nep-for New Economics Papers
on Forecasting
Issue of 2017‒11‒19
eight papers chosen by
Rob J Hyndman
Monash University

  1. A simple model for forecasting conditional return distributions By Stanislav Anatolyev; Jozef Barunik
  2. A state space approach to evaluate multi-horizon forecasts By Goodwin, Thomas; Tian, Jing
  3. Multiplicative state-space models for intermittent time series By Svetunkov, Ivan; Boylan, John Edward
  4. Realized volatility of CO2 futures By Thijs Benschop; Brenda López Cabrera;
  5. Designing fan charts for GDP growth forecasts to better reflect downturn risks By David Turner
  6. Predicting Inflation and Output in Pakistan: The Role of Yield Spread By Fida Hussain; Asif Mahmood
  7. How Accurate are Modern Value-at-Risk Estimators Derived from Extreme Value Theory? By Benjamin R. Auer; Benjamin Mögel
  8. A simple nonlinear predictive model for stock returns By Biqing Cai; Jiti Gao

  1. By: Stanislav Anatolyev; Jozef Barunik
    Abstract: This paper presents a simple approach to forecasting conditional probability distributions of asset returns. We work with a parsimonious parametrization of ordered binary choice regression that quite precisely forecasts future conditional probability distributions of returns, using past indicator and past volatility proxy as predictors. Direct benefits of the proposed model are revealed in the empirical application to 29 most liquid U.S. stocks. The forecast probability distribution is translated to significant economic gains in a simple trading strategy. The model can therefore serve as useful risk management tool for investors monitoring tail risk, or even building trading strategies based on the entire conditional return distribution. Our approach can also be useful in many other applications where conditional distribution forecasts are desired.
    Date: 2017–11
  2. By: Goodwin, Thomas (Tasmanian School of Business & Economics, University of Tasmania); Tian, Jing (Tasmanian School of Business & Economics, University of Tasmania)
    Abstract: We propose a state space modeling framework to evaluate a set of forecasts that target the same variable but are updated along the forecast horizon. The approach decomposes forecast errors into three distinct horizon-specific processes, namely, bias, rational error and implicit error, and attributes forecast revisions to corrections for these forecast errors. We derive the conditions under which forecasts that contain error that is irrelevant to the target can still present the second moment bounds of rational forecasts. By evaluating multi-horizon daily maximum temperature forecasts for Melbourne, Australia, we demonstrate how this modeling framework analyzes the dynamics of the forecast revision structure across horizons. Understanding forecast revisions is critical for weather forecast users to determine the optimal timing for their planning decision.
    Keywords: Rational forecasts, implicit forecasts, forecast revision structure, weather forecasts
    JEL: C32 C53
    Date: 2017
  3. By: Svetunkov, Ivan; Boylan, John Edward
    Abstract: Intermittent demand forecasting is an important supply chain task, which is commonly done using methods based on exponential smoothing. These methods however do not have underlying statistical models, which limits their generalisation. In this paper we propose a general state-space model that takes intermittence of data into account, extending the taxonomy of exponential smoothing models. We show that this model has a connection with conventional non-intermittent state space models and underlies Croston’s and Teunter-Syntetos-Babai (TSB) forecasting methods. We discuss properties of the proposed models and show how a selection can be made between them in the proposed framework. We then conduct experiments on simulated data and on two real life datasets, demonstrating advantages of the proposed approach.
    Keywords: Intermittent demand, supply chain, forecasting, state-space models
    JEL: C53
    Date: 2017–11–07
  4. By: Thijs Benschop; Brenda López Cabrera;
    Abstract: The EU Emission Trading System (EU ETS) was created to reduce the CO2 and other greenhouse gas emissions at the lowest economic cost. In reality market participants are faced with considerable uncertainty due to price changes and require price and volatility estimates and forecasts for appropriate risk management, asset allocation and volatility trading. Although the simplest approach to estimate volatility is to use the historical standard deviation, realized volatility is a more accurate measure for volatility, since it is based on intraday data. Besides the stylized facts commonly observed in financial time series, we observe long-memory properties in the realized volatility series, which motivates the use of Heterogeneous Autoregressive (HAR) class models. Therefore, we propose to model and forecast the realized volatility of the EU ETS futures with HAR class models. The HAR models outperform benchmark models such as the standard long-memory ARFIMA model in terms of model fit, in-sample and out-of-sample forecasting. The analysis is based on intraday data (May 2007-April 2012) for futures on CO2 certificates for the second EU-ETS trading period (expiry December 2008-2012). The estimation results of the models allow to explain the volatility drivers in the market and volatility structure, according to the Heterogeneous Market Hypothesis as well as the observed asymmetries. We see that both speculators with short investment horizons as well as traders taking long-term hedging positions are active in the market. In a simulation study we test the suitability of the HAR model for option pricing and conclude that the HAR model is capable of mimicking the long-term volatility structure in the futures market and can be used for short-term and long-term option pricing.
    Keywords: EU ETS, Realized Volatility, HAR, Volatility Forecasting, Intraday Data, CO2 Emission Allowances, Emissions Markets, Asymmetry, SHAR, HARQ, MC Simulation JEL Classification: C00
    JEL: C00
    Date: 2017–08
  5. By: David Turner (OECD)
    Abstract: Forecasts of GDP growth are typically over-optimistic for horizons beyond the current year, particularly because they fail to predict the occurrence or severity of future downturns. Macroeconomic forecasters have also long been under pressure to convey the uncertainty surrounding their forecasts, particularly since the financial crisis. The current paper proposes a method to address both these issues simultaneously by constructing fan charts which are parameterised on the basis of the historical forecasting track record, but distinguish between a "safe" regime and a "downturn-risk" regime. To identify the two regimes, use is made of recent OECD work on early warning indicators of a prospective downturn, relating to housing market or credit developments. Thus, when an early warning indicator is “flashing", the associated fan chart is not only wider to reflect increased uncertainty, but is also skewed to reflect greater downside risks using a two-piece normal distribution of the form used by central banks to provide fan charts around inflation forecasts. Conversely, in a safe regime, when the early warning indicators are not flashing, as well as being symmetric, the fan chart is narrower both relative to the downturn-risk regime and relative to what the fan chart would be if the dispersion was calculated with respect to the entire forecast track record with no distinction between regimes. The method is illustrated by reference to OECD GDP forecasts for the major seven economies made just prior to the global financial crisis, with fan charts calibrated using the track record of forecasts published in the OECD Economic Outlook. Fan charts which take account of early warning indicators in this way are much better at encapsulating the outturns associated with a downturn than a symmetrical fan chart calibrated indiscriminately on all forecast errors.
    Keywords: downturn, economic forecasts, Fan charts, risk, uncertainty
    JEL: E17 E58 E65 E66 G01
    Date: 2017–11–17
  6. By: Fida Hussain (State Bank of Pakistan); Asif Mahmood (State Bank of Pakistan)
    Abstract: This paper presents the empirical evidences on the predictability of yield spread, particularly with respect to inflation and output growth in Pakistan. To our knowledge, this study is the first of its kind in case of Pakistan. We also test the role of foreign interest rates such as of the US in influencing the domestic interest rates in Pakistan and their contribution towards predicting inflation and output growth as well. Our results indicate that the yield spread in Pakistan do contain information about future changes in output growth but not for inflation. Both in-sample and out-of-sample forecasts for output growth show that the predictive content span from 6 to 24 months in future across the yield spreads. Use of the US yield spread further increases the predictability of domestic yield spread for output growth. In case of inflation, the results are found to be insignificant across different horizons and measures of yield spreads.
    Keywords: Yield curve, inflation, Output, Forecasting
    JEL: E43 O47 E31 C53
    Date: 2017–10
  7. By: Benjamin R. Auer; Benjamin Mögel
    Abstract: In this study, we compare the out-of-sample forecasting performance of several modern Value-at- Risk (VaR) estimators derived from extreme value theory (EVT). Specifically, in a multi-asset study covering 30 years of stock, bond, commodity and currency market data, we analyse the accuracy of the classic generalised Pareto peak over threshold approach and three recently proposed methods based on the Box-Cox transformation, L-moment estimation and the Johnson system of distributions. We find that, in their unconditional form, some of the estimators are acceptable under current regulatory assessment rules but none of them can continuously pass more advanced tests of forecasting accuracy. In their conditional forms, forecasting power is significantly increased and the Box-Cox method proves to be the most promising estimator. However, it is also important to stress that the traditional historical simulation approach, which is currently the most frequently used VaR estimator in commercial banks, can not only keep up with the EVT-based methods but occasionally even outperforms them (depending on the setting: unconditional vs. conditional). Thus, recent claims to generally replace this simple method by theoretically more advanced EVT-based methods may be premature.
    Keywords: value-at-risk, extreme value theory, historical simulation, backtest, financial crisis
    JEL: G10 G11 G17
    Date: 2016
  8. By: Biqing Cai; Jiti Gao
    Abstract: In this paper, we propose a simple approach to testing and modelling nonlinear predictability of stock returns using Hermite Functions. The proposed test suggests that there exists a kind of nonlinear predictability for the dividend yield. Furthermore, the out-of-sample evaluation results suggest the dividend yield has nonlinear predictive power for stock returns while the book-to-market ratio and earning-price ratio have little predictive power.
    Keywords: Hermite functions, out-of-sample forecast, return predictability, series estimator, unit root.
    JEL: C14 C22 G17
    Date: 2017

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