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on Forecasting |
| By: | Massimiliano Marcellino (European University Institute and Bocconi University); Mario Porqueddu (Bank of Italy); Fabrizio Venditti (Bank of Italy) |
| Abstract: | In this paper we develop a mixed frequency dynamic factor model featuring stochastic shifts in the volatility of both the latent common factor and the idiosyncratic components. We take a Bayesian perspective and derive a Gibbs sampler to obtain the posterior density of the model parameters. This new tool is then used to investigate business cycle dynamics and to forecast GDP growth at short-term horizons in the euro area. We discuss three sets of empirical results. First, we use the model to evaluate the impact of macroeconomic releases on point and density forecast accuracy and on the width of forecast intervals. Second, we show how our setup allows us to make a probabilistic assessment of the contribution of releases to forecast revisions. Third, we design a pseudo out-of-sample forecasting exercise and examine point and density forecast accuracy. In line with findings in literature on Bayesian Vector Autoregressions (BVAR), we find that stochastic volatility contributes to an improvement in density forecast accuracy. |
| Keywords: | forecasting, business cycle, mixed-frequency data, nonlinear models, nowcasting |
| JEL: | E32 C22 E27 |
| Date: | 2013–01 |
| URL: | http://d.repec.org/n?u=RePEc:bdi:wptemi:td_896_13&r=for |
| By: | Wolfgang Karl Härdle; Brenda López-Cabrera; Matthias Ritter |
| Abstract: | Forecasting based pricing of Weather Derivatives (WDs) is a new approach in valuation of contingent claims on nontradable underlyings. Standard techniques are based on historical weather data. Forward-looking information such as meteorological forecasts or the implied market price of risk (MPR) are often not incorporated. We adopt a risk neutral approach (for each location) that allows the incorporation of meteorological forecasts in the framework of WD pricing. We study weather Risk Premiums (RPs) implied from either the information MPR gain or the meteorological forecasts. The size of RPs is interesting for investors and issuers of weather contracts to take advantages of geographic diversification, hedging effects and price determinations. By conducting an empirical analysis to London and Rome WD data traded at the Chicago Mercantile Exchange (CME), we find out that either incorporating the MPR or the forecast outperforms the standard pricing techniques. |
| Keywords: | Weather derivatives, seasonal variation, temperature, risk premia |
| JEL: | G19 G29 G22 N23 N53 Q59 |
| Date: | 2012–03 |
| URL: | http://d.repec.org/n?u=RePEc:hum:wpaper:sfb649dp2012-027&r=for |
| By: | Matthias Ritter; ; ; |
| Abstract: | Many companies depend on weather conditions, so they require reliable weather forecasts for production planning or risk hedging. In this article, we propose a new way of gaining weather forecasts by exploiting the forward-looking information included in the market prices of weather derivatives traded at the Chicago Mercantile Exchange (CME). For this purpose, the CME futures prices of two monthly temperature indices relevant for the energy sector are compared with index forecasts derived from meteorological temperature forecasts. It turns out that the market prices generally outperform the meteorological forecasts in predicting the outcome of the monthly index. Hence, companies whose prot strongly depends on these indices, such as energy companies, can prot from this additional information source about future weather. |
| Keywords: | Weather derivatives, weather forecasts, CME, energy sector |
| JEL: | G15 G17 Q41 Q47 |
| Date: | 2012–12 |
| URL: | http://d.repec.org/n?u=RePEc:hum:wpaper:sfb649dp2012-067&r=for |
| By: | Mattéo Luciani; Lorenzo Ricci |
| Abstract: | We produce predictions of the previous, the current, and the next quarter of NorwegianGDP. To this end, we estimate a Bayesian Dynamic Factor model on a panel of 14variables (all followed closely by market operators) ranging from 1990 to 2011. By meansof a real time forecasting exercise we show that the Bayesian Dynamic Factor Model outperformsa standard benchmark model, while it performs equally well than the BloombergSurvey. Additionally, we use our model to produce annual GDP growth rate nowcast. Weshow that our annual nowcast outperform the Norges Bank’s projections of current yearGDP. |
| Keywords: | real-time forecasting; bayesian factor model; nowcasting |
| JEL: | C32 C53 E37 |
| Date: | 2013–02 |
| URL: | http://d.repec.org/n?u=RePEc:eca:wpaper:2013/139866&r=for |
| By: | Halkos, George; Kevork, Ilias |
| Abstract: | This paper considers the classical newsvendor model when, (a) demand is autocorrelated, (b) the parameters of the marginal distribution of demand are unknown, and (c) historical data for demand are available for a sample of successive periods. An estimator for the optimal order quantity is developed by replacing in the theoretical formula which gives this quantity the stationary mean and the stationary variance with their corresponding maximum likelihood estimators. The statistical properties of this estimator are explored and general expressions for prediction intervals for the optimal order quantity are derived in two cases: (a) when the sample consists of two observations, and (b) when the sample is considered as sufficiently large. Regarding the asymptotic prediction intervals, specifications of the general expression are obtained for the time-series models AR(1), MA(1), and ARMA(1,1). These intervals are estimated in finite samples using in their theoretical expressions, the sample mean, the sample variance, and estimates of the theoretical autocorrelation coefficients at lag one and lag two. To assess the impact of this estimation procedure on the optimal performance of the newsvendor model, four accuracy implication metrics are considered which are related to: (a) the mean square error of the estimator, (b) the accuracy and the validity of prediction intervals, and (c) the actual probability of running out of stock during the period when the optimal order quantity is estimated. For samples with more than two observations, these metrics are evaluated through simulations, and their values are presented to appropriately constructed tables. The general conclusion is that the accuracy and the validity of the estimation procedure for the optimal order quantity depends upon the critical fractile, the sample size, the autocorrelation level, and the convergence rate of the theoretical autocorrelation function to zero. |
| Keywords: | Newsvendor model; accuracy implication metrics; time-series models; prediction intervals; Monte-Carlo simulations |
| JEL: | C13 M11 C53 C22 M21 |
| Date: | 2013–02–04 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:44189&r=for |
| By: | Laura Gonzalez Cabanillas; Alessio Terzi |
| Abstract: | This paper analyses the Commission's forecast track record, by building on previous analyses. The extension of the observation period to 2011 allows a first analysis of forecast accuracy during the years of the economic and financial crisis. Over the full timespan, forecasts for the EU and euro area are found to be generally unbiased. The same holds true for the outlook for most Member States, largely confirming earlier results. Moreover, the Commission services track record appears generally in line with that of the OECD, IMF and Consensus Economics, and in some cases better. Finally, while the analysis points to a limited impact of the crisis on the accuracy of the Commission's current-year forecasts, a significant deterioration of the accuracy of year-ahead projections is found. This applies in particular for the forecasts of GDP, investment, inflation and the government budget balance, due mainly to larger forecast errors in the recession year 2009, which by all standards proved exceptional and unanticipated by institutional and market forecasters. |
| JEL: | E17 E27 E37 |
| Date: | 2012–12 |
| URL: | http://d.repec.org/n?u=RePEc:euf:ecopap:0476&r=for |
| By: | Kathryn M.E. Dominguez; Matthew D. Shapiro |
| Abstract: | This paper asks whether the slow recovery of the US economy from the trough of the Great Recession was anticipated, and identifies some of the factors that contributed to surprises in the course of the recovery. It constructs a narrative using news reports and government announcements to identify policy and financial shocks. It then compares forecasts and forecast revisions of GDP to the narrative. Successive financial and fiscal shocks emanating from Europe, together with self-inflicted wounds from the political stalemate over the US fiscal situation, help explain the slowing of the pace of an already slow recovery. |
| JEL: | E32 E37 N10 |
| Date: | 2013–02 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:18751&r=for |
| By: | Laura Coroneo; Domenico Giannone; Michèle Modugno |
| Abstract: | We show that two macroeconomic factors have an important predictive content for governmentbond yields and excess returns. These factors are not spanned by the cross-section of yields andare well proxied by economic growth and real interest rates. |
| Keywords: | Yield curve; Government Bonds; factor models; forecasting |
| JEL: | C33 C53 E43 E44 G12 |
| Date: | 2013–01 |
| URL: | http://d.repec.org/n?u=RePEc:eca:wpaper:2013/138904&r=for |
| By: | Nikolaus Hautsch; Julia Schaumburg; Melanie Schienle; |
| Abstract: | We propose a methodology for forecasting the systemic impact of financial institutions in interconnected systems. Utilizing a five-year sample including the 2008/9 financial crisis, we demonstrate how the approach can be used for timely systemic risk monitoring of large European banks and insurance companies. We predict firms’ systemic relevance as the marginal impact of individual downside risks on systemic distress. The so-called systemic risk betas account for a company’s position within the network of financial interdependencies in addition to its balance sheet characteristics and its exposure towards general market conditions. Relying only on publicly available daily market data, we determine time-varying systemic risk networks, and forecast systemic relevance on a quarterly basis. Our empirical findings reveal time-varying risk channels and firms’ specific roles as risk transmitters and/or risk recipients. |
| Keywords: | Forecasting systemic risk contributions, time-varying systemic risk network, model selection with regularization in quantiles |
| JEL: | G01 G18 G32 G38 C21 C51 C63 |
| Date: | 2013–01 |
| URL: | http://d.repec.org/n?u=RePEc:hum:wpaper:sfb649dp2013-008&r=for |
| By: | Carlos A. Medel; Sergio C. Salgado |
| Abstract: | We test two questions: (i) Is the Bayesian information criterion (BIC) more parsimonious than the Akaike information criterion (AIC)?, and (ii) Can the BIC forecast better than the AIC? By using simulated data, we provide statistical inference of both hypotheses individually and then jointly with a multiple hypotheses testing procedure to control better for type-I error. Both testing procedures deliver the same result: The BIC shows an in- and out-of-sample superiority over AIC only in a long-sample context. |
| Date: | 2012–11 |
| URL: | http://d.repec.org/n?u=RePEc:chb:bcchwp:679&r=for |
| By: | David Pacini |
| Abstract: | This paper investigates the identification and estimation of the least square linear predictor for the conditional expectation of an outcome variable Y given covariates (X;Z0) from data consisting of two independent random samples; the first sample contains replications of the variables (Y;Z0) but not X, while the second sample contains replications of (X;Z0) but not Y . The contribution is to characterize the identified set of the least square linear predictor when no assumption on the joint distribution of (Y;X;Z0), except for the existence of second order moments, is imposed. We show that the identified set is not a singleton, so the least square linear predictor of interest is set identified. The characterization is used to construct a sample analog estimator of the identified set. The asymptotic properties of the estimator are established and its implementation is illustrated via Monte Carlo exercises. |
| Keywords: | Network Identification; Least Square Linear Prediction; Two samples |
| JEL: | C21 C26 |
| Date: | 2012–11 |
| URL: | http://d.repec.org/n?u=RePEc:bri:uobdis:12/631&r=for |
| By: | Bussière, M. |
| Abstract: | The 2008 financial crisis has rekindled interest in the issue of early warning signals (EWS) of financial distress. It has also triggered renewed interest in the literature on currency crises, with many countries, especially among emerging market economies, experiencing severe exchange market pressure. While several policy institutions are in the process of developing new early warning systems, there is a lot of skepticism on the ability to predict currency crises or, more generally, any type of financial crises. This skepticism stems from the alleged poor out-of-sample performance of leading models, but also from a more fundamental objection, according to which it is by definition impossible to predict crises – what can be referred to as a new “impossibility theorem”. Moreover, another criticism of early warning systems is that they may contribute to the phenomenon they are supposed to fight (the self-fulfilling prophecies view). The objective of this paper is to challenge this skeptical view. To this aim, the paper discusses the general conditions under which the “impossibility theorem” may fail and self-fulfilling prophecies can be avoided, stemming e.g. from political economy arguments. The ability of a simple currency crisis model to provide useful information on economic vulnerabilities is illustrated by testing its out-of-sample performance in a panel of emerging market economies following the collapse of Lehman Brothers. |
| Keywords: | Exchange rates, currency crises, financial crises, early warning signals, political economy. |
| JEL: | B40 C52 C53 D72 F31 G01 |
| Date: | 2013 |
| URL: | http://d.repec.org/n?u=RePEc:bfr:banfra:420&r=for |