nep-for New Economics Papers
on Forecasting
Issue of 2018‒06‒25
three papers chosen by
Rob J Hyndman
Monash University

  1. Stochastic volatility and leverage effect in energy markets: evidence from high frequency data with VaR and CVaR risk analysis By Christopher F Baum; Paola Zerilli; Liyuan Chen
  2. The Fed's Asymmetric Forecast Errors By Andrew C. Chang
  3. Nonparametric Forecasting of Multivariate Probability Density Functions By Matteo Iacopini; Dominique Guégan

  1. By: Christopher F Baum (Boston College; German Institute for Economic Research (DIW Berlin)); Paola Zerilli (University of York); Liyuan Chen (University of York)
    Abstract: The study of volatility in crude oil and natural gas markets and its interaction with returns (leverage) has a broad range of financial impacts both from an hedging point of view and also for forecasting purposes. The main limitation of using daily data is that volatility is not observable. In contrast, intra-day data provide an almost continuous observation of the return series, making volatility observable so that it can be studied in great detail. From an econometric point of view, the employment of intra-day data leads to the estimation of structural parameters of stochastic volatility models using simple moment conditions while fitting all the relevant empirical features of energy and stock index returns. This paper contributes to the current debate by: 1) exploring evidence of leverage effects in energy futures markets versus financial stock indexes (S&P500) and 2) evaluating the impact of leverage on risk forecasting in a VaR and CVaR sense. We find significant evidence of a leverage e§ect for S&P500 and crude oil markets: a negative shock to returns increases volatility in these markets. We also find evidence of an inverse leverage effect for the natural gas market: volatility becomes higher when energy returns increase. We show that the introduction of leverage improves the forecasting ability of the SV model using the RMSE and MAE criteria for all the markets considered.
    Keywords: stochastic volatility, leverage effect, energy markets, high frequency data, VaR, CVaR
    JEL: C53 C58 G17 G32 Q41 Q47
    Date: 2018–06–15
  2. By: Andrew C. Chang
    Abstract: I show that the probability that the Board of Governors of the Federal Reserve System staff's forecasts (the "Greenbooks'") overpredicted quarterly real gross domestic product (GDP) growth depends on both the forecast horizon and also whether the forecasted quarter was above or below trend real GDP growth. For forecasted quarters that grew below trend, Greenbooks were much more likely to overpredict real GDP growth, with one-quarter ahead forecasts overpredicting real GDP growth more than 75% of the time, and this rate of overprediction was higher for further ahead forecasts. For forecasted quarters that grew above trend, Greenbooks were slightly more likely to underpredict real GDP growth, with one-quarter ahead forecasts underpredicting growth about 60% of the time. Unconditionally, on average, Greenbooks overpredicted real GDP growth.
    Keywords: Asymmetric forecast errors ; Federal open market committee ; Forecast accuracy ; Greenbook ; Monetary policy ; Real-time data
    JEL: C53 D23 E00 E17
    Date: 2018–04–16
  3. By: Matteo Iacopini (Department of Economics, Cà Foscari University of Venice); Dominique Guégan
    Abstract: The study of dependence between random variables is the core of theoretical and applied statistics. Static and dynamic copula models are useful for describing the dependence structure, which is fully encrypted in the copula probability density function. However, these models are not always able to describe the temporal change of the dependence patterns, which is a key characteristic of financial data. We propose a novel nonparametric framework for modelling a time series of copula probability density functions, which allows to forecast the entire function without the need of post-processing procedures to grant positiveness and unit integral. We exploit a suitable isometry that allows to transfer the analysis in a subset of the space of square integrable functions, where we build on nonparametric functional data analysis techniques to perform the analysis. The framework does not assume the densities to belong to any parametric family and it can be successfully applied also to general multivariate probability density functions with bounded or unbounded support. Finally, a noteworthy field of application pertains the study of time varying networks represented through vine copula models. We apply the proposed methodology for estimating and forecasting the time varying dependence structure between the S&P500 and NASDAQ indices.
    Keywords: Functional data analysis, functional PCA, functional time series, time varying dependence, time varying copula, clr transform, compositional data analysis
    JEL: C13 C33 C51 C53
    Date: 2018

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