nep-for New Economics Papers
on Forecasting
Issue of 2012‒04‒17
eleven papers chosen by
Rob J Hyndman
Monash University

  1. Inflation Forecast Contracts By Gersbach, Hans; Hahn, Volker
  2. Combination schemes for turning point predictions By Monica Billio; Roberto Casarin; Francesco Ravazzolo; Herman K. van Dijk
  3. Financial variables as leading indicators of GDP growth: Evidence from a MIDAS approach during the Great Recession By Laurent Ferrara; Clément Marsilli
  4. Realized Wavelet Jump-GARCH model: Can wavelet decomposition of volatility improve its forecasting? By Jozef Barunik; Lukas Vacha
  5. Robust Ranking of Multivariate GARCH Models by Problem Dimension By Massimiliano Caporin; Michael McAleer
  6. Forecasting Value-at-Risk Using Block Structure Multivariate Stochastic Volatility Models By Michael McAleer; Manabu Asai; Massimiliano Caporin
  7. Large Time-Varying Parameter VARs By Gary Koop; Dimitris Korobilis
  8. Modelling and Forecasting Yield Differentials in the euro area. A non-linear Global VAR model By Carlo A. Favero
  9. Model Implied Credit Spreads By Gunnar Grass
  10. Evolutionary Selection of Individual Expectations and Aggregate Outcomes in Asset Pricing Experiments (revised version of WP 09-09) By Anufriev, M.; Hommes, C.H.
  11. Price Jump Prediction in Limit Order Book By Ban Zheng; Eric Moulines; Fr\'ed\'eric Abergel

  1. By: Gersbach, Hans; Hahn, Volker
    Abstract: We introduce a new type of incentive contract for central bankers: inflation forecast contracts, which make central bankers’ remunerations contingent on the precision of their inflation forecasts. We show that such contracts enable central bankers to influence inflation expectations more effectively, thus facilitating more successful stabilization of current inflation. Inflation forecast contracts improve the accuracy of inflation forecasts, but have adverse consequences for output. On balance, paying central bankers according to their forecasting performance improves welfare. Optimal inflation forecast contracts stipulate high rewards for accurate forecasts.
    Keywords: central banks; incentive contracts; inflation forecast targeting; inflation targeting; intermediate targets; transparency
    JEL: E58
    Date: 2012–04
  2. By: Monica Billio (University of Venice, GRETA Assoc. and School for Advanced Studies in Venice); Roberto Casarin (University of Venice, GRETA Assoc. and School for Advanced Studies in Venice); Francesco Ravazzolo (Norges Bank (Central Bank of Norway) and BI Norwegian Business School); Herman K. van Dijk (Econometric Institute, Erasmus University Rotterdam and VU University Amsterdam and Tinbergen Institute)
    Abstract: We propose new forecast combination schemes for predicting turning points of business cycles. The combination schemes deal with the forecasting performance of a given set of models and possibly providing better turning point predictions. We consider turning point predictions generated by autoregressive (AR) and Markov-Switching AR models, which are commonly used for business cycle analysis. In order to account for parameter uncertainty we consider a Bayesian approach to both estimation and prediction and compare, in terms of statistical accuracy, the individual models and the combined turning point predictions for the United States and Euro area business cycles.
    Keywords: Turning Points, Markov-switching, Forecast Combination, Bayesian Model Averaging
    JEL: C11 C15 C53 E37
    Date: 2012–04–10
  3. By: Laurent Ferrara; Clément Marsilli
    Abstract: The global economic recession, referred to as the Great Recession, endured by the main industrialized countries during the period 2008-09, in the wake of the financial and banking crisis, has pointed out the current importance of the financial sector in macroeconomics. In this paper, we evaluate the predictive power of some major financial variables to anticipate GDP growth in euro area countries during this specific period of time. In this respect, we implement a MIDAS-based modeling approach, put forward by Ghysels et al. (2007), that enables to forecast quarterly GDP growth rates using exogenous variables sampled at higher frequencies. Empirical results show that, overall, stock prices help to improve the accuracy of GDP forecasts by comparison with a standard opinion survey variable, while oil prices and term spread appear to be less informative.
    Keywords: Great Recession, Forecasting, Financial variables, MIDAS approach
    JEL: C2 C5 E3
    Date: 2012
  4. By: Jozef Barunik; Lukas Vacha
    Abstract: In this paper, we propose a forecasting model for volatility based on its decomposition to several investment horizons and jumps. As a forecasting tool, we utilize Realized GARCH framework of Hansen et al. (2011), which models jointly returns and realized measures of volatility. For the decomposition, we use jump wavelet two scale realized volatility estimator (JWTSRV) of Barunik and Vacha (2012). While the main advantage of our time-frequency estimator is that it provides us with realized volatility measure robust to noise as well as with consistent estimate of jumps, it also allows to decompose volatility into the several investment horizons. On currency futures data covering the period of recent financial crisis, we compare forecasts from Realized GARCH(1,1) model using several measures. Namely, we use the realized volatility, bipower variation, two- scale realized volatility, realized kernel and our jump wavelet two scale realized volatility. We find that in-sample as well as out-of-sample performance of the model significantly differs based on the realized measure used. When JWTSRV estimator is used, model produces significantly best forecasts. We also utilize jumps and build Realized Jump-GARCH model. Utilizing the decomposition obtained by our estimator, we finally build Realized Wavelet-Jump GARCH model, which uses estimated jumps as well as volatility at several investment horizons. Our Realized Wavelet-Jump GARCH model proves to further improve the volatility forecasts. We conclude that realized volatility measurement in the time-frequency domain and inclusion of jumps improves the volatility forecasting considerably.
    Date: 2012–04
  5. By: Massimiliano Caporin; Michael McAleer (University of Canterbury)
    Abstract: During the last 15 years, several Multivariate GARCH (MGARCH) models have appeared in the literature. Recent research has begun to examine MGARCH specifications in terms of their out-of-sample forecasting performance. We provide an empirical comparison of alternative MGARCH models, namely BEKK, DCC, Corrected DCC (cDCC), CCC, OGARCH Exponentially Weighted Moving Average, and covariance shrinking, using historical data for 89 US equities. We contribute to the literature in several directions. First, we consider a wide range of models, including the recent cDCC and covariance shrinking models. Second, we use a range of tests and approaches for direct and indirect model comparison, including the Model Confidence Set. Third, we examine how the robust model rankings are influenced by the crosssectional dimension of the problem.
    Keywords: Covariance forecasting; model confidence set; robust model ranking; MGARCH; robust model comparison
    JEL: C18 C81 Y10
    Date: 2012–04–01
  6. By: Michael McAleer (Erasmus University Rotterdam,Tinbergen Institute,Kyoto University,Complutense University of Madrid); Manabu Asai (Faculty of Economics Soka University); Massimiliano Caporin (Department of Economics and Management “Marco Fanno”University of Padova)
    Abstract: Most multivariate variance or volatility models suffer from a common problem, the “curse of dimensionality”. For this reason, most are fitted under strong parametric restrictions that reduce the interpretation and flexibility of the models. Recently, the literature has focused on multivariate models with milder restrictions, whose purpose was to combine the need for interpretability and efficiency faced by model users with the computational problems that may emerge when the number of assets is quite large. We contribute to this strand of the literature proposing a block-type parameterization for multivariate stochastic volatility models. The empirical analysis on stock returns on US market shows that 1% and 5 % Value-at-Risk thresholds based on one-step-ahead forecasts of covariances by the new specification are satisfactory for the period includes the global financial crisis.
    Keywords: block structures; multivariate stochastic volatility; curse of dimensionality; leverage effects; multi-factors; heavy-tailed distribution.
    JEL: C32 C51 C10
    Date: 2012–04
  7. By: Gary Koop (University of Strathclyde, UK; The Rimini Centre for Economic Analysis (RCEA), Italy); Dimitris Korobilis (University of Glasgow, UK; The Rimini Centre for Economic Analysis (RCEA), Italy)
    Abstract: In this paper we develop methods for estimation and forecasting in large time-varying parameter vector autoregressive models (TVP-VARs). To overcome computational constraints with likelihood-based estimation of large systems, we rely on Kalman filter estimation with forgetting factors. We also draw on ideas from the dynamic model averaging literature and extend the TVP-VAR so that its dimension can change over time. A final extension lies in the development of a new method for estimating, in a time-varying manner, the parameter(s) of the shrinkage priors commonly-used with large VARs. These extensions are operationalized through the use of forgetting factor methods and are, thus, computationally simple. An empirical application involving forecasting inflation, real output, and interest rates demonstrates the feasibility and usefulness of our approach.
    Keywords: Bayesian VAR; forecasting; time-varying coefficients; state-space model
    JEL: C11 C52 E27 E37
    Date: 2012–03
  8. By: Carlo A. Favero
    Abstract: Unstability in the comovement among bond spreads in the euro area is an important feature for dynamic econometric modelling and forecasting. This paper proposes a non-linear GVAR approach to spreads in the euro area where the changing interdepence among these variables is modelled by making each country spread function of a global variable determined by fiscal fundamentals with a time-varying composition. The model naturally accommodates the possibility of multiple equilibria in the relation between default premia and local fiscal fundamentals. The estimation reveals a significant non-linear relation between spreads and fiscal fundamentals that generates time-varying impulse response of local spreads to shocks in other euro area countries spreads. The GVAR framework is then applied to the analysis of the dynamic effects of fiscal stabilization packages on the cost of government borrowing and to the evaluation of the importance of potential contagion effects determining a significant increase in cross-market linkages after a shock to a group of countries.
    Date: 2012
  9. By: Gunnar Grass
    Abstract: I propose a new measure of credit risk, model implied credit spreads (MICS), which can be extracted from any structural credit risk model in which debt values are a function of asset risk and the payout ratio. I implement MICS assuming a barrier option framework nesting the Merton (1974) model of capital structure. MICS are the increase in the payout to creditors necessary to offset the impact of an increase in asset variance on the option value of debt. Endogenizing asset payouts, my measure (i) predicts higher credit risk for safe firms and lower credit risk for firms with high volatility and leverage than a standard distance to default (DD) measure and (ii) clearly outperforms the DD measure when used to predict corporate default or to explain variations in credit spreads.
    Keywords: Structural Credit Risk Models, Bankruptcy Prediction, Risk-Neutral Pricing
    JEL: G33 G13 G32
    Date: 2012
  10. By: Anufriev, M. (University of Amsterdam); Hommes, C.H. (University of Amsterdam)
    Abstract: In recent `learning to forecast' experiments with human subjects (Hommes, et al. 2005), three different patterns in aggregate price behavior have been observed: slow monotonic convergence, permanent oscillations and dampened fluctuations. We show that a simple model of individual learning can explain these different aggregate outcomes within the same experimental setting. The key idea of the model is the evolutionary selection among heterogeneous expectation rules, driven by the relative performance of the rules. Out-of-sample predictive power of our switching model is higher compared to the rational or other homogeneous expectations benchmarks. Our results show that heterogeneity in expectations is crucial to describe individual forecasting behavior as well as aggregate price behavior.
  11. By: Ban Zheng; Eric Moulines; Fr\'ed\'eric Abergel
    Abstract: A limit order book provides information on available limit order prices and their volumes. Based on these quantities, we give an empirical result on the relationship between the bid-ask liquidity balance and trade sign and we show that liquidity balance on best bid/best ask is quite informative for predicting the future market order's direction. Moreover, we define price jump as a sell (buy) market order arrival which is executed at a price which is smaller (larger) than the best bid (best ask) price at the moment just after the precedent market order arrival. Features are then extracted related to limit order volumes, limit order price gaps, market order information and limit order event information. Logistic regression is applied to predict the price jump from the limit order book's feature. LASSO logistic regression is introduced to help us make variable selection from which we are capable to highlight the importance of different features in predicting the future price jump. In order to get rid of the intraday data seasonality, the analysis is based on two separated datasets: morning dataset and afternoon dataset. Based on an analysis on forty largest French stocks of CAC40, we find that trade sign and market order size as well as the liquidity on the best bid (best ask) are consistently informative for predicting the incoming price jump.
    Date: 2012–04

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