nep-for New Economics Papers
on Forecasting
Issue of 2010‒09‒18
six papers chosen by
Rob J Hyndman
Monash University

  1. A Cholesky-MIDAS model for predicting stock portfolio volatility By Ralf Becker; Adam Clements; Robert O'Neill
  2. Meteorological forecasts and the pricing of weather derivatives By Matthias Ritter; Oliver Mußhoff; Martin Odening
  3. VaR Forecasts and Dynamic Conditional Correlations for Spot and Futures Returns on Stocks and Bonds By Abdul Hakim; Michael McAleer
  4. Ranking Multivariate GARCH Models by Problem Dimension By Massimiliano Caporin; Michael McAleer
  5. Realized Volatility Risk ( Revised in January 2010 ) By David E. Allen; Michael McAleer; Marcel Scharth
  6. Has inflation targeting increased predictive power of term structure about future inflation: evidence from an emerging market ? By Ege, Yazgan; Huseyin, Kaya

  1. By: Ralf Becker; Adam Clements; Robert O'Neill
    Abstract: This paper presents a simple forecasting technique for variance covariance matrices. It relies significantly on the contribution of Chiriac and Voev (2010) who propose to forecast elements of the Cholesky decomposition which recombine to form a positive definite forecast for the variance covariance matrix. The method proposed here combines this methodology with advances made in the MIDAS literature to produce a forecasting methodology that is flexible, scales easily with the size of the portfolio and produces superior forecasts in simulation experiments and an empirical application.
    Date: 2010
  2. By: Matthias Ritter; Oliver Mußhoff; Martin Odening
    Abstract: In usual pricing approaches for weather derivatives, forward-looking information such as meteorological weather forecasts is not considered. Thus, important knowledge used by market participants is ignored in theory. By extending a standard model for the daily temperature, this paper allows the incorporation of meteorological forecasts in the framework of weather derivative pricing and is able to estimate the information gain compared to a benchmark model without meteorological forecasts. This approach is applied for temperature futures referring to New York, Minneapolis and Cincinnati with forecast data 13 days in advance. Despite this relatively short forecast horizon, the models using meteorological forecasts outperform the classical approach and more accurately forecast the market prices of the temperature futures traded at the Chicago Mercantile Exchange (CME). Moreover, a concentration on the last two months or on days with actual trading improves the results.
    Keywords: Weather forecasting, weather risk, price forecasting, nancial markets, temperature futures, CME
    JEL: C53 G13 N23
    Date: 2010–09
  3. By: Abdul Hakim (Faculty of Economics, Indonesian Islamic University); Michael McAleer (Econometric Institute, Erasmus School)
    Abstract: The paper investigates the interdependence and conditional correlations between futures contracts and their underlying assets, both for stock and bond markets, and the impact of the interdependence and conditional correlations on VaR forecasts. The paper finds evidence of volatility spillovers from spot (futures) to futures (spot) markets, and time-varying conditional correlations between futures and their underlying assets. It also finds evidence that the DCC model of Engle (2002) provides slightly better VaR forecasts as compared with the CCC model of Bollerslev (1990) and the BEKK model of Engle and Kroner (1995).
    Date: 2009–10
  4. By: Massimiliano Caporin (Department of Economics and Management); Michael McAleer (Erasmus School of Economics, Erasmus University Rotterdam,)
    Abstract: In the last 15 years, several Multivariate GARCH (MGARCH) models have appeared in the literature. The two most widely known and used are the Scalar BEKK model of Engle and Kroner (1995) and Ding and Engle (2001), and the DCC model of Engle (2002). Some recent research has begun to examine MGARCH specifications in terms of their out-of-sample forecasting performance. In this paper, we provide an empirical comparison of a set of MGARCH models, namely BEKK, DCC, Corrected DCC (cDCC) of Aeilli (2008), CCC of Bollerslev (1990), Exponentially Weighted Moving Average, and covariance shrinking of Ledoit and Wolf (2004), using the historical data of 89 US equities. Our methods follow some of the approach described in Patton and Sheppard (2009), and contribute to the literature in several directions. First, we consider a wide range of models, including the recent cDCC model and covariance shrinking. Second, we use a range of tests and approaches for direct and indirect model comparison, including the Weighted Likelihood Ratio test of Amisano and Giacomini (2007). Third, we examine how the model rankings are influenced by the cross-sectional dimension of the problem.
    Date: 2010–05
  5. By: David E. Allen (School of Accounting, Finance and Economics,); Michael McAleer (Econometric Institute,); Marcel Scharth (VU University Amsterdam)
    Abstract: In this paper we document that realized variation measures constructed from high-frequency returns reveal a large degree of volatility risk in stock and index returns, where we characterize volatility risk by the extent to which forecasting errors in realized volatility are substantive. Even though returns standardized by ex post quadratic variation measures are nearly gaussian, this unpredictability brings considerably more uncertainty to the empirically relevant ex ante distribution of returns. Carefully modeling this volatility risk is fundamental. We propose a dually asymmetric realized volatility (DARV) model, which incorporates the important fact that realized volatility series are systematically more volatile in high volatility periods. Returns in this framework display time varying volatility, skewness and kurtosis. We provide a detailed account of the empirical advantages of the model using data on the S&P 500 index and eight other indexes and stocks.
    Date: 2009–12
  6. By: Ege, Yazgan; Huseyin, Kaya
    Abstract: This paper contributes to the vast literature on the predictive power of term structure about future inflation, by focusing on an emerging market case. The following important result emerged in our paper: Monetary policy change is an important determinant of the relationship between term structure and inflation to the extent that even the existence of the relationship critically depends on the nature of monetary policy regime. In our case, the change in monetary policy is associated with the beginning of the implementation of an inflation targeting (IT) regime. While, before IT regime, the information in term structure does not provide any predictive power for future inflation, this phenomenon seems to be completely reversed after IT. Since the implementation of IT, term structure of interest rates has seemed to gain considerable forecasting power for future inflation.
    Keywords: Term Structure of Interest Rate; Structural Break; Inflation; Monetary Policy; Inflation Targeting
    JEL: E43 C53 E52 G00
    Date: 2010–08

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