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on Forecasting |
| By: | T. O. Benli |
| Abstract: | It is very vital for suppliers and distributors to predict the deregulated electricity prices for creating their bidding strategies in the competitive market area. Pre requirement of succeeding in this field, accurate and suitable electricity tariff price forecasting tools are needed. In the presence of effective forecasting tools, taking the decisions of production, merchandising, maintenance and investment with the aim of maximizing the profits and benefits can be successively and effectively done. According to the electricity demand, there are four various electricity tariffs pricing in Turkey; monochromic, day, peak and night. The objective is find the best suitable tool for predicting the four pricing periods of electricity and produce short term forecasts (one year ahead-monthly). Our approach based on finding the best model, which ensures the smallest forecasting error measurements of: MAPE, MAD and MSD. We conduct a comparison of various forecasting approaches in total accounts for nine teen, at least all of those have different aspects of methodology. Our beginning step was doing forecasts for the year 2015. We validated and analyzed the performance of our best model and made comparisons to see how well the historical values of 2015 and forecasted data for that specific period matched. Results show that given the time-series data, the recommended models provided good forecasts. Second part of practice, we also include the year 2015, and compute all the models with the time series of January 2011 to December 2015. Again by choosing the best appropriate forecasting model, we conducted the forecast process and also analyze the impact of enhancing of time series periods (January 2007 to December 2015) to model that we used for forecasting process. |
| Date: | 2016–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1610.08415&r=for |
| By: | Degiannakis, Stavros; Potamia, Artemis |
| Abstract: | In order to provide reliable Value-at-Risk (VaR) and Expected Shortfall (ES) forecasts, this paper attempts to investigate whether an inter-day or an intra-day model provides accurate predictions. We investigate the performance of inter-day and intra-day volatility models by estimating the AR(1)-GARCH(1,1)-skT and the AR(1)-HAR-RV-skT frameworks, respectively. This paper is based on the recommendations of the Basel Committee on Banking Supervision. Regarding the forecasting performances, the exploitation of intra-day information does not appear to improve the accuracy of the VaR and ES forecasts for the 10-steps-ahead and 20-steps-ahead for the 95%, 97.5% and 99% significance levels. On the contrary, the GARCH specification, based on the inter-day information set, is the superior model for forecasting the multiple-days-ahead VaR and ES measurements. The intra-day volatility model is not as appropriate as it was expected to be for each of the different asset classes; stock indices, commodities and exchange rates. The inter-day specification predicts VaR and ES measures adequately at a 95% confidence level. Regarding the 97.5% confidence level that has been recently proposed in the revised 2013 version of Basel III, the GARCH-skT specification provides accurate forecasts of the risk measures for stock indices and exchange rates but not for commodities (i.e. Silver and Gold). In the case of the 99% confidence level, we do not achieve sufficiently accurate VaR and ES forecasts for all the assets. |
| Keywords: | Basel II, Basel III, Value-at-Risk, Expected Shortfall, volatility forecasting, intra-day data, multi-period-ahead, forecasting accuracy, risk modelling. |
| JEL: | C15 C32 C53 G15 G17 |
| Date: | 2016–01–01 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:74670&r=for |
| By: | Zhi-Qiang Jiang (ECUST, BU); Gang-Jin Wang (HNU, BU); Askery Canabarro (BU, UFA); Boris Podobnik (ZSEM); Chi Xie (HNU); H. Eugene Stanley (BU); Wei-Xing Zhou (ECUST) |
| Abstract: | Being able to predict the occurrence of extreme returns is important in financial risk management. Using the distribution of recurrence intervals---the waiting time between consecutive extremes---we show that these extreme returns are predictable on the short term. Examining a range of different types of returns and thresholds we find that recurrence intervals follow a $q$-exponential distribution, which we then use to theoretically derive the hazard probability $W(\Delta t |t)$. Maximizing the usefulness of extreme forecasts to define an optimized hazard threshold, we indicates a financial extreme occurring within the next day when the hazard probability is greater than the optimized threshold. Both in-sample tests and out-of-sample predictions indicate that these forecasts are more accurate than a benchmark that ignores the predictive signals. This recurrence interval finding deepens our understanding of reoccurring extreme returns and can be applied to forecast extremes in risk management. |
| Date: | 2016–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1610.08230&r=for |
| By: | Yunjung Kim (Department of Economics, Korea University, Seoul, Republic of Korea); Cheolbeom Park (Department of Economics, Korea University, Seoul, Republic of Korea) |
| Abstract: | We use factor-augmented predictive regression to analyze the relation between nominal exchange rates and macroeconomic variables. Using a panel of 127 US macroeconomic time series, we estimate eight factors through principal component analysis. Those estimated factors have significant predictive power and can substantially improve the predictive power of Purchasing Power Parity through both in-sample and out-of-sample analyses. The estimated macroeconomic factor, which comoves with US money supply measures, has strong predictive power for nominal exchange rate fluctuations in the short run, while estimated factors, comoving with interest rate spreads and employment variables, have strong predictive power in the long run. Moreover, optimal factors selected by the BIC in the out-of-sample analysis differ greatly depending on the time points when forecasts are made. Finally, we show that factors extracted from a panel of 127 US time series data and those extracted from a panel of 215 Korean macroeconomic series together can predict a substantial portion of movements in the Korea-US bilateral exchange rate. |
| JEL: | F31 F37 F47 |
| Date: | 2016 |
| URL: | http://d.repec.org/n?u=RePEc:iek:wpaper:1606&r=for |
| By: | Michael Clements (ICMA Centre, Henley Business School, University of Reading) |
| Abstract: | We consider whether survey density forecasts (such as the in?ation and output growth histograms of the US Survey of Professional Forecasters) are superior to unconditional density forecasts. The unconditional forecasts assume that the average level of uncertainty experienced in the past will prevail in the future, whereas the SPF projections ought to be adapted to current conditions and the outlook at each forecast origin. The SPF forecasts might be expected to outperform the unconditional densities at the shortest horizons, but this does not transpire to be the case, for either the aggregate or individual respondents'? forecasts. |
| Keywords: | probability distribution forecasts, aggregation, Kullback-Leibler information criterion |
| JEL: | C53 |
| Date: | 2016–04 |
| URL: | http://d.repec.org/n?u=RePEc:rdg:icmadp:icma-dp2016-02&r=for |
| By: | Michael Clements (ICMA Centre, Henley Business School, University of Reading,) |
| Abstract: | We investigate whether there are systematic differences between forecasters in terms of their levels of disagreement and the accuracy of their forecasts, and whether these differences are related to whether or not a forecaster efficiently uses their available information. We ?find that forecasters are not interchangeable. At any point in time, the level of disagreement between forecasters is more likely to be due to a given set of forecasters, as opposed to any randomly-selected set of forecasters. In terms of forecast accuracy, we also fi?nd persistence, in that forecasters who are more (less) accurate in one period tend to be more (less) accurate in a subsequent period. Finally, we reject efficiency for around half of all forecasters at short horizons (depending on the variable in question), and ?find that efficient forecasters tend to be more accurate and less contrarian. Our results do not support the notion that contrarian forecasts stand apart by virtue of having superior information - knowing something that others do not. |
| Keywords: | Expectations formation, Disagreement, Accuracy, Forecast Efficiency |
| JEL: | C53 E37 |
| Date: | 2016–10 |
| URL: | http://d.repec.org/n?u=RePEc:rdg:icmadp:icma-dp2016-08&r=for |