nep-for New Economics Papers
on Forecasting
Issue of 2012‒12‒06
thirteen papers chosen by
Rob J Hyndman
Monash University

  1. Stock Return and Cash Flow Predictability: The Role of Volatility Risk By Tim Bollerslev; Lai Xu; Hao Zhou
  2. Forecasting with a noncausal VAR model By Nyberg , Henri; Saikkonen, Pentti
  3. Time-varying Combinations of Predictive Densities using Nonlinear Filtering By Monica Billio; Roberto Casarin; Francesco Ravazzolo; Herman K. van Dijk
  4. Interest Rate Forecasts in Inflation Targeting Open-Economies By Alessandro Flamini
  5. Evaluating a Global Vector Autoregression for Forecasting By Neil R. Ericsson; Erica L. Reisman
  6. Persistence and Cycles in the US Federal Funds Rate By Guglielmo Maria Caporale; Luis A. Gil-Alana
  7. Multivariate wishart stochastic volatility and changes in regime By Gribisch, Bastian
  8. The Selection of ARIMA Models with or without Regressors By Søren Johansen; Marco Riani; Anthony C. Atkinson
  9. Nonlinear Kalman Filtering in Affine Term Structure Models By Peter Christoffersen; Christian Dorion; Kris Jacobs; Lotfi Karoui
  10. Forecasting Life Satisfaction Across Adulthood: Benefits of Seeing a Dark Future? By Frieder R. Lang; David Weiss; Denis Gerstorf; Gert G. Wagner
  11. Early warning indicator model of financial developments using an ordered logit By Reimers, Hans-Eggert
  12. Modeling First Line Of An Order Book With Multivariate Marked Point Processes By Alexis Fauth; Ciprian A. Tudor
  13. Changes in the composition of publicly traded firms: Implications for the dividend-price ratio and return predictability By Jank, Stephan

  1. By: Tim Bollerslev (Duke University, NBER and CREATES); Lai Xu (Duke University); Hao Zhou (Federal Reserve Board)
    Abstract: We examine the joint predictability of return and cash flow within a present value framework, by imposing the implications from a long-run risk model that allow for both time-varying volatility and volatility uncertainty. We provide new evidence that the expected return variation and the variance risk premium positively forecast both short-horizon returns and dividend growth rates. We also confirm that dividend yield positively forecasts long-horizon returns, but that it cannot forecast dividend growth rates. Our equilibrium-based “structural” factor GARCH model permits much more accurate inference than the reduced form VAR and univariate regression procedures traditionally employed in the literature. The model also allows for the direct estimation of the underlying economic mechanisms, including a new volatility leverage effect, the persistence of the latent long-run growth component and the two latent volatility factors, as well as the contemporaneous impacts of the underlying “structural” shocks.
    Keywords: Return and dividend growth predictability, variance risk premium, expected variation, long-run risk, equilibrium pricing, stochastic volatility and uncertainty, reduced form VAR, “structural” factor GARCH
    JEL: G12 G13 C12 C13
    Date: 2012–11–16
  2. By: Nyberg , Henri (Department of Political and Economic Studies, and HECER, University of Helsinki); Saikkonen, Pentti (Department of Mathematics and Statistics, and HECER, University of Helsinki, and the Monetary Policy and Research Department of the Bank of Finland)
    Abstract: We propose simulation-based forecasting methods for the noncausal vector autoregressive model proposed by Lanne and Saikkonen (2012). Simulation or numerical methods are required because the prediction problem is generally nonlinear and, therefore, its analytical solution is not available. It turns out that different special cases of the model call for different simulation procedures. Simulation experiments demonstrate that gains in forecasting accuracy are achieved by using the correct noncausal VAR model instead of its conventional causal counterpart. In an empirical application, a noncausal VAR model comprised of U.S. inflation and marginal cost turns out superior to the best-fitting conventional causal VAR model in forecasting inflation.
    Keywords: noncausal vector autoregression; forecasting; simulation; importance sampling; inflation
    JEL: C32 C53 E31
    Date: 2012–11–09
  3. By: Monica Billio (University of Venice, GRETA Assoc. and School for Advanced Studies in Venice); Roberto Casarin (University of Venice, GRETA Assoc. and School for Advanced Studies in Venice); Francesco Ravazzolo (Norges Bank and BI Norwegian Business School); Herman K. van Dijk (Erasmus University Rotterdam, VU University Amsterdam)
    Abstract: We propose a Bayesian combination approach for multivariate predictive densities which relies upon a distributional state space representation of the combination weights. Several specifications of multivariate time-varying weights are introduced with a particular focus on weight dynamics driven by the past performance of the predictive densities and the use of learning mechanisms. In the proposed approach the model set can be incomplete, meaning that all models can be individually misspecified. A Sequential Monte Carlo method is proposed to approximate the filtering and predictive densities. The combination approach is assessed using statistical and utility-based performance measures for evaluating density forecasts. Simulation results indicate that, for a set of linear autoregressive models, the combination strategy is successful in selecting, with probability close to one, the true model when the model set is complete and it is able to detect parameter instability when the model set includes the true model that has generated subsamples of data. For the macro series we find that incompleteness of the models is relatively large in the 70's, the beginning of the 80's and during the recent financial crisis, and lower during the Great Moderation. With respect to returns of the S&P 500 series, we find that an investment strategy using a combination of predictions from professional forecasters and from a white noise model puts more weight on the white noise model in the beginning of the 90's and switches to giving more weight to the professional forecasts over time.
    Keywords: Density Forecast Combination; Survey Forecast; Bayesian Filtering; Sequential Monte Carlo
    JEL: C11 C15 C53 E37
    Date: 2012–11–07
  4. By: Alessandro Flamini (Department of Economics and Management, University of Pavia)
    Date: 2012–11
  5. By: Neil R. Ericsson (Board of Governors of the Federal Reserve System); Erica L. Reisman (Board of Governors of the Federal Reserve System)
    Abstract: Global vector autoregressions (GVARs) have several attractive features: multiple potential channels for the international transmission of macroeconomic and financial shocks, a standardized economically appealing choice of variables for each country or region examined, systematic treatment of long-run properties through cointegration analysis, and flexible dynamic specification through vector error correction modeling. Pesaran, Schuermann, and Smith (2009) generate and evaluate forecasts from a paradigm GVAR with 26 countries, based on Dées, di Mauro, Pesaran, and Smith (2007). The current paper empirically assesses the GVAR in Dées, di Mauro, Pesaran, and Smith (2007) with impulse indicator saturation (IIS)—a new generic procedure for evaluating parameter constancy, which is a central element in model-based forecasting. The empirical results indicate substantial room for an improved, more robust specification of that GVAR. Some tests are suggestive of how to achieve such improvements.
    Keywords: cointegration, error correction, forecasting, GVAR, impulse indicator saturation, model design, model evaluation, model selection, parameter constancy, VAR
    JEL: C32 F41
    Date: 2012–11
  6. By: Guglielmo Maria Caporale; Luis A. Gil-Alana
    Abstract: This paper uses long-range dependence techniques to analyse two important features of the US Federal Funds effective rate, namely its persistence and cyclical behaviour. It examines annual, monthly, bi-weekly and weekly data, from 1954 until 2010. Two models are considered. One is based on an I(d) specification with AR(2) disturbances and the other on two fractional differencing structures, one at the zero and the other at a cyclical frequency. Thus, the two approaches differ in the way the cyclical component of the process is modelled. In both cases we obtain evidence of long memory and fractional integration. The in-sample goodness-of-fit analysis supports the second specification in the majority of cases. An out-of-sample forecasting experiment also suggests that the long-memory model with two fractional differencing parameters is the most adequate one, especially over long horizons.
    Keywords: Federal Funds rate, persistence, cyclical behaviour, fractional integration
    JEL: C32 E43
    Date: 2012
  7. By: Gribisch, Bastian
    Abstract: This paper generalizes the basic Wishart multivariate stochastic volatility model of Philipov and Glickman (2006) and Asai and McAleer (2009) to encompass regime switching behavior. The latent state variable is driven by a first-order Markov process. The model allows for state-dependent (co)variance and correlation levels and state-dependent volatility spillover effects. Parameter estimates are obtained using Bayesian Markov Chain Monte Carlo procedures and filtered estimates of the latent variances and covariances are generated by particle filter techniques. The model is applied to five European stock index return series. The results show that the proposed regime-switching specification substantially improves the in-sample fit and the VaR forecasting performance relative to the basic model. --
    Keywords: Multivariate stochastic volatility,Dynamic correlations,Wishart distribution,Markov switching,Markov chain Monte Carlo
    JEL: C32 C58 G17
    Date: 2012
  8. By: Søren Johansen (Department of Economics, University of Copenhagen and CREATES, University of Aarhus); Marco Riani (Dipartimento di Economia, Universita di Parma); Anthony C. Atkinson (Department of Statistics, London School of Economics, UK)
    Abstract: We develop a Cp statistic for the selection of regression models with stationary and nonstationary ARIMA error term. We derive the asymptotic theory of the maximum likelihood estimators and show they are consistent and asymptotically Gaussian. We also prove that the distribution of the sum of squares of one step ahead standardized prediction errors, when the parameters are estimated, differs from the chi-squared distribution by a term which tends to infinity at a lower rate than X (2/n). We further prove that, in the prediction error decomposition, the term involving the sum of the variance of one step ahead standardized prediction errors is convergent. Finally, we provide a small simulation study. Empirical comparisons of a consistent version of our Cp statistic with BIC and a generalized RIC show that our statistic has superior performance, particularly for small signal to noise ratios. A new plot of our time series Cp statistic is highly informative about the choice of model. On the way we introduce a new version of AIC for regression models, show that it estimates a Kullback-Leibler distance and provide a version for small samples that is bias corrected. We highlight the connections with standard Mallows Cp.
    Keywords: AIC; ARMA models; bias correction; BIC; Cp plot; generalized RIC; Kalman filter; Kullback-Leibler distance; state-space formulation
    JEL: C22
    Date: 2012–11–08
  9. By: Peter Christoffersen (University of Toronto - Rotman School of Management and CREATES); Christian Dorion (HEC Montreal); Kris Jacobs (University of Houston and Tilburg University); Lotfi Karoui (Goldman, Sachs & Co.)
    Abstract: When the relationship between security prices and state variables in dynamic term structure models is nonlinear, existing studies usually linearize this relationship because nonlinear fi?ltering is computationally demanding. We conduct an extensive investigation of this linearization and analyze the potential of the unscented Kalman ?filter to properly capture nonlinearities. To illustrate the advantages of the unscented Kalman ?filter, we analyze the cross section of swap rates, which are relatively simple non-linear instruments, and cap prices, which are highly nonlinear in the states. An extensive Monte Carlo experiment demonstrates that the unscented Kalman fi?lter is much more accurate than its extended counterpart in fi?ltering the states and forecasting swap rates and caps. Our fi?ndings suggest that the unscented Kalman fi?lter may prove to be a good approach for a number of other problems in fi?xed income pricing with nonlinear relationships between the state vector and the observations, such as the estimation of term structure models using coupon bonds and the estimation of quadratic term structure models.
    Keywords: Kalman filtering, nonlinearity, term structure models, swaps, caps.
    JEL: G12
    Date: 2012–05–14
  10. By: Frieder R. Lang; David Weiss; Denis Gerstorf; Gert G. Wagner
    Abstract: Anticipating one’s future self is a unique human capacity that contributes importantly to adaptation and health throughout adulthood and old age. Using the adult lifespan sample of the national German Socio-Economic Panel (SOEP; N > 10,000, age range 18-96 years), we investigated age-differential stability, correlates, and outcomes of accuracy in anticipation of future life satisfaction across six subsequent 5-year time intervals. As expected, we observed few age differences in current life satisfaction, but stronger age differences in future expectations: Younger adults anticipated improved future life satisfaction, overestimating their actual life satisfaction 5 years later. By contrast, older adults were more pessimistic about the future, generally underestimating their actual life satisfaction after 5 years. Such age differences persisted above and beyond the effects of self-rated health and income. Survival analyses revealed that in later adulthood, underestimating one’s life satisfaction 5 years later was related to lower hazard ratios for disability (n = 735 became disabled) and mortality (n = 879 died) across 10 or more years, even after controlling for age, sex, education, income, and self-rated health. Findings suggest that older adults are more likely to underestimate their life satisfaction in the future, and that such underestimation was associated with positive health outcomes.
    Keywords: Subjective well-being, future anticipation, optimism, aging, health, mortality, disability, SOEP
    Date: 2012
  11. By: Reimers, Hans-Eggert
    Abstract: The recent financial crisis has demonstrated in an impressive way that boom/bust cycles can have devastating effects on the real economy. This paper aims at contributing to the literature on early warning indicator exercises for asset price development. Using a sample of 17 industrialised OECD countries and the euro area over the period 1969 Q1 - 2011 Q2, an asset price composite indicator incorporating developments in both stock and house price markets is constructed. The latter is then further developed in order to identify periods that can be characterised as asset price booms and busts. The subsequent empirical analysis is based on an ordered logit-type approach incorporating several monetary, financial and real variables. Following some statistical tests, credit aggregates, the interest rate spread together with the house price growth gap and stock price developments appear to be useful indicators for the prediction of asset price developments. --
    JEL: E37 E44 E51 G01
    Date: 2012
  12. By: Alexis Fauth (SAMM); Ciprian A. Tudor (LPP)
    Abstract: We introduce a new model in order to describe the fluctuation of tick-by-tick financial time series. Our model, based on marked point process, allows us to incorporate in a unique process the duration of the transaction and the corresponding volume of orders. The model is motivated by the fact that the "excitation" of the market is different in periods of time with low exchanged volume and high volume exchanged. We illustrate our result by numerical simulations on foreign exchange data sampling in millisecond. By checking the main stylized facts, we show that the model is consistent with the empirical data. We also find an interesting relation between the distribution of the volume of limited order and the volume of market orders. To conclude, we propose an application to risk management and we introduce a forecast procedure.
    Date: 2012–11
  13. By: Jank, Stephan
    Abstract: This article documents how the changing composition of U.S. publicly traded firms has prompted a decline in the long-run mean of the aggregate dividend-price ratio, most notably since the 1970s. Adjusting the dividend-price ratio for such changes resolves several issues with respect to the predictability of stock market returns: The adjusted dividend-price ratio is less persistent, in-sample evidence for predictability is more pronounced, there is greater parameter stability in the predictive regression (particularly during the 1990s), and there is evidence of out-of-sample predictability. --
    Keywords: return predictability,dividend-price ratio,payout policy,sample selection,choice of organizational structure
    JEL: G10 G12 G14 G35
    Date: 2012

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