nep-for New Economics Papers
on Forecasting
Issue of 2011‒12‒19
thirteen papers chosen by
Rob J Hyndman
Monash University

  1. Real-Time Analysis of Oil Price Risks Using Forecast Scenarios By Baumeister, Christiane; Kilian, Lutz
  2. Parameter Estimation and Forecasting for Multiplicative Lognormal Cascades By Andrés E. Leövey,Thomas Lux
  3. Forecasting with Option Implied Information By Peter Christoffersen; Kris Jacobs; Bo Young Chang
  4. Investment forecasting with business survey data By Leandro D’Aurizio; Stefano Iezzi
  5. Dynamic Forecasting Rules and the Complexity of Exchange Rate Dynamics By Dewachter, Hans; Houssa, Romain; Lyrio, Marco & Kaltwasser, Pablo Rovira
  6. Testing the Monetary Model for Exchange Rate Determination in South Africa: Evidence from 101 Years of Data By Riane de Bruyn; Rangan Gupta; Lardo Stander
  7. How does the FOMC learn about economic revolutions? evidence from the New Economy Era, 1994-2001 By Richard G. Anderson; Kevin L. Kliesen
  8. Utility-based Forecast Evaluation with Multiple Decision Rules and a New Maxmin Rule By Manuel Lukas
  9. Bayesian analysis of coefficient instability in dynamic regressions By Emanuela Ciapanna; Marco Taboga
  10. Long Memory Dynamics for Multivariate Dependence under Heavy Tails By Pawel Janus; Siem Jan Koopman; André Lucas
  11. A chronology of turning points in economic activity: Spain 1850-2011 By Travis J. Berge; Òscar Jordà
  12. Do Realized Skewness and Kurtosis Predict the Cross-Section of Equity Returns? By Diego Amaya; Peter Christoffersen; Kris Jacobs; Aurelio Vasquez

  1. By: Baumeister, Christiane; Kilian, Lutz
    Abstract: Recently, there has been increased interest in real-time forecasts of the real price of crude oil. Standard oil price forecasts based on reduced-form regressions or based on oil futures prices do not allow consumers of forecasts to explore how much the forecast would change relative to the baseline forecast under alternative scenarios about future oil demand and oil supply conditions. Such scenario analysis is of central importance for end-users of oil price forecasts interested in evaluating the risks underlying these forecasts. We show how policy-relevant forecast scenarios can be constructed from recently proposed structural vector autoregressive models of the global oil market and how changes in the probability weights attached to these scenarios affect the upside and downside risks embodied in the baseline real-time oil price forecast. Such risk analysis helps forecast users understand what assumptions are driving the forecast. An application to real-time data for December 2010 illustrates the use of these tools in conjunction with reduced-form vector autoregressive forecasts of the real price of oil, the superior real-time forecast accuracy of which has recently been established.
    Keywords: Forecast; Oil price; Predictive density; Real time; Risk; Scenario analysis; VAR
    JEL: C53 E32 Q43
    Date: 2011–12
  2. By: Andrés E. Leövey,Thomas Lux
    Abstract: We study the well-known multiplicative Lognormal cascade process in which the multiplication of Gaussian and Lognormally distributed random variables yields time series with intermittent bursts of activity. Due to the non-stationarity of this process and the combinatorial nature of such a formalism, its parameters have been estimated mostly by fitting the numerical approximation of the associated non-Gaussian pdf to empirical data, cf. Castaing et al. [Physica D, 46, 177 (1990)]. More recently, an alternative estimator based upon qth order absolute moments has been introduced by Kiyono et al. [Phys. Rev. E 76 41113 (2007)]. In this paper, we pursue this moment-based approach further and develop a more rigorous Generalized Method of Moments (GMM) estimation procedure to cope with the documented difficulties of previous methodologies. We show that even under uncertainty about the actual number of cascade steps, our methodology yields very reliable results for the estimated intermittency parameter. Employing the Levinson-Durbin algorithm for best linear forecasts, we also show that estimated parameters can be used for forecasting the evolution of the turbulent flow. We compare forecasting results from the GMM and Kiyono et al.'s procedure via Monte Carlo simulations. We finally test the applicability of our approach by estimating the intermittency parameter and forecasting of volatility for a sample of financial data from stock and foreign exchange markets
    Keywords: Random Lognormal cascades, GMM estimation, best linear forecasting, volatility of financial returns
    JEL: C20 G12
    Date: 2011–12
  3. By: Peter Christoffersen (University of Toronto - Rotman School of Management and CREATES); Kris Jacobs (University of Houston - C.T. Bauer College of Business); Bo Young Chang (Bank of Canada)
    Abstract: This chapter surveys the methods available for extracting forward-looking information from option prices. We consider volatility, skewness, kurtosis, and density forecasting. More generally, we discuss how any forecasting object which is a twice differentiable function of the future realization of the underlying risky asset price can utilize option implied information in a well-defi?ned manner. Going beyond the univariate option-implied density, we also consider results on option-implied covariance, correlation and beta forecasting as well as the use of option-implied information in cross-sectional forecasting of equity returns.
    Keywords: Volatility, skewness, kurtosis, density forecasting, risk-neutral.
    JEL: G13 G17 C53
    Date: 2011–12–08
  4. By: Leandro D’Aurizio (Bank of Italy); Stefano Iezzi (Bank of Italy)
    Abstract: Business investment is a very important variable for short- and medium-term economic analysis, but it is volatile and difficult to predict. Qualitative business survey data are widely used to provide indicators of economic activity ahead of the publication of official data. Traditional indicators exploit only aggregate survey information, namely the proportions of respondents who report “up” and “down”. As a consequence, neither the heterogeneity of individual responses nor the panel dimension of microdata is used. We illustrate the use of a disaggregate panel-based indicator that exploits all information coming from two yearly industrial surveys carried out on the same sample of Italian manufacturing firms. Using the same sample allows us to match exactly investment plans and investment realisations for each firm and so estimate a panel data model linking individual investment realisations to investment intentions. The model generates a one-year-ahead forecast of investment variation that follows the aggregate dynamics with a limited bias.
    Keywords: investment plans, dynamic panel data model, forecasting
    JEL: C50 C52 C53
    Date: 2011–11
  5. By: Dewachter, Hans; Houssa, Romain; Lyrio, Marco & Kaltwasser, Pablo Rovira
    Date: 2011–10
  6. By: Riane de Bruyn (Department of Economics, University of Pretoria); Rangan Gupta (Department of Economics, University of Pretoria); Lardo Stander (Department of Economics, University of Pretoria)
    Abstract: Evidence in favour of the monetary model of exchange rate determination for the South African Rand is at best mixed. A co-integrating relationship between the nominal exchange rate and fundamentals forms the basis of the monetary model. With the econometric literature suggesting that it is the span of the data, and not the frequency, that determines the power of the co-integration tests, and all the studies on South Africa using short-span data of the post-Bretton Woods era, we decided to test the long-run monetary model of exchange rate determination for the South African Rand relative to the US Dollar, using annual data from 1910 – 2010. The results provide some support for the monetary model in the sense that long-run co-integration is found between the nominal exchange rate and the output and money supply deviations. However, the theoretical restrictions required by the monetary model are rejected. A vector errorcorrection model identifies both the nominal exchange rate and the monetary fundamentals as the channel for the adjustment process of deviations from the long-run equilibrium exchange rate. A subsequent comparison of nominal exchange rate forecasts based on the monetary model with those of the random walk model, suggests that the forecasting performance of the monetary model is superior.
    Keywords: Nominal exchange rate, monetary model, long-span data, forecasting
    JEL: C22 C32 C53 F31 F47
    Date: 2011–12
  7. By: Richard G. Anderson; Kevin L. Kliesen
    Abstract: Forecasting is a daunting challenge for business economists and policymakers, often made more difficult by pervasive uncertainty. No such uncertainty is more difficult than projecting the reaction of policymakers to major shifts in the economy. We explore the process by which the FOMC came to recognize, and react to, the productivity acceleration of the 1990s. Initial impressions were formed importantly by anecdotal evidence. Then, policymakers—and chiefly Alan Greenspan—came to mistrust the data and the forecasts. Eventually, revisions to published data confirmed initial impressions. Our main conclusion is that the productivity-driven positive supply side shocks of the 1990s were initially viewed favorably. However, over time they came to be viewed as posing a threat to the economy, chiefly through unsustainable increases in aggregate demand growth that threatened to increase inflation pressures. Perhaps nothing so complicates business planning and forecasting as policymakers who initially embrace an unanticipated shift and, later, come to abhor the same shift.
    Keywords: Federal Open Market Committee ; Financial crises ; Productivity
    Date: 2011
  8. By: Manuel Lukas (Aarhus University and CREATES)
    Abstract: In this paper we generalize the existing approach to utility-based evaluation of density forecast models by allowing for multiple decision rules. In the generalized approach forecast models and decision rules can only be evaluated jointly. We show how to conduct the joint evaluation and explore to which extent conclusions about either forecast models or decision rules are possible. As a specic decision rule we introduce a Gilboa-Schmeidler (1989) type multiple-prior maxmin decision rule, where we use the model condence set of Hansen, Lunde, and Nason (2011) as priors. In an empirical application, the density forecasts of ve GARCH-type models are combined with this maxmin rule and other decision rules for static portfolio choice with daily data on the S&P500.
    Keywords: Decision rules, forecast evaluation and comparison, maxmin, ambiguity aversion, portfolio choice.
    JEL: C44 C53 D81 G11
    Date: 2011–11–26
  9. By: Emanuela Ciapanna (Bank of Italy); Marco Taboga (Bank of Italy)
    Abstract: This paper proposes a Bayesian regression model with time-varying coefficients (TVC) that makes it possible to estimate jointly the degree of instability and the time-path of regression coefficients. Thanks to its computational tractability, the model proves suitable to perform the first (to our knowledge) Monte Carlo study of the finite-sample properties of a TVC model. Under several specifications of the data generating process, the proposed model’s estimation precision and forecasting accuracy compare favourably with those of other methods commonly used to deal with parameter instability. Furthermore, the TVC model leads to small losses of efficiency under the null of stability and it is robust to mis-specification, providing a satisfactory performance also when regression coefficients experience discrete structural breaks. As a demonstrative application, we use our TVC model to estimate the exposures of S&P 500 stocks to market-wide risk factors: we find that a vast majority of stocks have time-varying risk exposures and that the TVC model helps to forecast these exposures more accurately.
    Keywords: time-varying regression, coefficient instability
    JEL: C11 C32 C50
    Date: 2011–11
  10. By: Pawel Janus (VU University Amsterdam); Siem Jan Koopman (VU University Amsterdam); André Lucas (VU University Amsterdam)
    Abstract: We develop a new simultaneous time series model for volatility and dependence with long memory (fractionally integrated) dynamics and heavy-tailed densities. Our new multivariate model accounts for typical empirical features in financial time series while being robust to outliers or jumps in the data. In the empirical study for four Dow Jones equities, we find that the degree of memory in the volatilities of the equity return series is similar, while the degree of memory in correlations between the series varies significantly. The forecasts from our model are compared with high-frequency realised volatility and dependence measures. The forecast accuracy is overall higher compared to those from some well-known competing benchmark models.
    Keywords: fractional integration; correlation; Student's t copula; time-varying dependence; multivariate volatility
    JEL: C10 C22 C32 C51
    Date: 2011–12–12
  11. By: Travis J. Berge; Òscar Jordà
    Abstract: This paper codifies in a systematic and transparent way a historical chronology of business cycle turning points for Spain reaching back to 1850 at annual frequency, and 1939 at monthly frequency. Such an exercise would be incomplete without assessing the new chronology itself and against others —this we do with modern statistical tools of signal detection theory. We also use these tools to determine which of several existing economic activity indexes provide a better signal on the underlying state of the economy. We conclude by evaluating candidate leading indicators and hence construct recession probability forecasts up to 12 months in the future.
    Keywords: Business cycles ; Spain
    Date: 2011
  12. By: Diego Amaya (HEC Montreal - Department of Management Sciences); Peter Christoffersen (University of Toronto - Rotman School of Management and CREATES); Kris Jacobs (University of Houston - C.T. Bauer College of Business); Aurelio Vasquez (Instituto Tecnológico Autónomo de México (ITAM) - Department of Business Administration)
    Abstract: Yes. We use intraday data to compute weekly realized variance, skewness and kurtosis for individual equities and assess whether this week?s realized moments predict next week?s stock returns in the cross-section. We sort stocks each week according to their past realized moments, form decile portfolios and analyze subsequent weekly returns. We ?nd a very strong negative relationship between realized skewness and next week?s stock returns, and a positive relationship between realized kurtosis and next week?s stock returns. We do not ?nd a strong relationship between realized volatility and stock returns. A trading strategy that buys stocks in the lowest realized skewness decile and sells stocks in the highest realized skewness decile generates an average weekly return of 43 basis points with a t-statistic of 8:91. A similar strategy that buys stocks with high realized kurtosis and sells stocks with low realized kurtosis produces a weekly return of 16 basis points with a t-statistic of 2:98. Our results are robust across sample periods, portfolio weightings, and proxies for ?rm characteristics, and they are not captured by the Fama-French and Carhart factors.
    Keywords: Realized volatility, skewness, kurtosis, equity markets, return prediction.
    JEL: G11 G12 G17
    Date: 2011–07–29
  13. By: Stefano D'Addona (University of Roma Tre); Christos Giannikos (Zicklin School of Business)
    Abstract: Standard Real Business Cycle (RBC) models are well known to generate counter-factual asset pricing implications. This paper provides a simple extension to the prior literature where we study an economy that follows a regimes switching process both in the mean and the volatility, in conjunction with Epstein-Zin preferences for the consumers. We provide a detailed theoretical and numerical analysis of the model’s predictions. We also show that a reasonable parameterization of our model conveys reasonable financial figures. Furthermore, we provide evidence in support of the necessity to model the decline of macroeconomic risk in this particular class of models.
    Keywords: Asset Pricing, Real Business Cycle Models, Recursive Preferences, Markov Switching Models
    JEL: G12 E32 E23
    Date: 2011

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