Volume 11, Issue 1

This paper shows that the Zero-Information-Limit-Condition (ZILC) formulated by Nelson and Startz (2006) holds in the GARCH (1,1) model. As a result, the GARCH estimate tends to have too small a standard error relative to the true one when the ARCH parameter is small, even when sample size becomes very large. In combination with an upward bias in the GARCH estimate, the small standard error will often lead to the spurious inference that volatility is highly persistent when it is not. We develop an empirical strategy to deal with this issue and show how it applies to real datasets.

This paper investigates the transmission mechanisms of structural shocks and volatility between economies through trade links, and the effects of synchronization on business cycles. We investigate the transmission of outside structural shocks and the fluctuations that the shocks generate. We identify conditions under which international economic links reduce the volatility and unpredictability of economic output emanating from shocks within the individual economies. Under certain conditions, devaluation of a country's currency causes reduction in the unpredictability of the business cycle and its volatility as seen by that country's exporters, while increased valuation of a country's currency produces higher unpredictability and volatility, as seen by the country's importers.

In this paper we systematically compare forecasting accuracy of hypothesis testing procedures with that of a model combining algorithm. Testing procedures are commonly used in applications to select a model, based on which forecasts are made. However, besides the well-known difficulty in dealing with multiple tests, the testing approach has a potentially serious drawback: controlling the probability of Type I error at a conventional level (e.g., 0.05) often excessively favors the null, which can be problematic for the purpose of forecasting. In addition, as shown in this paper, testing procedures can be very unstable, which results in high variability in the forecasts.Selecting a candidate forecast by testing and combining forecasts are both useful but for complementary situations. Currently, there seems to be little guidance in the literature on when combining should be preferred to selecting. We propose instability measures that are helpful for a forecaster to gauge the difficulty in selecting a single optimal forecast.Based on empirical evidences and theoretical considerations, we advocate the use of forecast combining when there is considerable instability in model selection by testing procedures. On the other hand, when there is little instability, testing procedures could work well or even better than forecast combining in terms of forecast accuracy.

The quasi-geometric (hyperbolic) literature typically assumes that agents are short-run impatient. In this paper, we deviate from this assumption by considering an economy in which a fraction of the population is short-run patient and the remaining population is short-run impatient. In a calibrated version of a neoclassical growth model with uninsurable risk and liquidity constraints, we find that the presence of few short-run patient and many short-run impatient agents leads to empirically plausible degrees of wealth inequality.

This study presents a novel model for analyzing duration data, called the smooth transition autoregressive conditional duration model of price and duration, which considers past price changes and durations. The model enables the process of the conditional expected duration to switch in a smooth transition way, broadening the autoregressive conditional duration (ACD) model in Engle and Russell (1998). The model is applied to empirical data, and estimation results indicate that the process of the expected duration is nonlinear. The expected trade duration behavior on the market opening is affected by past trade durations, while the expected trade duration behavior during the trading hours is affected by past price changes and trade durations. Expected trade durations are much more persistent in the upward market compared to the downward market. Shocks to trade durations are more persistent on the market opening and gradually decrease in the downward market.

We consider long horizon regression models where the disturbance and the predictor are possibly fractionally integrated. Asymptotic distributions of the OLS estimator and of the test statistic are given. It is found that the t-statistic diverges at the rate of square root of T, where T is the sample size. Thus, it is desirable to use the scaled test statistic, as it converges to a well-defined limit, which depends on the memory parameters through the functionals on the fractional Wiener processes. Simulation studies present some empirical distributions of the scaled test statistic according to different values of the memory parameters. The proposed model with fractional processes is empirically more tractable than the model with local to unity processes, since memory parameters are consistently estimable unlike localizing parameters in the latter model.

Empirical results from long-horizon regression tests have been influential in the finance literature. Yet, it has come to be understood that traditional long-horizon tests may be unreliable in finite samples when regressors are persistent and when the horizon is long relative to sample size. Recent research has provided valid alternative inference procedures in long-horizon regression in the case for which the regressor follows a near-unit root autoregressive process. However, in small samples, such processes may sometimes be difficult to distinguish with confidence from other persistent data generating processes, such as those displaying long-memory or structural breaks. In this paper, we demonstrate a simple means by which existing nonparametric sign and signed rank tests may be applied to provide exact inference in long-horizon predictive tests, without requiring any modeling assumptions on the regressor. Employing this robust approach, we find evidence of stock return predictability at moderate horizons using short-term interest rates, but little evidence of either short or long-run predictability using dividend-price ratios.