nep-for New Economics Papers
on Forecasting
Issue of 2014‒03‒08
nine papers chosen by
Rob J Hyndman
Monash University

  1. Forecasting Equity Premia using Bayesian Dynamic Model Averaging By Joscha Beckmann; Rainer Schüssler
  2. Energy Markets Volatility Modelling using GARCH By Olga Efimova; Apostolos Serletis
  3. "Volatility and Quantile Forecasts by Realized Stochastic Volatility Models with Generalized Hyperbolic Distribution" By Makoto Takahashi; Toshiaki Watanabe; Yasuhiro Omori
  4. Forecasting Realized Volatility with Changes of Regimes By Giampiero M. Gallo; Edoardo Otranto
  5. Disentangling Systematic and Idiosyncratic Dynamics in Panels of Volatility Measures By Matteo Barigozzi; Christian T. Brownlees; Giampiero M. Gallo; David Veredas
  6. Positive Semidefinite Integrated Covariance Estimation, Factorizations and Asynchronicity By Kris Boudt; Sébastien Laurent; Asger Lunde; Rogier Quaedvlieg
  7. Measuring Environmental and Economic Efficiency in Italy: an Application of the Malmquist-DEA and Grey Forecasting Model By O.A. Carboni; P. Russu
  8. Go with the Flow: A GAS model for Predicting Intra-daily Volume Shares By Francesco Calvori; Fabrizio Cipollini; Giampiero M. Gallo
  9. Efficient Modeling and Forecasting of the Electricity Spot Price By Florian Ziel; Rick Steinert

  1. By: Joscha Beckmann; Rainer Schüssler
    Abstract: This paper introduces a Bayesian version for Dynamic Model Averaging for predicting aggregate stock returns. Our suggested approach simultaneously accounts for many sources of uncertainty. It is designed to handle (i) parameter instability, (ii) time-varying volatility, (iii) model uncertainty and (iv) time-varying model weights. We use our method to analyze predictability of S&P500 returns for the 1927 - 2012 period. The flexibility of the econometric setup enables us to disentangle the multitude of effects at work when generating (point and density) forecasts. A key point of our analysis is to assess which components of forecast models pay off in terms of statistical accuracy and economic value. We document that statistical and economic evaluation metrics can be in sharp contrast. While stochastic volatility emerges to be important both in terms of density forecast accuracy and economic gains, return prediction models that use economic covariates turned out to be helpful to time the market only within very limited periods of time.
    Keywords: Asset allocation; Density forecasting; Model averaging
    JEL: C11 G11
    Date: 2014–02
  2. By: Olga Efimova; Apostolos Serletis (University of Calgary)
    Abstract: This paper investigates the empirical properties of oil, natural gas, and electricity price volatilities using a range of univariate and multivariate GARCH models and daily data from wholesale markets in the United States for the period from 2001 to 2013. The key contribution to the literature is the estimation of trivariate BEKK and DCC models that allow us to observe spillovers and interactions among energy markets. We evaluate and compare the performance of univariate and multivariate models with a range of diagnostic and forecast performance tests, and assess forecasting performance and conditional correlation dynamics.
    Date: 2014–02–24
  3. By: Makoto Takahashi (Center for the Study of Finance and Insurance, Osaka University and Department of Finance, Kellogg School of Management, Northwestern University); Toshiaki Watanabe (Institute of Economic Research, Hitotsubashi University); Yasuhiro Omori (Faculty of Economics, The University of Tokyo)
    Abstract:    The realized stochastic volatility model of Takahashi, Omori, and Watanabe (2009), which incorporates the asymmetric stochastic volatility model with the realized volatility, is extended with more general form of bias correction in realized volatility and wider class distribution, the generalized hyperbolic skew Student's t -distribution, fornancial returns. The extensions make it possible to adjust the bias due to the market microstructure noise and non-trading hours, which possibly depends on the level of the volatility, and to consider the heavy tail and skewness in nancial returns. With the Bayesian estimation scheme via Markov chain Monte Carlo method, the model enables us to estimate the parameters in the return distribution and in the model jointly. It also makes it possible to forecast volatility and return quantiles by sampling from their posterior distributions jointly. The model is applied to quantile forecasts of nancial returns such as value-at-risk and expected shortfall as well as volatility forecasts and those forecasts are evaluated by several backtesting procedures. Empirical results with SPDR, the S&P 500 exchange-traded fund, show that the heavy tail and skewness of daily returns are important for the model fit and the quantile forecasts but not for the volatility forecasts, and that the additional bias correction improves the quantile forecasts but does not substantially improve the model fit nor the volatility forecasts.
    Date: 2014–02
  4. By: Giampiero M. Gallo (Dipartimento di Statistica, Informatica, Applicazioni "G.Parenti", Università di Firenze); Edoardo Otranto (Dipartimento di Scienze Cognitive e della Formazione, Università degli Studi di Messina)
    Abstract: Realized volatility of financial time series generally shows a slow–moving average level from the early 2000s to recent times, with alternating periods of turmoil and quiet. Modeling such a pattern has been variously tackled in the literature with solutions spanning from long–memory, Markov switching and spline interpolation. In this paper, we explore the extension of Multiplicative Error Models to include a Markovian dynamics (MS-MEM). Such a model is able to capture some sudden changes in volatility following an abrupt crisis and to accommodate different dynamic responses within each regime. The model is applied to the realized volatility of the S&P500 index: next to an interesting interpretation of the regimes in terms of market events, the MS-MEM has better in–sample fitting capability and achieves good out–of–sample forecasting performances relative to alternative specifications.
    Keywords: MEM, regime switching, realized volatility, volatility persistence, volatility forecasting
    JEL: C22 C24 C58
    Date: 2014–02
  5. By: Matteo Barigozzi (London School of Economics and Political Science – Department of Statistics); Christian T. Brownlees (Universitat Pompeu Fabra – Department of Economics and Business & Barcelona GSE); Giampiero M. Gallo (Dipartimento di Statistica, Informatica, Applicazioni "G.Parenti", Università di Firenze); David Veredas (ECARES – Solvay Brussels School of Economics and Management – Université libre de Bruxelles)
    Abstract: Realized volatilities measured on several assets exhibit a common secular trend and some idiosyncratic pattern. We accommodate such an empirical regularity extending the class of Multiplicative Error Models (MEMs) to a model where the common trend is estimated nonparametrically while the idiosyncratic dynamics are assumed to follow univariate MEMs. Estimation theory based on seminonparametric methods is developed for this class of models for large cross-sections and large time dimensions. The methodology is illustrated using two panels of realized volatility measures between 2001 and 2008: the SPDR Sectoral Indices of the S&P500 and the constituents of the S&P100. Results show that the shape of the common volatility trend captures the overall level of risk in the market and that the idiosyncratic dynamics have an heterogeneous degree of persistence around the trend. An out–of–sample forecasting exercise shows that the proposed methodology improves volatility prediction over a number of benchmark specifications.
    Keywords: Vector Multiplicative Error Model, Seminonparametric Estimation, Volatility.
    JEL: C32 C51 G01
    Date: 2014–02
  6. By: Kris Boudt (Department of Business, Vrije Universiteit Brussel, Belgium and VU University Amsterdam, Netherlands); Sébastien Laurent (Aix-Marseille University, Aix-Marseille School of Economics, CNRS & EHESS, France); Asger Lunde (Aarhus University and CREATES); Rogier Quaedvlieg (Department of Finance, Maastricht University, Netherlands)
    Abstract: An estimator of the ex-post covariation of log-prices under asynchronicity and microstructure noise is proposed. It uses the Cholesky factorization on the correlation matrix in order to exploit the heterogeneity in trading intensity to estimate the different parameters sequentially with as many observations as possible. The estimator is guaranteed positive semidefinite. Monte Carlo simulations confirm good finite sample properties. In the application we forecast portfolio Value-at-Risk and sector risk exposures for a portfolio of 52 stocks. We find that forecasts obtained from dynamic models utilizing the proposed high-frequency estimator provide statistically and economically superior forecasts to models using daily returns.
    Keywords: Cholesky decomposition, Integrated covariance, Non-synchronous trading, Positive semidefinite, Realized covariance
    JEL: C10 C58
    Date: 2014–02–24
  7. By: O.A. Carboni; P. Russu
    Abstract: Economic and environmental efficiency has being receiving growing attention among researchers. In general terms, this concept is related to the capability of the economic systems to employ natural resources efficiently, so as to increase economic and human wealth. This clearly implies that both the economic and ecological aspects of decisions ought to be considered. Bearing this in mind, this paper considers economic and ecological performance together, by applying data envelopment analysis (DEA) and the Malmquist productivity index (MPI) to investigating the efficiency of the 20 Italian regions from 2004 to 2011. The results reveal that the northern regions have been more efficient than the southern ones, highlighting the strong geographical differences between the two. Furthemore this paper uses the Grey System Theory to forecast regional economic and environmental efficiency. The results of the forecasting analysis show that the North-south duality remains strong and will possibly increase since the regions in the south get worse in term of environmental and economic efficiency.
    Keywords: panel data, forecasting, Data envelopment analysis (DEA), Malmquist productivity index (MPI), Grey system theory
    JEL: E17 C61 C23 C14
    Date: 2014
  8. By: Francesco Calvori (Dipartimento di Statistica, Informatica, Applicazioni "G.Parenti", Università di Firenze); Fabrizio Cipollini (Dipartimento di Statistica, Informatica, Applicazioni "G.Parenti", Università di Firenze); Giampiero M. Gallo (Dipartimento di Statistica, Informatica, Applicazioni "G.Parenti", Università di Firenze)
    Abstract: The Volume Weighted Average Price (VWAP) mixes volumes and prices at intra-daily intervals and is a benchmark measure frequently used to evaluate a trader's performance. Under suitable assumptions, splitting a daily order according to ex-ante volume predictions is a good strategy to replicate the VWAP. To bypass possible problems generated by local trends in volumes, we propose a novel Generalized Autoregressive Score (GAS) model for predicting volume shares (relative to the daily total), inspired by the empirical regularities of the observed series (intra-daily periodicity pattern, residual serial dependence). An application to six NYSE tickers confirms the suitability of the model proposed in capturing the features of intra-daily dynamics of volume shares.
    Keywords: High Frequency Financial Data, Prediction, Trading Volumes, Volume Shares, VWAP, GAS, Dirichlet Distribution
    JEL: C22 C53 C58
    Date: 2014–02
  9. By: Florian Ziel; Rick Steinert
    Abstract: The raising importance of renewable energy, especially solar and wind power, led to new impacts on the formation of electricity prices. Hence, this paper introduces an econometric model for the hourly time series of electricity prices of the EEX which incorporates specific features like renewable energy. The model consists of several sophisticated and established approaches and can be regarded as a periodic VAR-TARCH with wind power, solar power and load as influencing time series. It is able to map the distinct and well-known features of electricity prices in Germany. An efficient iteratively reweighted lasso approach is used for estimation. Moreover, it is shown that several existing models are outperformed by using the procedure developed within this paper.
    Date: 2014–02

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