nep-for New Economics Papers
on Forecasting
Issue of 2017‒03‒12
six papers chosen by
Rob J Hyndman
Monash University

  1. Robust Evaluation of Multivariate Density Forecasts By Dovern, Jonas; Manner, Hans
  2. Forecasting Inflation in a Macroeconomic Framework: An Application to Tunisia By Souhaïb Chamseddine Zardi
  3. Modelling Conditional Volatility and Downside Risk for Istanbul Stock Exchange By Amira Akl Ahmed; Doaa Akl Ahmed
  4. Non-parameteric news impact curve: a variational approach By Matthieu Garcin; Clément Goulet
  5. Predicting the equity premium via its components By Bätje, Fabian; Menkhoff, Lukas
  6. A suite of inflation forecasting models By Luis J. Álvarez; Isabel Sánchez

  1. By: Dovern, Jonas; Manner, Hans
    Abstract: We derive new tests for proper calibration of multivariate density forecasts based on Rosenblatt probability integral transforms. These tests have the advantage that they i) do not depend on the ordering of variables in the forecasting model, ii) are applicable to densities of arbitrary dimensions, and iii) have superior power relative to existing approaches. We furthermore develop adjusted tests that allow for estimated parameters and, consequently, can be used as in-sample specification tests. We demonstrate the problems of existing tests and how our new approaches can overcome those using two applications based on multivariate GARCH-based models for stock market returns and on a macroeconomic Bayesian vectorautoregressive model.
    JEL: C12 C32 C53
    Date: 2016
  2. By: Souhaïb Chamseddine Zardi (Central Bank of Tunisia)
    Abstract: The aim of this paper is to evaluate the relative performance of different forecasts of inflation methods for the case of Tunisia. For that, we use a large number of econometric models to forecast short-run inflation. Specifically, we use univariate models as Random Walk, SARIMA, a Time Varying Parameter model and a suite of multivariate autoregressive models as Bayesian VAR and Dynamic Factor models. The forecasting results suggest that models which incorporate more economic information outperform the benchmark random walk for the first two quarters ahead. Furthermore, we combine our forecasts by means and the results reveal that the combination of forecasts leads to a reduction in forecast errors compared to individual models.
    Keywords: Short-run forecasting, Dynamic Factor Models, Forecast combination
    Date: 2017–03
  3. By: Amira Akl Ahmed (University of Benha, Egypt); Doaa Akl Ahmed
    Abstract: We investigated the impact of alternative variance equation specifications and different densities on the forecasting of one-day-ahead value-at-risk for the Istanbul stock market. The three employed models are symmetric GARCH(1,1) of Bollerslev (1986), symmetric GARCH(1,1) of Taylor (1986) and APGARCH(1,1) of Ding et al. (1996) models, under three distributions. The comparison focuses on two different aspects: the difference between symmetric and asymmetric GARCH (i.e., GARCH versus APGARCH) and the difference between normal-tailed and fat-tailed distributions (i.e., Normal, Student-t, and GED). The GARCH(1,1) of Taylor was found to be unjustified since convergence could not be achieved. Also, we examined if the estimated coefficients are time-varying. We executed a fixed size rolling sample estimation to provide the one-step-ahead variance forecasts required to generate the one-step-ahead VaR. Our results indicate that the APGARCH(1,1) with t-distribution model outperform its competitors regarding out-of-sample forecasting ability. Moreover, we found that the power transformation parameter of APGARCH model was time-variant. In contrast, degrees of freedom of t-distribution and thickness parameter of GED distribution are time-invariant indicating that fat-tailedness of innovation does not change over time. Thus, these findings suggest that the student-t APGARCH(1,1) model could be used by conservative investors to evaluate their investment risk. Also, both exchanges and regulators may benefit from using that model when the market faces turmoil and becomes more volatile.
    Date: 2016–07
  4. By: Matthieu Garcin (Natixis Asset Management et LabEX ReFi); Clément Goulet (Centre d'Economie de la Sorbonne et LabEX ReFi)
    Abstract: In this paper, we propose an innovative methodology for modelling the news impact curve. The news impact curve provides a non-linear relation between past returns and current volatility and thus enables to forecast volatility. Our news impact curve is the solution of a dynamic optimization problem based on variational calculus. Consequently, it is a non-parametric and smooth curve. To our knowledge, this is the first time that such a method is used for volatility modelling. Applications on simulated heteroskedastic processes as well as on financial data show a better accuracy in estimation and forecast for this approach than for standard parametric (symmetric or asymmetric ARCH) or non-parametric (Kernel-ARCH) econometric techniques
    Keywords: Volatility modeling; news impact curve; calculus of variations; wavelet theory; ARCH
    JEL: C02 C14 C22 C51 C53 C58 C61
    Date: 2015–09
  5. By: Bätje, Fabian; Menkhoff, Lukas
    Abstract: We propose a refined way of forecasting the equity premium. Our approach rests on the sum-of-parts approach which disaggregates the equity premium into four components. Each of these components is predicted separately, following the approach of Ferreira and Santa-Clara (2011). We extend the set of standard macroeconomic variables by also using technical indicators as predictors. We find that macro indicators best predict the price-earnings multiple, whereas technical indicators better predict earnings growth. Applying this allocation generates superior forecast performance, statistically and economically. Moreover, we show that macroeconomic and technical indicators inform about complementary aspects of the business cycle.
    JEL: G17 G11 C53
    Date: 2016
  6. By: Luis J. Álvarez (Banco de España); Isabel Sánchez (Banco de España)
    Abstract: This paper describes the econometric models used by the Banco de España to monitor consumer price inflation and forecast its future trends. The strategy followed heavily relies on the results from a set of econometric models, supplemented by expert judgment. We consider three different types of approaches and highlight the relevance of heterogeneity in price-setting behaviour and the importance of using models that allow for a slowly evolving local mean when forecasting inflation.
    Keywords: inflation, forecasting, Phillips curves, transfer functions, judgemental forecasts
    JEL: C53 E31 E37
    Date: 2017–02

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