Robustness of the CUSUM and CUSUM-of-Squares Tests to Serial Correlation, Endogeneity and Lack of Structural Invariance. Some Monte Carlo Evidence

This paper investigates by means of Monte Carlo techniques the robustness of the CUSUM and CUSUM-of-squares tests (Brown et al., 1975) to serial correlation, endogeneity and lack of structural invariance. Our findings suggest that these tests perform better in the context of a dynamic model of the ADL type, which is not affected by serial correlation or nonpredetermined regressors even if over-specified. In this case, the empirical sizes of both tests are close to the nominal ones, whether a stationary or a cointegration environment is considered. The CUSUM-of-squares test is to be preferred, as it is very powerful to detect changes in the conditional model parameters, whether or not the variance of the regression error is included in the set of parameters shifting, especially towards the end of the sample.CUSUM and CUSUM-of-squares tests, Parameter instability, Structural invariance, Marginal and conditional processes, ADL model

Selectivity, market timing and the morningstar star-rating system

This paper evaluates the Morningstar mutual fund ranking system. We find that indeed higher Morningstar ratings are associated with higher returns on the portfolios including respectively five-, four-, three-, two- and one-star funds only (STAR5 to STAR1). We then perform an unconditional and conditional portfolio performance evaluation. In both cases the evidence suggests that the better performance of the STAR3, STAR4 and STAR5 categories reflects superior stock selection rather than market timing abilities. Overall, the implication for the Morningstar ranking system is that this is most effective in identifying the worst-performing funds (STAR1 or STAR2) rather than the best-performing ones

Looking far in the past: Revisiting the growth-returns nexus with non-parametric tests

In this paper we reexamine the linkages between output growth and real stock price changes for the G7 countries using a battery of non-parametric procedures to account for the impact of long-lagged observations. We find that correlation between growth and returns is detected at larger horizons than those typically employed in parametric studies. The major feedbacks emerge from stock price changes to growth within the first 6 to 12 months, but we show that significant feedbacks may last for up to two or three years. Our evidence also suggests that the correlation patterns differ substantially between the countries at hand when the sectoral share indices are considered.real stock price changes, output growth, long-run covariance matrix

Parameter Instability and Forecasting Performance. A Monte Carlo Study

This paper uses Monte Carlo techniques to assess the loss in terms of forecast accuracy which is incurred when the true DGP exhibits parameter instability which is either overlooked or incorrectly modelled. We find that the loss is considerable when a FCM is estimated instead of the true TVCM, this loss being an increasing function of the degree of persistence and of the variance of the process driving the slope coefficient. A loss is also incurred when a TVCM different from the correct one is specified, the resulting forecasts being even less accurate than those of a FCM. However, the loss can be minimised by selecting a TVCM which, although incorrect, nests the true one, more specifically an AR(1) model with a constant. Finally, there is hardly any loss resulting from using a TVCM when the underlying DGP is characterised by fixed coefficients.Fixed coefficient model, Time varying parameter models, Forecasting

The Contribution of Growth and Interest Rate Differentials to the Persistence of Real Exchange Rates.

This paper employs a new methodology for measuring the contribution of growth and interest rate differentials to the half-life of deviations from Purchasing Power Parity (PPP). Our method is based on directly comparing the impulse response function of a VAR model, where the real exchange rate is Granger caused by these variables with the impulse response function of a univatiate ARMA model for the real exchange rate. We show that the impulse response function of the VAR model is not, in general, the same with the impulse response function obtained from the equivalent ARMA representation, if the real exchange rate is Granger caused by other variables in the system. The difference between the two functions captures the effects of the Granger-causing variables on the half-life of deviations from PPP. Our empirical results for a set of four currencies suggest that real and nominal long term interest rate differentials and real GDP growth differentials account for 22% to 50% of the half-life of deviations from PPP.real exchange rate; persistence measures; VAR; impulse response function; PPP

Looking far in the past:Revisiting the growth-returns nexus with non-parametric tests

In this paper we reexamine the linkages between output growth and real stock price changes for the G7 countries using a battery of non-parametric procedures to account for the impact of long-lagged observations. We find that correlation between growth and returns is detected at larger horizons than those typically employed in parametric studies. The major feedbacks emerge from stock price changes to growth within the first 6 to 12 months, but we show that significant feedbacks may last for up to two or three years. Our evidence also suggests that the correlation patterns differ substantially between the countries at hand when the sectoral share indices are considered.real stock price changes, output growth, long-run covariance matrix

The Contribution of Growth and Interest Rate Differentials to the Persistence of Real Exchange Rates

This paper employs a new methodology for measuring the contribution of growth and interest rate differentials to the half-life of deviations from Purchasing Power Parity (PPP). Our method is based on directly comparing the impulse response function of a VAR model, where the real exchange rate is Granger caused by these variables with the impulse response function of a univatiate ARMA model for the real exchange rate. We show that the impulse response function of the VAR model is not, in general, the same with the impulse response function obtained from the equivalent ARMA representation, if the real exchange rate is Granger caused by other variables in the system. The difference between the two functions captures the effects of the Granger-causing variables on the half-life of deviations from PPP. Our empirical results for a set of four currencies suggest that real and nominal long term interest rate differentials and real GDP growth differentials account for 22% to 50% of the half-life of deviations from PPP.real exchange rate; persistence measures; VAR; impulse response function; PPP.

Selectivity, Market Timing and the Morningstar Star-Rating System

This paper evaluates the Morningstar mutual fund ranking system. We find that indeed higher Morningstar ratings are associated with higher returns on the portfolios including respectively five-, four-, three-, two- and one-star funds only (STAR5 to STAR1). We then perform an unconditional and conditional portfolio performance evaluation. In both cases the evidence suggests that the better performance of the STAR3, STAR4 and STAR5 categories reflects superior stock selection rather than market timing abilities. Overall, the implication for the Morningstar ranking system is that this is most effective in identifying the worst-performing funds (STAR1 or STAR2) rather than the best-performing ones.mutual fund, Morningstar Star-Rating System, CAPM, conditional and unconditional portfolio performance evaluation

Statistical modeling of stock returns: explanatory ordescriptive? A historical survey with some methodologicalreflections

The purpose of this paper is twofold: first, to survey the statistical models of stock returns that have been suggested in the finance literature since the middle of the twentieth century; second, to examine under the prism of the contemporary philosophy of science, which of the aforementioned models can be classified as explanatory and which as descriptive. Special emphasis is paid on tracing the interactions between the motivation for the birth of statistical models of stock returns in any given historical period and the concurrent changes of the theoretical paradigm in financial economics, as well as those of probability theory

The Bds Test As A Test For The Adequacy Of A Garch(1,1) Specification: A Monte Carlo Study

In this study we examine the widely used Brock, Dechert and Scheinkman (BDS) test when applied to the logarithm of the standardized residuals of an estimated GARCH(1,1) model as a test for the adequacy of this speciÞcation. We review the conditions derived by De Lima (1996, Econometric Reviews, 15, 237-259) for the nuisance-parameter free property to hold, and address the issue of their necessity, using the ßexible framework offered by the GARCH(1,1) model in terms of moment, memory and time heterogeneity properties. By means of Monte Carlo simulations, we show that the BDS test statistic still approximates the standard null distribution even for mildly explosive processes that violate the majority of the conditions. Thus, the test performs reasonably well, its empirical size being rather close to the nominal one. As a by-product of this study, we also shed light on the related issue of consistency of the QML estimators of the conditional variance parameters under various parameter conÞgurations and alternative distributional assumptions on the innovation process