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on Forecasting |
| By: | Johanna Garnitz; Robert Lehmann; Klaus Wohlrabe |
| Abstract: | Comprehensive and international comparable leading indicators across countries and continents are rare. In this paper, we use a free and instantaneous available source of leading indicators, the ifo World Economic Survey (WES), to forecast growth of Gross Domestic Product (GDP) in 44 countries and three country aggregates separately. We come up with three major results. First, for more than three-fourths of the countries or country-aggregates in our sample a model containing one of the major WES indicators produces on average lower forecast errors compared to a benchmark model. Second, the most important WES indicators are either the economic climate or the expectations on future economic development for the next six months. And third, adding the WES indicators of the main trading partners leads to a further increase of forecast accuracy in more than 50% of the countries. It seems therefore reasonable to incorporate economic signals from the domestic economy’s main trading partners. |
| Keywords: | world economic survey, economic climate, forecasting GDP |
| JEL: | E17 E27 E37 |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_7691&r=all |
| By: | Melo-Velandia, Luis Fernando; Loaiza, Rubén; Villamizar-Villegas, Mauricio |
| Abstract: | Typically, central banks use a variety of individual models (or a combination of models) when forecasting inflation rates. Most of these require excessive amounts of data, time, and computational power; all of which are scarce when monetary authorities meet to decide over policy interventions. In this paper we use a rolling Bayesian combination technique that considers inflation estimates by the staff of the Central Bank of Colombia during 2002-2011 as prior information. Our results show that: 1) the accuracy of individual models is improved by using a Bayesian shrinkage methodology, and 2) priors consisting of staff's estimates outperform all other priors that comprise equal or zero-vector weights. Consequently, our model provides readily available forecasts that exceed all individual models in terms of forecasting accuracy at every evaluated horizon. |
| Keywords: | Bayesian shrinkage; Inflation forecast combination; Internal forecasts; Rolling window estimation |
| JEL: | C22 C53 C11 E31 |
| Date: | 2019–06 |
| URL: | http://d.repec.org/n?u=RePEc:rie:riecdt:9&r=all |
| By: | Melo-Velandia, Luis Fernando; Loaiza, Rubén; Villamizar-Villegas, Mauricio |
| Abstract: | Typically, central banks use a variety of individual models (or a combination of models) when forecasting inflation rates. Most of these require excessive amounts of data, time, and computational power; all of which are scarce when monetary authorities meet to decide over policy interventions. In this paper we use a rolling Bayesian combination technique that considers inflation estimates by the staff of the Central Bank of Colombia during 2002-2011 as prior information. Our results show that: 1) the accuracy of individual models is improved by using a Bayesian shrinkage methodology, and 2) priors consisting of staff's estimates outperform all other priors that comprise equal or zero-vector weights. Consequently, our model provides readily available forecasts that exceed all individual models in terms of forecasting accuracy at every evaluated horizon. |
| Keywords: | Bayesian shrinkage; Inflation forecast combination; Internal forecasts; Rolling window estimation |
| JEL: | C22 C53 C11 E31 |
| Date: | 2019–06 |
| URL: | http://d.repec.org/n?u=RePEc:rie:riecdt:8&r=all |
| By: | Gerlach, Richard; Naimoli, Antonio; Storti, Giuseppe |
| Abstract: | This paper proposes generalisations of the Realized GARCH model by Hansen et al. (2012), in three different directions. First, heteroskedasticity in the noise term in the measurement equation is allowed, since this is generally assumed to be time-varying as a function of an estimator of the Integrated Quarticity for intra-daily returns. Second, in order to account for attenuation bias effects, the volatility dynamics are allowed to depend on the accuracy of the realized measure. This is achieved by letting the response coefficient of the lagged realized measure depend on the time-varying variance of the volatility measurement error, thus giving more weight to lagged volatilities when they are more accurately measured. Finally, a further extension is proposed by introducing an additional explanatory variable into the measurement equation, aiming to quantify the bias due to effect of jumps and measurement errors. |
| Keywords: | Realized Volatility, Realized GARCH, Measurement Error, Realized Quarticity |
| JEL: | C22 C53 C58 |
| Date: | 2018–01–08 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:94289&r=all |
| By: | Pierrick Piette (SAF - Laboratoire de Sciences Actuarielle et Financière - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon, LPSM UMR 8001 - Laboratoire de Probabilités, Statistique et Modélisation - UPD7 - Université Paris Diderot - Paris 7 - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique) |
| Abstract: | On the one hand, recent advances in satellite imagery and remote sensing allow one to easily follow in near-real time the crop conditions all around the world. On the other hand, it has been shown that governmental agricultural reports contain useful news for the commodities market, whose participants react to this valuable information. In this paper, we investigate wether one can forecast some of the newsworthy information contained in the USDA reports through satellite data. We focus on the corn futures market over the period 2000-2016. We first check the well-documented presence of market reactions to the release of the monthly WASDE reports through statistical tests. Then we investigate the informational value of early yield estimates published in these governmental reports. Finally, we propose an econometric model based on MODIS NDVI time series to forecast this valuable information. Results show that market rationally reacts to the NASS early yield forecasts. Moreover, the modeled NDVI-based information is signicantly correlated with the market reactions. To conclude, we propose some ways of improvement to be considered for a practical implementation. |
| Keywords: | NDVI,USDA reports,MODIS,Market information,Corn,Commodities market |
| Date: | 2019–06–06 |
| URL: | http://d.repec.org/n?u=RePEc:hal:wpaper:hal-02149355&r=all |
| By: | Simon Schn\"urch; Andreas Wagner |
| Abstract: | This paper employs machine learning algorithms to forecast German electricity spot market prices. The forecasts utilize in particular bid and ask order book data from the spot market but also fundamental market data like renewable infeed and expected demand. Appropriate feature extraction for the order book data is developed. Using cross-validation to optimise hyperparameters, neural networks and random forests are proposed and compared to statistical reference models. The machine learning models outperform traditional approaches. |
| Date: | 2019–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1906.06248&r=all |
| By: | Roni Michaely (University of Geneva - Geneva Finance Research Institute (GFRI); Swiss Finance Institute); Amir Rubin (Simon Fraser University (SFU) - Beedie School of Business; Interdisciplinary Center (IDC) Herzliyah); Dan Segal (Interdisciplinary Center (IDC) Herzliyah); Alexander Vedrashko (Simon Fraser University - Beedie School of Business) |
| Abstract: | We find that investors are fixated on analysts’ consensus outputs (earnings forecasts, recommendations, and forecast dispersion), which can be inferior signals compared to the corresponding outputs provided by high-quality analysts, especially when a large number of high-quality analysts follow the firm. This result, which holds at the firm and market level, implies inefficient use of the information contained in analysts’ outputs. Further, the post-earnings announcement drift (PEAD) phenomenon occurs only when high-quality analysts are more uncertain about the firm’s performance than all analysts following the firm. We conclude that the market’s fixation on consensus measures has significant negative economic implications. |
| Keywords: | consensus, analyst quality, forecasts, post-earnings announcement drift, stock recommendations |
| JEL: | G10 G11 G14 G17 G24 M41 |
| Date: | 2019–03 |
| URL: | http://d.repec.org/n?u=RePEc:chf:rpseri:rp1906&r=all |