean absolute percentage error). In addition, we also show that the proposed feature selection algorithm is able to perform a correct assessment, i.e. to discard the irrelevant features. |
| Date: | 2017–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1708.07061&r=for |
| By: | Hajer Ben Romdhane (Central Bank of Tunisia); Nahed Ben Tanfous (Central Bank of Tunisia) |
| Abstract: | The aim of this paper is to compute the conditional forecasts of a set of variables of interest on future paths of some variables in dynamic systems. We build a large dynamic factor models for a quarterly data set of 30 macroeconomic and financial indicators. Results of forecasting suggest that conditional FAVAR models which incorporate more economic information outperform the unconditional FAVAR in terms of the forecast errors. |
| Keywords: | FAVAR, Conditional FAVAR, Conditional Forecast. |
| Date: | 2017–07 |
| URL: | http://d.repec.org/n?u=RePEc:gii:giihei:heidwp15-2017&r=for |
| By: | Evangelia Kasimati (Bank of Greece); Nikolaos Veraros (King’s College London) |
| Abstract: | Participants in the maritime industry place much interest in the Forward Freight Agreements (FFA/FFAs), being an indispensable tool for hedging shipping freight risk. Our paper innovates by directly comparing the FFA predictions with their actual future settlement prices as well as by examining contracts going forward as far as next calendar year. We combine straightforward comparison measurements with cointegration analysis to test for the accuracy and efficiency of the FFA projections. We find that FFAs display limited usefulness in predicting future freights, only slightly superior than simple naive models. The shorter the contract period and the smaller the vessel the better the forecast. We also find FFAs being relatively good predictors of future market direction but missing the turning points of the market cycles. |
| Keywords: | Shipping; Freight Rates; Forward Freight Agreements; Forecasting; Vector Error Correction Models |
| JEL: | C32 G13 G14 |
| Date: | 2017–06 |
| URL: | http://d.repec.org/n?u=RePEc:bog:wpaper:230&r=for |
| By: | Haskamp, Ulrich |
| Abstract: | Regional banks as savings and cooperative banks are widespread in continental Europe. In the aftermath of the financial crisis, however, they had problems keeping their profitability which is an important quantitative indicator for the health of a bank and the banking sector overall. We use a large data set of bank-level balance sheet items and regional economic variables to forecast protability for about 2,000 regional banks. Machine learning algorithms are able to beat traditional estimators as ordinary least squares as well as autoregressive models in forecasting performance. |
| Keywords: | profitability,regional banking,forecasting,machine learning |
| JEL: | C53 G21 |
| Date: | 2017 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:rwirep:705&r=for |
| By: | Haskamp, Ulrich |
| Abstract: | There is empirical evidence for a time-varying relationship between exchange rates and fundamentals. Such a relationship with time-varying coefficients can be estimated by a Kalman filter model. A Kalman filter estimates the coefficients recursively depending on the prediction error of the examined model. Using a Taylor rule based exchange rate model, which in the literature was found to have promising forecasting abilities, it is possible to further improve the performance if the utilization of information from the prediction error is restricted. This is necessary as classic exchange rate models do not perform badly solely because they neglect the time-varying relationship, but also due to missing explanatory information. So, if the Kalman filter uses the entire information from the prediction error, it would overestimate the need for coefficient adjustment. With this calibration of the Kalman filter model the short-term out-ofsample forecasting accuracy can be enhanced for 10 out of 12 exchange rates. |
| Keywords: | exchange rates,forecasting,Kalman filter,state space models |
| JEL: | C53 F31 F37 |
| Date: | 2017 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:rwirep:704&r=for |
| By: | Grzegorz Marcjasz; Bartosz Uniejewski; Rafal Weron |
| Abstract: | In day-ahead electricity price forecasting the daily and weekly seasonalities are always taken into account, but the long-term seasonal component was believed to add unnecessary complexity and in most studies ignored. The recent introduction of the Seasonal Component AutoRegressive (SCAR) modeling framework has changed this viewpoint. However, the latter is based on linear models estimated using Ordinary Least Squares. Here we show that considering non-linear neural network-type models with the same inputs as the corresponding SCAR model can lead to a yet better performance. While individual Seasonal Component Artificial Neural Network (SCANN) models are generally worse than the corresponding SCAR-type structures, we provide empirical evidence that committee machines of SCANN networks can significantly outperform the latter. |
| Keywords: | Electricity spot price; Forecasting; Day-ahead market; Long-term seasonal component; Neural network; Committee machine |
| JEL: | C14 C22 C45 C51 C53 Q47 |
| Date: | 2017–07–29 |
| URL: | http://d.repec.org/n?u=RePEc:wuu:wpaper:hsc1703&r=for |