Forecasting papers 2009-08-01

In this issue we have Forecasting with high frequency based volatility (HEAVY) models, Forecasting Inflation Using Dynamic Model Averaging, Forecasting Performance for US Output Growth and Inflation, Evaluating German Business Cycle Forecasts, Latent Variable Approach to Forecasting the Unemployment Rate and more.

  • Realising the future: forecasting with high frequency based volatility (HEAVY) models
    Date: 2009
    By: Neil Shephard
    Kevin Sheppard
    This paper studies in some detail a class of high frequency based volatility (HEAVY) models. These models are direct models of daily asset return volatility based on realized measures constructed from high frequency data. Our analysis identifies that the models have momentum and mean reversion effects, and that they adjust quickly to structural breaks in the level of the volatility process. We study how to estimate the models and how they perform through the credit crunch, comparing their fit to more traditional GARCH models. We analyse a model based bootstrap which allow us to estimate the entire predictive distribution of returns. We also provide an analysis of missing data in the context of these models.
    Keywords: ARCH models; bootstrap; missing data; multiplicative error model; multistep ahead prediction; non-nested likelihood ratio test; realised kernel; realised volatility.
  • Forecasting Inflation Using Dynamic Model Averaging
    Date: 2009-01
    By: Gary Koop (Department of Economics, University of Strathclyde and RCEA)
    Dimitris Korobilis (Department of Economics, University of Strathclyde and RCEA)
    There is a large literature on forecasting inflation using the generalized Phillips curve (i.e. using forecasting models where inflation depends on past inflation, the unemployment rate and other predictors). The present paper extends this literature through the use of econometric methods which incorporate dynamic model averaging. These not only allow for coefficients to change over time (i.e. the marginal effect of a predictor for inflation can change), but also allows for the entire forecasting model to change over time (i.e. different sets of predictors can be relevant at different points in time). In an empirical exercise involving quarterly US inflation, we fi…nd that dynamic model averaging leads to substantial forecasting improvements over simple benchmark approaches (e.g. random walk or recursive OLS forecasts) and more sophisticated approaches such as those using time varying coefficient models.
    Keywords: Option Pricing; Modular Neural Networks; Non-parametric Methods
    JEL: E31
  • Has Economic Models' Forecasting Performance for US Output Growth and Inflation Changed Over Time, and When?
    Date: 2009
    By: Tatevik Sekhposyan
    Barbara Rossi
    We evaluate various economic models' relative performance in forecasting future US output growth and inflation on a monthly basis. Our approach takes into account the possibility that the models' relative performance can be varying over time. We show that the models' relative performance has, in fact, changed dramatically over time, both for revised and realtime data, and investigate possible factors that might explain such changes. In addition, this paper establishes two empirical stylized facts. Namely, most predictors for output growth lost their predictive ability in the mid-1970s, and became essentially useless in the last two decades. When forecasting inflation, instead, fewer predictors are significant, and their predictive ability significantly worsened around the time of the Great Moderation.
    Keywords: Output Growth Forecasts, Inflation Forecasts, Model Selection, Structural Change, Forecast Evaluation, Real-time data. Evaluation
    JEL: C22
  • Evaluating German Business Cycle Forecasts Under an Asymmetric Loss Function
    Date: 2009-07
    By: Joerg Doepke (University of Applied Sciences Merseburg)
    Ulrich Fritsche (Department for Socioeconomics, Department for Economics, University of Hamburg)
    Boriss Siliverstovs (KOF Swiss Economic Institute, ETH Zurich)
    Based on annual data for growth and inflation forecasts for Germany covering the time span from 1970 to 2007 and up to 17 different forecasts per year, we test for a possible asymmetry of the forecasters' loss function and estimate the degree of asymmetry for each forecasting institution using the approach of Elliot et al. (2005). Furthermore, we test for the rationality of the forecasts under the assumption of a possibly asymmetric loss function and for the features of an optimal forecast under the assumption of a generalized loss function. We find only limited evidence for the existence of an asymmetric loss functions of German forecasters. As regards the rationality of the forecasts the results depend on the underlying assumption of the test. The rationality of inflation forecasts is more doubtful than those of growth forecasts.
    Keywords: Business cycle forecast evaluation, asymmetric loss function, and rational expectations
    JEL: C53
  • A Latent Variable Approach to Forecasting the Unemployment Rate
    Date: 2009-07
    By: C. L. Chua (Melbourne Institute of Applied Economic and Social Research, The University of Melbourne)
    G. C. Lim (Melbourne Institute of Applied Economic and Social Research, The University of Melbourne)
    Sarantis Tsiaplias (Melbourne Institute of Applied Economic and Social Research, The University of Melbourne)
    A forecasting model for unemployment is constructed that exploits the time-series properties of unemployment while satisfying the economic relationships specified by Okun's law and the Phillips curve. In deriving the model, we jointly consider the problem of obtaining estimates of the unobserved potential rate of unemployment consistent with Okun's law and Phillips curve, and associating the potential rate of unemployment to actual unemployment. The empirical example shows that the model clearly outperforms alternative forecasting procedures typically used to forecast unemployment.
    Keywords: Forecasting, Unemployment, Unobserved Components.
    JEL: C53
  • On the realized volatility of the ECX CO2 emissions 2008 futures contract: distribution, dynamics and forecasting
    Date: 2009
    By: Julien Chevallier
    Benoît Sévi
    The recent implementation of the EU Emissions Trading Scheme (EU ETS) in January 2005 created new financial risks for emitting firms. To deal with these risks, options are traded since October 2006. Because the EU ETS is a new market, the relevant underlying model for option pricing is still a controversial issue. This article improves our understanding of this issue by characterizing the conditional and unconditional distributions of the realized volatility for the 2008 futures contract in the European Climate Exchange (ECX), which is valid during Phase II (2008-2012) of the EU ETS. The realized volatility measures from naive, kernel-based and subsampling estimators are used to obtain inferences about the distributional and dynamic properties of the ECX emissions futures volatility. The distribution of the daily realized volatility in logarithmic form is shown to be close to normal. The mixture-of-distributions hypothesi s is strongly rejected, as the returns standardized using daily measures of volatility clearly departs from normality. A simplified HAR-RV model (Corsi, 2009) with only a weekly component, which reproduces long memory properties of the series, is then used to model the volatility dynamics. Finally, the predictive accuracy of the HAR-RV model is tested against GARCH specifications using one-step-ahead forecasts, which confirms the HAR-RV superior ability. Our conclusions indicate that (i) the standard Brownian motion is not an adequate tool for option pricing in the EU ETS, and (ii) a jump component should be included in the stochastic process to price options, thus providing more efficient tools for risk-management activities.
    Keywords: CO2 Price, Realized Volatility, HAR-RV, GARCH, Futures Trading, Emissions Markets, EU ETS, Intraday data, Forecasting
    JEL: C5
  • Forecasting Volatility under Fractality, Regime-Switching, Long Memory and Student-t Innovations
    Date: 2009-07
    By: Thomas Lux
    Leonardo Morales-Arias
    We examine the performance of volatility models that incorporate features such as long (short) memory, regime-switching and multifractality along with two competing distributional assumptions of the error component, i.e. Normal vs Student-t. Our precise contribution is twofold. First, we introduce a new model to the family of Markov-Switching Multifractal models of asset returns (MSM), namely, the Markov-Switching Multifractal model of asset returns with Student-t innovations (MSM-t). Second, we perform a comprehensive panel forecasting analysis of the MSM models as well as other competing volatility models of the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) legacy. Our cross-sections consist of all-share equity indices, bond indices and real estate security indices at the country level. Furthermore, we investigate complementarities between models via combined forecasts. We find that: (i) Maximum Like lihood (ML) and Generalized Method of Moments (GMM) estimation are both suitable for MSM-t models, (ii) empirical panel forecasts of MSM-t models show an improvement over the alternative volatility models in terms of mean absolute forecast errors and that (iii) forecast combinations obtained from the different MSM and (FI)GARCH models considered appear to provide some improvement upon forecasts from single models
    Keywords: Multiplicative volatility models, long memory, Student-t innovations, international volatility forecasting
    JEL: C20
  • A Note on Updating Forecasts When New Information Arrives between Two Periods
    Date: 2009
    By: Chen, Pu
    In this note the author discusses the problem of updating forecasts in a time-discrete forecasting model when information arrives between the current period and the next period. To use the information that arrives between two periods, he assumes that the process between two periods can be approximated by a linear interpolation of the timediscrete forecasting model. Based on this assumption the author drives the optimal updating rule for the forecast of the next period when new information arrives between the current period and the next period. He demonstrates by theoretical arguments and empirical examples that this updating rule is simple, intuitively appealing, defendable and useful.
    Keywords: Forecast
    JEL: C32
  • Do high-frequency measures of volatility improve forecasts of return distributions?
    Date: 2009-01
    By: John M. Maheu (Department of Economics, University of Toronto and RCEA)
    Thomas H. McCurdy (Rotman School of Management, University of Toronto, and CIRANO)
    Many finance questions require the predictive distribution of returns. We propose a bivariate model of returns and realized volatility (RV), and explore which features of that time-series model contribute to superior density forecasts over horizons of 1 to 60 days out of sample. This term structure of density forecasts is used to investigate the importance of: the intraday information embodied in the daily RV estimates; the functional form for log(RV ) dynamics; the timing of information availability; and the assumed distributions of both return and log(RV) innovations. We find that a joint model of returns and volatility that features two components for log(RV) provides a good fit to S&P 500 and IBM data, and is a significant improvement over an EGARCH model estimated from daily returns
    Keywords: Realized Volatility, multiperiod out-of-sample prediction, term structure of density forecasts, Stochastic Volatility
  • Role thinking: Standing in other people's shoes to forecast decisions in conflicts
    Date: 2009-05-30
    By: Green, Kesten C.
    Armstrong, J. Scott
    Better forecasts of decisions in conflict situations, such as occur in business, politics, and war, can help protagonists achieve better outcomes. It is common advice to "stand in the other person's shoes" when involved in a conflict, a procedure we refer to as "role thinking." We tested this advice in order to assess the extent to which it can improve accuracy. Improvement in accuracy is important because prior research found that unaided judgment produced forecasts that were little better than guessing. We obtained 101 role-thinking forecasts from 27 Naval postgraduate students (experts) and 107 role-thinking forecasts from 103 second-year organizational behavior students (novices) of the decisions that would be made in nine diverse conflicts. The accuracy of the forecasts from the novices was 33% and of those from the experts 31%. The accuracy of the role-thinking forecasts was little different from chance, w hich was 28%. In contrast, when we asked groups of participants to each act as if they were in the shoes one of the protagonists, accuracy was 60%.
    Keywords: combining; group decision-making; simulated interaction; unaided judgment
    JEL: D81
  • Extrapolative Projections of Mortality: Towards a More Consistent Method
    Date: 2009-05
    By: Dalkhat M. Ediev
    After a comparative study of the Lee-Carter forecasting method and looking into the direct extrapolation of mortality by age and sex, this paper advocates the use of the latter method. The method is, however, supplemented by additional procedures in order to improve its efficiency in the short run and preclude implausible mortality patterns in the long run. The short-run efficiency is improved by building the forecast on data from the most recent periods of age/sex-specific duration, when the mortality dynamics exhibit a steady trend. In the long run, the rates of the decline in mortality are assumed to converge to a plausible function of age and sex, which is derived from the data based on the assumption that it is a monotonic function of age. The framework proposed also provides a natural basis for developing multi-regional projection methods and also for introducing uncertainty into the projection.
    Keywords: Mortality forecasting, direct extrapolation, age-specific death rates, Lee-Carter method
  • An investigation of customer order flow in the foreign exchange market
    Date: 2009-07
    By: Mario Cerrato
    Nicholas Sarantis
    Alex Saunders
    This paper examines the effect that heterogeneous customer orders flows have on exchange rates by using a new propreitary dataset of weekly net order flow segmented by customer type across nine of the most liquid currency pairs. We make three contributions. First, we investigate the extent to which order flow can help to explain exchange rate movements over and above the influence of macroeconomic variables. Second, we look at the usefulness of order flow in forecasting exchange rate movements at longer horizons than those generally considered in the microstructure literature. Finally we address the question of whether the out-of-sample exchange rate forecasts generated by order flows can be employed profitably in the foreign exchange markets.
    Keywords: Customer order flow; exchange rates; microstructure; forecasting
    JEL: F31
  • The Effects of Age Structure on Economic Growth: An Application of Probabilistic Forecasting in India
    Date: 2009-05
    By: Alexia Prskawetz
    Thomas Kögel
    Warren C. Sanderson
    Sergei Scherbov
    During recent years there has been an increasing awareness of the explanatory power of demographic variables in economic growth regressions. We estimate a new model of the effects of age structure change on economic growth. We use the new model and recent probabilistic demographic projections for India to derive the uncertainty of predicted economic growth rates caused by the uncertainty in demographic developments.
    Keywords: Economic growth, age structure, probabilistic demographic projections, India
  • Taken from the NEP-FOR mailing list edited by Rob Hyndman.