nep-ets New Economics Papers
on Econometric Time Series
Issue of 2005‒10‒29
nine papers chosen by
Yong Yin
SUNY at Buffalo

  1. Optimal combination of density forecasts By Stephen Hall; James Mitchell
  2. Density Forecast Combination By Stephen Hall; James Mitchell
  3. Panel Smooth Transition Regression Models By Andres Gonzalez; Timo Terasvirta; Dick van Dijk
  4. Implied Calibration of Stochastic Volatility Jump Diffusion Models By Stefano Galluccio; Yann Le Cam
  5. Time-varying Beta Risk of Pan-European Industry Portfolios: A Comparison of Alternative Modeling Techniques By Sascha Mergner; Jan Bulla
  6. The Emerging Market Crisis and Stock Market Linkages: Further Evidence By Jian Yang; Cheng Hsiao; Qi Li; Zijun Wang
  7. A Dynamic Semiparametric Factor Model for Implied Volatility String Dynamics By Matthias Fengler; Wolfgang Härdle; Enno Mammen
  8. The Limiting Power of Autocorrelation Tests in Regression Models with Linear Restrictions By Wan, Alan T.K.; Zou, Guohua; Banerjee, Anurag
  9. Model Selection Uncertainty and Detection of Threshold Effects By Pitarakis, Jean-Yves

  1. By: Stephen Hall; James Mitchell
    Abstract: This paper brings together two important but hitherto largely unrelated areas of the forecasting literature, density forecasting and forecast combination. It proposes a simple data-driven approach to direct combination of density forecasts using optimal weights.
    Date: 2004–11
  2. By: Stephen Hall; James Mitchell
    Abstract: In this paper we investigate whether and how far density forecasts sensibly can be combined to produce a "better" pooled density forecast. In so doing we bring together two important but hitherto largely unrelated areas of the forecasting literature in economics, density forecasting and forecast combination. We provide simple Bayesian methods of pooling information across alternative density forecasts. We illustrate the proposed techniques in an application to two widely used published density forecasts for U.K. inflation. We examine whether in practice improved density forecasts for inflation, one year ahead, might have been obtained if one had combined the Bank of England and NIESR density forecasts or "fan charts".
    Date: 2004–11
  3. By: Andres Gonzalez (Banco de la Republica de Colombia, Stockholm School of Economics); Timo Terasvirta (Department of Economic Statistics, Stockholm School of Economics); Dick van Dijk (Econometric Institute, Erasmus University Rotterdam)
    Abstract: We develop a non-dynamic panel smooth transition regression model with fixed individual effects. The model is useful for describing heterogenous panels, with regression coefficients that vary across individuals and over time. Heterogeneity is allowed for by assuming that these coefficients are continuous functions of an observable variable through a bounded function of this variable and fluctuate between a limited number (often two) of ?extreme regimes?. The model can be viewed as a generalization of the threshold panel model of Hansen (1999). We extend the modelling strategy for univariate smooth transition regression models to the panel context. This comprises of model specification based on homogeneity tests, parameter estimation, and diagnostic checking, including tests for parameter constancy and no remaining nonlinearity. The new model is applied to describe firms? investment decisions in the presence of capital market imperfections.
    Keywords: financial constraints; heterogenous panel; investment; misspecification test; nonlinear modelling panel data; smooth transition models
    JEL: C12 C23 C52 G31 G32
    Date: 2005–08–01
  4. By: Stefano Galluccio (BNP Paribas); Yann Le Cam (University of Evry Val d'Essonne)
    Abstract: In the context of arbitrage-free modelling of financial derivatives, we introduce a novel calibration technique for models in the affine- quadratic class for the purpose of contingent claims pricing and risk- management. In particular, we aim at calibrating a stochastic volatility jump diffusion model to the whole market volatility surface at any given time. We numerically implement the algorithm and show that the proposed approach is both stable and accurate.
    Keywords: Affine-quadratic models, Option pricing, Model Calibration
    JEL: G12 G13
    Date: 2005–10–25
  5. By: Sascha Mergner (AMB Generali Asset Managers); Jan Bulla (Georg-August-University, Goettingen)
    Abstract: This paper investigates the time-varying behavior of systematic risk for eighteen pan-European sectors. Using weekly data over the period 1987- 2005, four different modeling techniques in addition to the standard constant coefficient model are employed: a bivariate t-GARCH(1,1) model, two Kalman filter based approaches, a bivariate stochastic volatility model estimated via the efficient Monte Carlo likelihood technique as well as two Markov switching models. A comparison of the different models' ex-ante forecast performances indicates that the random walk process in connection with the Kalman filter is the preferred model to describe and forecast the time-varying behavior of sector betas in a European context. Remarkably, the Markov switching models yield a worse out-of-sample performance than standard OLS.
    Keywords: Markov switching; Kalman filter; stochastic volatility; efficient Monte Carlo likelihood; bivariate t-GARCH; European industry portfolios; time-varying beta risk
    JEL: C22 C32 G10 G12 G15
    Date: 2005–10–26
  6. By: Jian Yang; Cheng Hsiao; Qi Li; Zijun Wang
    Abstract: This study examines the long-run price relationship and the dynamic price transmission among the U.S., Germany, and four major Eastern European emerging stock markets, with particular attention to the impact of the 1998 Russian financial crisis. The results show that both the long-run price relationship and the dynamic price transmission were strengthened among these markets after the crisis. The influence of Germany became noticeable on all the Eastern European markets only after the crisis but not before the crisis. We also conduct a rolling generalized VAR analysis to confirm the robustness of the main findings.
    Keywords: market linkages, emerging stock markets, generalized impulse response analysis, generalized forecast error variance decomposition, rolling VAR analysis
    JEL: G15 C32
    Date: 2005–07
  7. By: Matthias Fengler; Wolfgang Härdle; Enno Mammen
    Abstract: A primary goal in modelling the implied volatility surface (IVS) for pricing and hedging aims at reducing complexity. For this purpose one fits the IVS each day and applies a principal component analysis using a functional norm. This approach, however, neglects the degenerated string structure of the implied volatility data and may result in a modelling bias. We propose a dynamic semiparametric factor model (DSFM), which approximates the IVS in a finite dimensional function space. The key feature is that we only fit in the local neighborhood of the design points. Our approach is a combination of methods from functional principal component analysis and backfitting techniques for additive models. The model is found to have an approximate 10% better performance than a sticky moneyness model. Finally, based on the DSFM, we devise a generalized vega-hedging strategy for exotic options that are priced in the local volatility framework. The generalized vega-hedging extends the usual approaches employed in the local volatility framework.
    Keywords: Smile, local volatility, generalized additive model, backfitting, functional principal component analysis
    JEL: C14 G12
    Date: 2005–03
  8. By: Wan, Alan T.K.; Zou, Guohua; Banerjee, Anurag
    Abstract: It is well known that the Durbin-Watson and several other tests for first-order autocorrelation have limiting power of either zero or one in a linear regression model without an intercept, and tend to a constant lying strictly between these values when an intercept term is present. This paper considers the limiting power of these tests in models with restricted coefficients. Surprisingly, it is found that with linear restrictions on the coefficients, the limiting power can still drop to zero even with the inclusion of an intercept in the regression. It is also shown that for regressions with valid restrictions, these test statistics have algebraic forms equivalent to the corresponding statistics in the unrestricted model.
    Date: 2004–03–01
  9. By: Pitarakis, Jean-Yves
    Abstract: Inferences about the presence or absence of threshold type nonlinearities in TAR models are conducted within models whose lag length has been estimated in a preliminary stage. Typically the null hypothesis of linearity is then tested against a threshold alternative on which the estimated lag length is imposed on each regime. In this paper we evaluate the properties of test statistics for detecting the presence of threshold effects in autoregressive models when this model uncertainty is taken into account. We show that this approach may lead to important distortions when the underlying model has truly threshold effects by establishing the limiting properties of the estimated lag length in the mispecified linear autoregressive fit and assessing the impact of this model uncertainty on the power of the tests. We subsequently propose a full model selection based approach designed to jointly detect the presence of threshold effects and optimally specify its dynamics and compare its performance with the traditional test based approach.
    Date: 2004–07–01

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