OPUS 4 Latest Documents RSS FeedLatest documents
http://publikationen.ub.uni-frankfurt.de/index/index/
Mon, 02 Nov 2015 11:17:46 +0100Mon, 02 Nov 2015 11:17:46 +0100Understanding the decline in the price of oil since june 2014
http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/36848
Some observers have conjectured that oil supply shocks in the United States and in other countries are behind the plunge in the price of oil since June 2014. Others have suggested that a major shock to oil price expectations occurred when in late November 2014 OPEC announced that it would maintain current production levels despite the steady increase in non-OPEC oil production. Both conjectures are perfectly reasonable ex ante, yet we provide quantitative evidence that neither explanation appears supported by the data. We show that more than half of the decline in the price of oil was predictable in real time as of June 2014 and therefore must have reflected the cumulative effects of earlier oil demand and supply shocks. Among the shocks that occurred after June 2014, the most influential shock resembles a negative shock to the demand for oil associated with a weakening economy in December 2014. In contrast, there is no evidence of any large positive oil supply shocks between June and December. We conclude that the difference in the evolution of the price of oil, which declined by 44% over this period, compared with other commodity prices, which on average only declined by about 5%-15%, reflects oil-market specific developments that took place prior to June 2014.Christiane Baumeister; Lutz Kilianworkingpaperhttp://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/36848Wed, 11 Feb 2015 11:17:46 +0100Inside the crystal ball: new approaches to predicting the gasoline price at the pump
http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/36381
Although there is much interest in the future retail price of gasoline among consumers, industry analysts, and policymakers, it is widely believed that changes in the price of gasoline are essentially unforecastable given publicly available information. We explore a range of new forecasting approaches for the retail price of gasoline and compare their accuracy with the no-change forecast. Our key finding is that substantial reductions in the mean-squared prediction error (MSPE) of gasoline price forecasts are feasible in real time at horizons up to two years, as are substantial increases in directional accuracy. The most accurate individual model is a VAR(1) model for real retail gasoline and Brent crude oil prices. Even greater reductions in MSPEs are possible by constructing a pooled forecast that assigns equal weight to five of the most successful forecasting models. Pooled forecasts have lower MSPE than the EIA gasoline price forecasts and the gasoline price expectations in the Michigan Survey of Consumers. We also show that as much as 39% of the decline in gas prices between June and December 2014 was predictable.Christiane Baumeister; Lutz Kilian; Thomas K. Leeworkingpaperhttp://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/36381Wed, 21 Jan 2015 16:02:13 +0100Marginalized predictive likelihood comparisons of linear Gaussian state-space models with applications to DSGE, DSGE-VAR, and VAR models
http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/35110
he predictive likelihood is of particular relevance in a Bayesian setting when the purpose is to rank models in a forecast comparison exercise. This paper discusses how the predictive likelihood can be estimated for any subset of the observable variables in linear Gaussian state-space models with Bayesian methods, and proposes to utilize a missing observations consistent Kalman filter in the process of achieving this objective. As an empirical application, we analyze euro area data and compare the density forecast performance of a DSGE model to DSGE-VARs and reduced-form linear Gaussian models.Anders Warne; Günter Coenen; Kai Christoffelworkingpaperhttp://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/35110Mon, 20 Oct 2014 15:27:07 +0200A general approach to recovering market expectations from futures prices with an application to crude oil
http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/34902
Futures markets are a potentially valuable source of information about market expectations. Exploiting this information has proved difficult in practice, because the presence of a time-varying risk premium often renders the futures price a poor measure of the market expectation of the price of the underlying asset. Even though the expectation in principle may be recovered by adjusting the futures price by the estimated risk premium, a common problem in applied work is that there are as many measures of market expectations as there are estimates of the risk premium. We propose a general solution to this problem that allows us to uniquely pin down the best possible estimate of the market expectation for any set of risk premium estimates. We illustrate this approach by solving the long-standing problem of how to recover the market expectation of the price of crude oil. We provide a new measure of oil price expectations that is considerably more accurate than the alternatives and more economically plausible. We discuss implications of our analysis for the estimation of economic models of energy-intensive durables, for the debate on speculation in oil markets, and for oil price forecasting.Christiane Baumeister; Lutz Kilianworkingpaperhttp://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/34902Mon, 29 Sep 2014 08:30:58 +0200Evaluating point and density forecasts of DSGE models : [Version 23 Januar 2012]
http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/34398
This paper investigates the accuracy of point and density forecasts of four DSGE models for inflation, output growth and the federal funds rate. Model parameters are estimated and forecasts are derived successively from historical U.S. data vintages synchronized with the Fed’s Greenbook projections. Point forecasts of some models are of similar accuracy as the forecasts of nonstructural large dataset methods. Despite their common underlying New Keynesian modeling philosophy, forecasts of different DSGE models turn out to be quite distinct. Weighted forecasts are more precise than forecasts from individual models. The accuracy of a simple average of DSGE model forecasts is comparable to Greenbook projections for medium term horizons. Comparing density forecasts of DSGE models with the actual distribution of observations shows that the models overestimate uncertainty around point forecasts.Maik Hendrik Woltersreporthttp://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/34398Tue, 29 Jul 2014 14:39:17 +0200Evaluating point and density forecasts of DSGE models : [Version 4 September 2012]
http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/34712
This paper investigates the accuracy of forecasts from four DSGE models for inflation, output growth and the federal funds rate using a real-time dataset synchronized with the Fed’s Greenbook projections. Conditioning the model forecasts on the Greenbook nowcasts leads to forecasts that are as accurate as the Greenbook projections for output growth and the federal funds rate. Only for inflation the model forecasts are dominated by the Greenbook projections. A comparison with forecasts from Bayesian VARs shows that the economic structure of the DSGE models which is useful for the interpretation of forecasts does not lower the accuracy of forecasts. Combining forecasts of several DSGE models increases precision in comparison to individual model forecasts. Comparing density forecasts with the actual distribution of observations shows that DSGE models overestimate uncertainty around point forecasts.Maik Hendrik Woltersworkingpaperhttp://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/34712Tue, 29 Jul 2014 14:16:57 +0200Do high-frequency financial data help forecast oil
prices? The MIDAS touch at work : [version november 20, 2013]
http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/32499
The substantial variation in the real price of oil since 2003 has renewed interest in the question of how to forecast monthly and quarterly oil prices. There also has been increased interest in the link between financial markets and oil markets, including the question of whether financial market information helps forecast the real price of oil in physical markets. An obvious advantage of financial data in forecasting oil prices is their availability in real time on a daily or weekly basis. We investigate whether mixed-frequency models may be used to take advantage of these rich data sets. We show that, among a range of alternative high-frequency predictors, especially changes in U.S. crude oil inventories produce substantial and statistically significant real-time improvements in forecast accuracy. The preferred MIDAS model reduces the MSPE by as much as 16 percent compared with the no-change forecast and has statistically significant directional accuracy as high as 82 percent. This MIDAS forecast also is more accurate than a mixed-frequency realtime VAR forecast, but not systematically more accurate than the corresponding forecast based on monthly inventories. We conclude that typically not much is lost by ignoring high-frequency financial data in forecasting the monthly real price of oil.Christiane Baumeister; Pierre Guérin; Lutz Kilianworkingpaperhttp://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/32499Mon, 16 Dec 2013 09:22:49 +0100Forecasting the real price of oil in a changing world: a forecast combination approach : [Version November 13, 2013]
http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/32488
The U.S. Energy Information Administration (EIA) regularly publishes monthly and quarterly forecasts of the price of crude oil for horizons up to two years, which are widely used by practitioners. Traditionally, such out-of-sample forecasts have been largely judgmental, making them difficult to replicate and justify. An alternative is the use of real-time econometric oil price forecasting models. We investigate the merits of constructing combinations of six such models. Forecast combinations have received little attention in the oil price forecasting literature to date. We demonstrate that over the last 20 years suitably constructed real-time forecast combinations would have been systematically more accurate than the no-change forecast at horizons up to 6 quarters or 18 months. MSPE reduction may be as high as 12% and directional accuracy as high as 72%. The gains in accuracy are robust over time. In contrast, the EIA oil price forecasts not only tend to be less accurate than no-change forecasts, but are much less accurate than our preferred forecast combination. Moreover, including EIA forecasts in the forecast combination systematically lowers the accuracy of the combination forecast. We conclude that suitably constructed forecast combinations should replace traditional judgmental forecasts of the price of oil.Christiane Baumeister; Lutz Kilianworkingpaperhttp://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/32488Fri, 13 Dec 2013 09:20:37 +0100Are product spreads useful for forecasting? An empirical evaluation of the Verleger hypothesis
http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/32486
Notwithstanding a resurgence in research on out-of-sample forecasts of the price of oil in recent years, there is one important approach to forecasting the real price of oil which has not been studied systematically to date. This approach is based on the premise that demand for crude oil derives from the demand for refined products such as gasoline or heating oil. Oil industry analysts such as Philip Verleger and financial analysts widely believe that there is predictive power in the product spread, defined as the difference between suitably weighted refined product market prices and the price of crude oil. Our objective is to evaluate this proposition. We derive from first principles a number of alternative forecasting model specifications involving product spreads and compare these models to the no-change forecast of the real price of oil. We show that not all product spread models are useful for out-of-sample forecasting, but some models are, even at horizons between one and two years. The most accurate model is a time-varying parameter model of gasoline and heating oil spot spreads that allows the marginal product market to change over time. We document MSPE reductions as high as 20% and directional accuracy as high as 63% at the two-year horizon, making product spread models a good complement to forecasting models based on economic fundamentals, which work best at short horizons.Christiane Baumeister; Lutz Kilian; Xiaoqing Zhouworkingpaperhttp://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/32486Fri, 13 Dec 2013 09:18:46 +0100Atypical behavior of credit: evidence from a monetary VAR
http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/30583
Credit boom detection methodologies (such as threshold method) lack robustness as they are based on univariate detrending analysis and resort to ratios of credit to real activity. I propose a quantitative indicator to detect atypical behavior of credit from a multivariate system - a monetary VAR. This methodology explicitly accounts for endogenous interactions between credit, asset prices and real activity and detects atypical credit expansions and contractions in the Euro Area, Japan and the U.S. robustly and timely. The analysis also proves useful in real time.Elena Afanasyevaworkingpaperhttp://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/30583Fri, 28 Jun 2013 13:56:40 +0200Forecasting and policy making
http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/26875
Volker Wieland; Maik Hendrik Woltersworkingpaperhttp://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/26875Wed, 07 Nov 2012 17:12:25 +0100Evaluating point and density forecasts of DSGE models : [Version 13 März 2012]
http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/26872
This paper investigates the accuracy of point and density forecasts of four DSGE models for inflation, output growth and the federal funds rate. Model parameters are estimated and forecasts are derived successively from historical U.S. data vintages synchronized with the Fed’s Greenbook projections. Point forecasts of some models are of similar accuracy as the forecasts of nonstructural large dataset methods. Despite their common underlying New Keynesian modeling philosophy, forecasts of different DSGE models turn out to be quite distinct. Weighted forecasts are more precise than forecasts from individual models. The accuracy of a simple average of DSGE model forecasts is comparable to Greenbook projections for medium term horizons. Comparing density forecasts of DSGE models with the actual distribution of observations shows that the models overestimate uncertainty around point forecasts.Maik Hendrik Woltersworkingpaperhttp://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/26872Wed, 07 Nov 2012 17:01:50 +0100Modelling and forecasting liquidity supply using semiparametric factor dynamics
http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/7061
We model the dynamics of ask and bid curves in a limit order book market using a dynamic semiparametric factor model. The shape of the curves is captured by a factor structure which is estimated nonparametrically. Corresponding factor loadings are assumed to follow multivariate dynamics and are modelled using a vector autoregressive model. Applying the framework to four stocks traded at the Australian Stock Exchange (ASX) in 2002, we show that the suggested model captures the spatial and temporal dependencies of the limit order book. Relating the shape of the curves to variables reflecting the current state of the market, we show that the recent liquidity demand has the strongest impact. In an extensive forecasting analysis we show that the model is successful in forecasting the liquidity supply over various time horizons during a trading day. Moreover, it is shown that the model’s forecasting power can be used to improve optimal order execution strategies.Wolfgang Karl Härdle; Nikolaus Hautsch; Andrija Mihociworkingpaperhttp://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/7061Sun, 20 Sep 2009 13:37:34 +0200European Securitisation : a GARCH model of CDO, MBS and Pfandbrief spreads
http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/3884
Asset-backed securitisation (ABS) is an asset funding technique that involves the issuance of structured claims on the cash flow performance of a designated pool of underlying receivables. Efficient risk management and asset allocation in this growing segment of fixed income markets requires both investors and issuers to thoroughly understand the longitudinal properties of spread prices. We present a multi-factor GARCH process in order to model the heteroskedasticity of secondary market spreads for valuation and forecasting purposes. In particular, accounting for the variance of errors is instrumental in deriving more accurate estimators of time-varying forecast confidence intervals. On the basis of CDO, MBS and Pfandbrief transactions as the most important asset classes of off-balance sheet and on-balance sheet securitisation in Europe we find that expected spread changes for these asset classes tends to be level stationary with model estimates indicating asymmetric mean reversion. Furthermore, spread volatility (conditional variance) is found to follow an asymmetric stochastic process contingent on the value of past residuals. This ABS spread behaviour implies negative investor sentiment during cyclical downturns, which is likely to escape stationary approximation the longer this market situation lasts.Andreas A. Jobstworkingpaperhttp://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/3884Mon, 26 Sep 2005 08:05:31 +0200Forecasting stock market volatility and the informational efficiency of the DAX-index options market
http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/4499
Alternative strategies for predicting stock market volatility are examined. In out-of-sample forecasting experiments implied-volatility information, derived from contemporaneously observed option prices or history-based volatility predictors, such as GARCH models, are investigated, to determine if they are more appropriate for predicting future return volatility. Employing German DAX-index return data it is found that past returns do not contain useful information beyond the volatility expectations already reflected in option prices. This supports the efficient market hypothesis for the DAX-index options market.Holger Claessen; Stefan Mittnikworkingpaperhttp://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/4499Mon, 13 Jun 2005 09:08:16 +0200Predicting recessions with interest rate spreads: a multicountry regime-switching analysis
http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/4529
This study uses Markov-switching models to evaluate the informational content of the term structure as a predictor of recessions in eight OECD countries. The empirical results suggest that for all countries the term spread is sensibly modelled as a two-state regime-switching process. Moreover, our simple univariate model turns out to be a filter that transforms accurately term spread changes into turning point predictions. The term structure is confirmed to be a reliable recession indicator. However, the results of probit estimations show that the markov-switching filter does not significantly improve the forecasting ability of the spread.Ralf Ahrensworkingpaperhttp://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/4529Mon, 13 Jun 2005 08:58:01 +0200Improving market-based forecasts of short-term interest rates: time-varying stationarity and the predictive content of switching regime-expectations
http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/4528
Modeling short-term interest rates as following regime-switching processes has become increasingly popular. Theoretically, regime-switching models are able to capture rational expectations of infrequently occurring discrete events. Technically, they allow for potential time-varying stationarity. After discussing both aspects with reference to the recent literature, this paper provides estimations of various univariate regime-switching specifications for the German three-month money market rate and bivariate specifications additionally including the term spread. However, the main contribution is a multi-step out-of-sample forecasting competition. It turns out that forecasts are improved substantially when allowing for state-dependence. Particularly, the informational content of the term spread for future short rate changes can be exploited optimally within a multivariate regime-switching framework.Ralf Ahrensworkingpaperhttp://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/4528Mon, 13 Jun 2005 08:57:45 +0200