nep-for New Economics Papers
on Forecasting
Issue of 2017‒01‒22
four papers chosen by
Rob J Hyndman
Monash University

  1. Exchange rate forecasting and the performance of currency portfolios By Crespo Cuaresma, Jesus; Fortin, Ines; Hlouskova, Jaroslava
  2. Forecast Errors and Uncertainty Shocks By Sylwia Nowak; Pratiti Chatterjee
  3. Forecasting the equity risk premium with frequency-decomposed predictors By Faria, Gonçalo; Verona, Fabio
  4. Wavelet-based option pricing: An empirical study By Liu, Xiaoquan; Shen, Liya

  1. By: Crespo Cuaresma, Jesus (Vienna University of Economics and Business (WU), Austria Wittgenstein Centre for Demography and Global Human Capital (WIC), International Institute for Applied Systems Analysis (IIASA), Austrian Institute of Economic Research (WIFO)); Fortin, Ines (Research Group Financial Markets and Econometrics, Institute for Advanced Studies); Hlouskova, Jaroslava (Research Group Financial Markets and Econometrics, Institute for Advanced Studies and Thompson Rivers University, Canada)
    Abstract: We examine the potential gains of using exchange rate forecast models and forecast combination methods in the management of currency portfolios for three exchange rates, the euro (EUR) versus the US dollar (USD), the British pound (GBP) and the Japanese yen (JPY). We use a battery of econometric specifications to evaluate whether optimal currency portfolios implied by trading strategies based on exchange rate forecasts outperform single-currency and the equally weighted portfolio. We assess the differences in profitability of optimal currency portfolios for different types of investor preferences, different trading strategies, different composite forecasts and different forecast horizons. Our results indicate that the benefits of integrating exchange rate forecasts from state-of-the-art econometric models in currency portfolios are sensitive to the trading strategy under consideration and vary strongly across prediction horizons.
    Keywords: currency portfolios, exchange rate forecasting, trading strategies, profitability
    JEL: G02 G11 E20
    Date: 2017–01
  2. By: Sylwia Nowak; Pratiti Chatterjee
    Abstract: Macroeconomic forecasts are persistently too optimistic. This paper finds that common factors related to general uncertainty about U.S. macrofinancial prospects and global demand drive this overoptimism. These common factors matter most for advanced economies and G- 20 countries. The results suggest that an increase in uncertainty-driven overoptimism has dampening effects on next-year real GDP growth rates. This implies that incorporating the common structure governing forecast errors across countries can help improve subsequent forecasts.
    Keywords: Economic forecasting;Forecasting models;Vector autoregression;Error analysis;Forecasting, common factors, uncertainty
    Date: 2016–11–17
  3. By: Faria, Gonçalo; Verona, Fabio
    Abstract: We show that the out-of-sample forecast of the equity risk premium can be signi ficantly improved by taking into account the frequency-domain relationship between the equity risk premium and several potential predictors. We consider fi fteen predictors from the existing literature, for the out-of-sample forecasting period from January 1990 to December 2014. The best result achieved for individual predictors is a monthly out-of-sample R2 of 2.98 % and utility gains of 549 basis points per year for a mean-variance investor. This performance is improved even further when the individual forecasts from the frequency-decomposed predictors are combined. These results are robust for di fferent subsamples, including the Great Moderation period, the Great Financial Crisis period and, more generically, periods of bad, normal and good economic growth. The strong and robust performance of this method comes from its ability to disentangle the information aggregated in the original time series of each variable, which allows to isolate the frequencies of the predictors with the highest predictive power from the noisy parts.
    JEL: C58 G11 G12 G17
    Date: 2017–01–03
  4. By: Liu, Xiaoquan; Shen, Liya
    Abstract: In this paper, we adopt a wavelet-based option valuation model and empirically compare the pricing and forecasting performance of this model with that of the stochastic volatility model with jumps and the spline method. Both the in-sample valuation and out-of-sample forecasting accuracy are examined using daily index options in the UK, Germany, and Hong Kong from January 2009 to December 2012. Our results show that the wavelet-based model compares favorably with the other two models and that it provides an excellent alternative for valuing option prices. Its superior performance comes from the powerful ability of the wavelet method in approximating the risk-neutral moment-generating functions.
    Keywords: Pricing; Option Pricing; Wavelet Method; Stochastic Volatility; Jump Risk
    Date: 2017–01

This nep-for issue is ©2017 by Rob J Hyndman. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.