This paper considers a new approach to the analysis of stable relationships between nonstationary seasonal time series. The basis of this approach is an error correction model in which both long-run effects and adjustment parameters are allowed to vary per season. First, we discuss theoretical arguments for such a periodic error correction model. We define periodic cointegration and compare this to the concept of seasonal cointegration. Next, we analyze statistical inference in the periodic error correction model A sequential procedure is proposed, consisting of a test for periodic cointegration, an estimator of the cointegration parameters and adjustment coefficients, and a class of tests for the hypothesis that some of the parameters are constant over the seasons. The finite sample behavior of the proposed test statistics is analyzed in a limited Monte Carlo exercise. We conclude the paper with an application to a model of aggregate Swedish consumption.

The interpretation of seasonality in terms of economic behavior depends on the form of the econometric time-series model that allows for a description of seasonality. Popular models often assume either approximate deterministic seasonality (cf. Miron (1996)) or stochastic trend seasonality (cf. Hylleberg (1994)). Inference from an inappropriate model can be shown to be invalid. Since much graphical evidence clearly suggests that seasonal fluctuations are not constant over time, we investigate whether the finding of seasonal unit roots can be due to neglected mean shifts. We provide relevant asymptotic theory and critical values for various test statistics. For a set of real gross domestic product series we find that much evidence for seasonal unit roots tends to disappear when a shift in the seasonal means is allowed. When we incorporate deterministic mean shifts in the deterministic seasonality model, we find that qualitative results on the presence of the so-called seasonal cycle documented in, for example, Miron and Beaulieu (1996) are robust.

We propose a modeling framework which allows for creating probability predictions on a future market crash in the medium term, like sometime in the next five days. Our framework draws upon noticeable similarities between stock returns around a financial market crash and seismic activity around earthquakes. Our model is incorporated in an Early Warning System for future crash days. Testing our EWS on S&P 500 data during the recent financial crisis, we find positive Hanssen–Kuiper Skill Scores. Furthermore our modeling framework is capable of exploiting information in the returns series not captured by well known and commonly used volatility models. EWS based on our models outperform EWS based on the volatility models forecasting extreme price movements, while forecasting is much less time-consuming.

This paper provides an empirical description of the relationship between the trading system operated by a stock exchange and the trading behaviour of heterogeneous investors who use the exchange. The recent introduction of SETS in the London Stock Exchange provides an excellent opportunity to study the impact of an electronic trading system upon traders who use the exchange. Using the cost-of-carry model of futures prices we estimate (non-linearly) the transaction costs and trade speeds faced by arbitragers who take advantage of mispricing of FTSE100 futures contracts relative to the spot prices of the stocks that make up the FTSE100 stock index. We divide the sample period into pre-SETS and post-SETS sample periods and conduct a comparative study of arbitrager behaviour under different trading systems. The results indicate that there has been a significant reduction in the level of transaction costs faced by arbitragers and in the degree of transaction cost heterogeneity. Finally, generalised impulse response functions show that both spot and futures prices adjust more quickly in the post-SETS period. These results suggest that both spot and futures markets have become more efficient under SETS.