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on Forecasting |
| By: | Zsolt Darvas (Department of Mathematical Economics and Economic Analysis, Corvinus University of Budapest); Zoltán Schepp (University of Pécs) |
| Abstract: | This paper shows that error correction models assuming that long-maturity forward rates are stationary outperform the random walk in out of sample forecasting at forecasting horizons mostly above one year, for US dollar exchange rates against nine industrial countries’ currencies, using the 1990-2006 period for evaluating the out of sample forecasts. The improvement in forecast accuracy of our models is economically significant for most of the exchange rate series, and statistically significant according to a bootstrap test. Our results are robust to the specification of the error correction model and to the underlying data frequency. |
| Keywords: | bootstrap, forecasting performance, out of sample, random walk, VECM |
| JEL: | E43 F31 F47 |
| Date: | 2007–05–18 |
| URL: | http://d.repec.org/n?u=RePEc:mkg:wpaper:0705&r=for |
| By: | Karine Michalon (DRM - Dauphine Recherches en Management - [CNRS : UMR7088] - [Université Paris Dauphine - Paris IX]); Sandrine Lardic (EconomiX - [CNRS : UMR7166] - [Université de Paris X - Nanterre]); François Dossou (SINOPIA AM - Sinopia AM - [Sinopia AM]) |
| Abstract: | The evaluation of the reliability of analysts' earnings forecasts is an important aspect of research for different reasons: Many empirical studies employ analysts' consensus forecasts as a proxy for the market's expectations of future earnings in order to identify the unanticipated component of earnings, institutional investors make considerable use of analysts' forecasts when evaluating and selecting individual sharesand the performance of analysts' forecasts sheds light on the process by which agents form expectations about key economic and financial variables. The recent period put forward a well-known phenomenon, namely the existence of a positive bias in experts' anticipations: the latter tend to over-estimate earnings. In this paper, we study the properties of this bias according to various aspects, that is to say according to country, sector, but also according to the size of the companies. |
| Keywords: | earnings forecasts, bias, consensus |
| Date: | 2007–05–10 |
| URL: | http://d.repec.org/n?u=RePEc:hal:papers:halshs-00142773_v1&r=for |
| By: | Ozun, Alper; Cifter, Atilla; Yilmazer, Sait |
| Abstract: | Extreme returns in stock returns need to be captured for a successful risk management function to estimate unexpected loss in portfolio. Traditional value-at-risk models based on parametric models are not able to capture the extremes in emerging markets where high volatility and nonlinear behaviors in returns are observed. The Extreme Value Theory (EVT) with conditional quantile proposed by McNeil and Frey (2000) is based on the central limit theorem applied to the extremes rater than mean of the return distribution. It limits the distribution of extreme returns always has the same form without relying on the distribution of the parent variable. This paper uses 8 filtered EVT models created with conditional quantile to estimate value-at-risk for the Istanbul Stock Exchange (ISE). The performances of the filtered expected shortfall models are compared to those of GARCH, GARCH with student-t distribution, GARCH with skewed student-t distribution and FIGARCH by using alternative back-testing algorithms, namely, Kupiec test (1995), Christoffersen test (1998), Lopez test (1999), RMSE (70 days) h-step ahead forecasting RMSE (70 days), number of exception and h-step ahead number of exception. The test results show that the filtered expected shortfall has better performance on capturing fat-tails in the stock returns than parametric value-at-risk models do. Besides increase in conditional quantile decreases h-step ahead number of exceptions and this shows that filtered expected shortfall with higher conditional quantile such as 40 days should be used for forward looking forecasting. |
| Keywords: | Value at-Risk; Filtered Expected shortfall; Extreme value theory; emerging markets |
| JEL: | C32 G0 C52 |
| Date: | 2007–05–22 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:3302&r=for |
| By: | Christian Huurman (Financial Engineering Associates); Francesco Ravazzolo (Erasmus Universiteit Rotterdam); Chen Zhou (Erasmus Universiteit Rotterdam) |
| Abstract: | In the literature the effects of weather on electricity sales are well-documented. However, studies that have investigated the impact of weather on electricity prices are still scarce (e.g. Knittel and Roberts, 2005), partly because the wholesale power markets have only recently been deregulated. We introduce the weather factor into well-known forecasting models to study its impact. We find that weather has explanatory power for the day-ahead power spot price. Using weather forecasts improves the forecast accuracy, and in particular new models with power transformations of weather forecast variables are significantly better in term of out-of-sample statistics than popular mean reverting models. For different power markets, such as Norway, Eastern Denmark and the Netherlands, we build specific models. The dissimilarity among these models indicates that weather forecasts influence not only the demand of electricity but also the supply side according to different electricity producing methods. |
| Keywords: | Electricity prices; forecasting; GARCH models; weather forecasts |
| JEL: | C53 G15 Q40 |
| Date: | 2007–04–25 |
| URL: | http://d.repec.org/n?u=RePEc:dgr:uvatin:20070036&r=for |