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on Econometric Time Series |
| By: | Christophe Boucher (CES - Centre d'économie de la Sorbonne - CNRS : UMR8174 - Université Panthéon-Sorbonne - Paris I, A.A.Advisors-QCG - ABN AMRO); Bertrand Maillet (CES - Centre d'économie de la Sorbonne - CNRS : UMR8174 - Université Panthéon-Sorbonne - Paris I, A.A.Advisors-QCG - ABN AMRO, EIF - Europlace Institute of Finance) |
| Abstract: | Researchers in finance very often rely on highly persistent - nearly integrated - explanatory variables to predict returns. This paper proposes to stand up to the usual problem of persistent regressor bias, by detrending the highly auto-correlated predictors. We find that the statistical evidence of out-of-sample predictability of stock returns is stronger, once predictors are adjusted for high persistence. |
| Keywords: | Forecasting, persistence, detrending, expected returns. |
| Date: | 2011–03 |
| URL: | http://d.repec.org/n?u=RePEc:hal:cesptp:halshs-00587775&r=ets |
| By: | Francois-Éric Racicot (Département des sciences administratives, Université du Québec (Outaouais), LRSP et Chaire d'information financière et organisationnelle); Raymond Théoret (Département de stratégie des affaires, Université du Québec (Montréal), Université du Québec (Outaouais), et Chaire d'information financière et organisationnelle) |
| Abstract: | In this paper, we aim at forecasting the stochastic volatility of key financial market variables with the Kalman filter using stochastic models developed by Taylor (1986, 1994) and Nelson (1990). First, we compare a stochastic volatility model relying on the Kalman filter to the conditional volatility estimated with the GARCH model. We apply our models to Canadian short-term interest rates. When comparing the profile of the interest rate stochastic volatility to the conditional one, we find that the omission of a constant term in the stochastic volatility model might have a perverse effect leading to a scaling problem, a problem often overlooked in the literature. Stochastic volatility seems to be a better forecasting tool than GARCH(1,1) since it is less conditioned by autoregressive past information. Second, we filter the S&P500 price-earnings (P/E) ratio in order to forecast its value. To make this forecast, we postulate a rational expectations process but our method may accommodate other data generating processes. We find that our forecast is close to a GARCH(1,1) profile. |
| Keywords: | Stochastic volatility; Kalman filter; P/E ratio forecast; Interest rate forecast. |
| JEL: | C13 C19 C49 G12 G31 |
| Date: | 2011–04–12 |
| URL: | http://d.repec.org/n?u=RePEc:pqs:wpaper:032011&r=ets |
| By: | Jason J. Wu; Aaron L. Game |
| Abstract: | This paper proposes a residual based cointegration test with improved power. Based on the idea of Hansen (1995) and Elliott & Jansson (2003) in the unit root testing case, stationary covariates are used to improve the power of the residual based Augmented Dickey Fuller (ADF) test. The asymptotic null distribution contains difficult to estimate nuisance parameters for which there is no obvious method of estimation, therefore we propose a bootstrap methodology to obtain test critical values. Local-to-unity asymptotics and Monte Carlo simulations are used to evaluate the power of the test in large and small samples, respectively. These exercises show that the addition of covariates increases power relative to the ADF and Johansen tests, and that the power depends on the long-run correlation between the covariates and the cointegration candidates. The new test is used to test for cointegration between Credit Default Swap (CDS) and corporate bond spreads for a panel of U.S. firms during the 2007-2009 financial crisis. The new test finds stronger evidence for cointegration between the two spreads for more firms, relative to ADF and Johansen tests. |
| Date: | 2011 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgfe:2011-18&r=ets |
| By: | Bernard Bercu; Frederic Proia |
| Abstract: | The purpose of this paper is to provide a sharp analysis on the asymptotic behavior of the Durbin-Watson statistic. We focus our attention on the first-order autoregressive process where the driven noise is also given by a first-order autoregressive process. We establish the almost sure convergence and the asymptotic normality for both the least squares estimator of the unknown parameter of the autoregressive process as well as for the serial correlation estimator associated to the driven noise. In addition, the almost sure rates of convergence of our estimates are also provided. It allows us to establish the almost sure convergence and the asymptotic normality for the Durbin-Watson statistic. Finally, we propose a new bilateral statistical test for residual autocorrelation. |
| Date: | 2011–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1104.3328&r=ets |
| By: | Louzis, Dimitrios P.; Xanthopoulos-Sisinis, Spyros; Refenes, Apostolos P. |
| Abstract: | In this paper, we assess the Value at Risk (VaR) prediction accuracy and efficiency of six ARCH-type models, six realized volatility models and two GARCH models augmented with realized volatility regressors. The α-th quantile of the innovation’s distribution is estimated with the fully parametric method using either the normal or the skewed student distributions and also with the Filtered Historical Simulation (FHS), or the Extreme Value Theory (EVT) methods. Our analysis is based on two S&P 500 cash index out-of-sample forecasting periods, one of which covers exclusively the recent 2007-2009 financial crisis. Using an extensive array of statistical and regulatory risk management loss functions, we find that the realized volatility and the augmented GARCH models with the FHS or the EVT quantile estimation methods produce superior VaR forecasts and allow for more efficient regulatory capital allocations. The skewed student distribution is also an attractive alternative, especially during periods of high market volatility. |
| Keywords: | High frequency intraday data; Filtered Historical Simulation; Extreme Value Theory; Value-at-Risk forecasting; Financial crisis. |
| JEL: | C13 C53 G32 G21 |
| Date: | 2011–04–18 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:30364&r=ets |
| By: | Lanne, Markku; Nyberg, Henri; Saarinen, Erkka |
| Abstract: | In this paper, we compare the forecasting performance of univariate noncausal and conventional causal autoregressive models for a comprehensive data set consisting of 170 monthly U.S. macroeconomic and financial time series. The noncausal models consistently outperform the causal models in terms of the mean square and mean absolute forecast errors. For a set of 18 quarterly time series, the improvement in forecast accuracy due to allowing for noncausality is found even greater. |
| Keywords: | Noncausal autoregression; forecast comparison; macroeconomic variables; financial variables |
| JEL: | C53 C22 E37 E47 |
| Date: | 2011–04–05 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:30254&r=ets |