nep-ets New Economics Papers
on Econometric Time Series
Issue of 2012‒11‒24
seven papers chosen by
Yong Yin
SUNY at Buffalo

  1. Equivalence Between Out-of-Sample Forecast Comparisons and Wald Statistics By Peter Reinhard Hansen; Allan Timmermann
  2. Exponential GARCH Modeling with Realized Measures of Volatility By Peter Reinhard Hansen; Zhuo Huang
  3. Choice of Sample Split in Out-of-Sample Forecast Evaluation By Peter Reinhard Hansen; Allan Timmermann
  4. The Selection of ARIMA Models with or without Regressors By Søren Johansen; Marco Riani; Anthony C. Atkinson
  5. VAR for VaR: Measuring Tail Dependence Using Multivariate Regression Quantiles By Habert white; Tae-Hwan Kim; Simone Manganelli
  6. Independent Factor Autoregressive Conditional Density Model By Alexios Ghalanos; Eduardo Rossi; Giovanni Urga
  7. Hurst Exponents For Short Time Series By Jingzhao Qi; Huijie Yang

  1. By: Peter Reinhard Hansen (European University Institute and CREATES); Allan Timmermann (UCSD and CREATES)
    Abstract: We establish the equivalence between a commonly used out-of-sample test of equal predictive accuracy and the difference between two Wald statistics. This equivalence greatly simpli?es the computational burden of calculating recursive out-of-sample tests and evaluating their critical values. Our results shed new light on many aspects of the test and establishes certain weaknesses associated with using out-of-sample forecast comparison tests to conduct inference about nested regression models.
    Keywords: Out-of-sample Forecast Evaluation, Nested Models, Testing.
    JEL: C12 C53 G17
    Date: 2012–10–10
  2. By: Peter Reinhard Hansen (European University Institute and CREATES); Zhuo Huang (Peking University, National School of Development, China Center for Economic Research)
    Abstract: We introduce the Realized Exponential GARCH model that can utilize multiple realized volatility measures for the modeling of a return series. The model specifies the dynamic properties of both returns and realized measures, and is characterized by a flexible modeling of the dependence between returns and volatility. We apply the model to DJIA stocks and an exchange traded fund that tracks the S&P 500 index and find that specifications with multiple realized measures dominate those that rely on a single realized measure. The empirical analysis suggests some convenient simplifications and highlights the advantages of the new specification.
    Keywords: EGARCH, High Frequency Data, Realized Variance, Leverage Effect.
    JEL: C10 C22 C80
    Date: 2012–10–10
  3. By: Peter Reinhard Hansen (European University Institute and CREATES); Allan Timmermann (UCSD and CREATES)
    Abstract: Out-of-sample tests of forecast performance depend on how a given data set is split into estimation and evaluation periods, yet no guidance exists on how to choose the split point. Empirical forecast evaluation results can therefore be difficult to interpret, particularly when several values of the split point might have been considered. When the sample split is viewed as a choice variable, rather than being ?xed ex ante, we show that very large size distortions can occur for conventional tests of predictive accu- racy. Spurious rejections are most likely to occur with a short evaluation sample, while conversely the power of forecast evaluation tests is strongest with long out-of-sample periods. To deal with size distortions, we propose a test statistic that is robust to the effect of considering multiple sample split points. Empirical applications to predictabil- ity of stock returns and in?ation demonstrate that out-of-sample forecast evaluation results can critically depend on how the sample split is determined.
    Keywords: Out-of-sample forecast evaluation, data mining, recursive estimation, predictability of stock returns, in?ation forecasting.
    JEL: C12 C53 G17
    Date: 2012–02–07
  4. By: Søren Johansen (University of Copenhagen and CREATES); Marco Riani (Dipartimento di Economia, Università di Parma); Anthony C. Atkinson (Department of Statistics, London School of Economics)
    Abstract: We develop a $C_{p}$ statistic for the selection of regression models with stationary and nonstationary ARIMA error term. We derive the asymptotic theory of the maximum likelihood estimators and show they are consistent and asymptotically Gaussian. We also prove that the distribution of the sum of squares of one step ahead standardized prediction errors, when the parameters are estimated, differs from the chi-squared distribution by a term which tends to infinity at a lower rate than $\chi _{n}^{2}$. We further prove that, in the prediction error decomposition, the term involving the sum of the variance of one step ahead standardized prediction errors is convergent. Finally, we provide a small simulation study. Empirical comparisons of a consistent version of our $C_{p}$ statistic with BIC and a generalized RIC show that our statistic has superior performance, particularly for small signal to noise ratios. A new plot of our time series $C_{p}$ statistic is highly informative about the choice of model. On the way we introduce a new version of AIC for regression models, show that it estimates a Kullback-Leibler distance and provide a version for small samples that is bias corrected. We highlight the connections with standard Mallows $C_{p}$.
    Keywords: AIC, ARMA models, bias correction, BIC, $C_{p}$ plot, generalized RIC, Kalman filter, Kullback-Leibler distance, state-space formulation
    JEL: C22
    Date: 2012–11–08
  5. By: Habert white; Tae-Hwan Kim (School of Economics, Yonsei University); Simone Manganelli (European Central Bank, DG-Research)
    Abstract: This paper proposes methods for estimation and inference in multivariate, multi-quantile models. The theory can simultaneously accommodate models with multiple random variables, multiple confidence levels, and multiple lags of the associated quantiles. The proposed framework can be conveniently thought of as a vector autoregressive (VAR) extension to quantile models. We estimate a simple version of the model using market equity returns data to analyse spillovers in the values at risk (VaR) between a market index and financial institutions. We construct impulse-response functions for the quantiles of a sample of 230 financial institutions around the world and study how financial institution-specific and system-wide shocks are absorbed by the system. We show how our methodology can successfully identify both in-sample and out-of-sample the set of financial institutions whose risk is most sentitive to market wide shocks in situations of financial distress, and can prove a valuable addition to the traditional toolkit of policy makers and supervisors.
    Keywords: Quantile impulse-responses, spillover, codependence,CAViaR
    JEL: C13 C14 C32
    Date: 2012–08
  6. By: Alexios Ghalanos (Faculty of Finance, Cass Business School); Eduardo Rossi (Department of Economics and Management, University of Pavia); Giovanni Urga (Faculty of Finance, Cass Business School and University of Bergamo)
    Abstract: In this paper, we propose a novel Independent Factor Autoregressive Conditional Density (IFACD) model able to generate time-varying higher moments using an independent factor setup. Our proposed framework incorporates dynamic estimation of higher comovements and feasible portfolio representation within a non elliptical multivariate distribution. We report an empirical application, using returns data from 14 MSCI equity index iShares for the period 1996 to 2011, and we show that the IFACD model provides superior VaR forecasts and portfolio allocations with respect to the CHICAGO and DCC models.
    Keywords: Independent Factor Model, GO-GARCH, Independent Component Analysis, Timevarying Co-moments
    JEL: C13 C16 C32 G11
    Date: 2012–11
  7. By: Jingzhao Qi; Huijie Yang
    Abstract: A new concept, called balanced estimator of diffusion entropy, is proposed to detect scalings in short time series. The effectiveness of the method is verified by means of a large number of artificial fractional Brownian motions. It is used also to detect scaling properties and structural breaks in stock price series of Shanghai Stock market.
    Date: 2012–11

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