Expression of the guanine nucleotide exchange factor, mr-gef, is regulated during the differentiation of specific subsets of telencephalic neurons

We have performed a screen combining subtractive hybridization with PCR to isolate genes that are regulated when neuroepithelial (NE) cells differentiate into neurons. From this screen, we have isolated a number of known genes that have not previously been associated with neurogenesis, together with several novel genes. Here we report that one of these genes, encoding a guanine nucleotide exchange factor (GEF), is regulated during the differentiation of distinct neuronal populations. We have cloned both rat and mouse GEF genes and shown that they are orthologs of the human gene, MR-GEF, which encodes a GEF that specifically activates the small GTPase, Rap1. We have therefore named the rat gene rat mr-gef (rmr-gef) and the mouse gene mouse mr-gef (mmr-gef). Here, we will collectively refer to these two rodent genes as mr-gef. Expression studies show that mr-gef is expressed by young neurons of the developing rodent CNS but not by progenitor cells in the ventricular zone (VZ). The expression pattern of mr-gef during early telencephalic neurogenesis is strikingly similar to that of GABA and the LIM homeobox gene Lhx6, a transcription factor expressed by GABAergic interneurons generated in the ventral telencephalon, some of which migrate into the cortex during development. These observations suggest that mr-gef encodes a protein that is part of a signaling pathway involved in telencephalic neurogenesis; particularly in the development of GABAergic interneurons.

Market transparency and collusion: on the UK agricultural tractor registration exchange

We review the decision by the European Commission in the case of the UK Agricultural Registration Exchange. We propose a theoretical model, offering a basis for some of the intuitive arguments used by the Commission on the anti-competitive role of information exchange in the case of price and non price collusion. Market transparency on non price data is shown to be a collusion facilitating device which may achieve stability in otherwise unstable cartels.

Evaluating forecasts from SETAR models of exchange rates

We consider the forecasting performance of two SETAR exchange rate models proposed by Kräger and Kugler [J. Int. Money Fin. 12 (1993) 195]. Assuming that the models are good approximations to the data generating process, we show that whether the non-linearities inherent in the data can be exploited to forecast better than a random walk depends on both how forecast accuracy is assessed and on the ‘state of nature’. Evaluation based on traditional measures, such as (root) mean squared forecast errors, may mask the superiority of the non-linear models. Generalized impulse response functions are also calculated as a means of portraying the asymmetric response to shocks implied by such models.

Quantile forecasts of daily exchange rate returns from forecasts of realized volatility

Quantile forecasts are central to risk management decisions because of the widespread use of Value-at-Risk. A quantile forecast is the product of two factors: the model used to forecast volatility, and the method of computing quantiles from the volatility forecasts. In this paper we calculate and evaluate quantile forecasts of the daily exchange rate returns of five currencies. The forecasting models that have been used in recent analyses of the predictability of daily realized volatility permit a comparison of the predictive power of different measures of intraday variation and intraday returns in forecasting exchange rate variability. The methods of computing quantile forecasts include making distributional assumptions for future daily returns as well as using the empirical distribution of predicted standardized returns with both rolling and recursive samples. Our main findings are that the Heterogenous Autoregressive model provides more accurate volatility and quantile forecasts for currencies which experience shifts in volatility, such as the Canadian dollar, and that the use of the empirical distribution to calculate quantiles can improve forecasts when there are shifts

Medieval foreign exchange: a time series analysis

This chapter applies rigorous statistical analysis to existing datasets of medieval exchange rates quoted in merchants’ letters sent from Barcelona, Bruges and Venice between 1380 and 1310, which survive in the archive of Francesco di Marco Datini of Prato. First, it tests the exchange rates for stationarity. Second, it uses regression analysis to examine the seasonality of exchange rates at the three financial centres and compares them against contemporary descriptions by the merchant Giovanni di Antonio da Uzzano. Third, it tests for structural breaks in the exchange rate series.

Cross-correlations and cross-bicorrelations in Sterling exchange rates

This paper proposes two new tests for linear and nonlinear lead/lag relationships between time series based on the concepts of cross-correlations and cross-bicorrelations, respectively. The tests are then applied to a set of Sterling-denominated exchange rates. Our analysis indicates that there existed periods during the post-Bretton Woods era where the temporal relationship between different exchange rates was strong, although these periods have become less frequent over the past 20 years. In particular, our results demonstrate the episodic nature of the nonlinearity, and have implications for the speed of flow of information between financial series. The method generalises recently proposed tests for nonlinearity to the multivariate context.

A Double-threshold GARCH Model for the French Franc/Deutschmark exchange rate

This paper combines and generalizes a number of recent time series models of daily exchange rate series by using a SETAR model which also allows the variance equation of a GARCH specification for the error terms to be drawn from more than one regime. An application of the model to the French Franc/Deutschmark exchange rate demonstrates that out-of-sample forecasts for the exchange rate volatility are also improved when the restriction that the data it is drawn from a single regime is removed. This result highlights the importance of considering both types of regime shift (i.e. thresholds in variance as well as in mean) when analysing financial time series.

Bicorrelations and cross-bicorrelations as non-linearity tests and tools for exchange rate forecasting

This paper proposes and implements a new methodology for forecasting time series, based on bicorrelations and cross-bicorrelations. It is shown that the forecasting technique arises as a natural extension of, and as a complement to, existing univariate and multivariate non-linearity tests. The formulations are essentially modified autoregressive or vector autoregressive models respectively, which can be estimated using ordinary least squares. The techniques are applied to a set of high-frequency exchange rate returns, and their out-of-sample forecasting performance is compared to that of other time series models

Forecasting exchange rate volatility using conditional variance models selected by information criteria

This paper uses appropriately modified information criteria to select models from the GARCH family, which are subsequently used for predicting US dollar exchange rate return volatility. The out of sample forecast accuracy of models chosen in this manner compares favourably on mean absolute error grounds, although less favourably on mean squared error grounds, with those generated by the commonly used GARCH(1, 1) model. An examination of the orders of models selected by the criteria reveals that (1, 1) models are typically selected less than 20% of the time.

Episodic nonstationarity in exchange rates

We examine a method recently proposed by Hinich and Patterson (mimeo, University of Texas at Austin, 1995) for testing the validity of specifying a GARCH error structure for financial time series data in the context of a set of ten daily Sterling exchange rates. The results demonstrate that there are statistical structures present in the data that cannot be captured by a GARCH model, or any of its variants. This result has important implications for the interpretation of the recent voluminous literature which attempts to model financial asset returns using this family of models.

Chaos in foreign exchange markets: a sceptical view

This paper tests directly for deterministic chaos in a set of ten daily Sterling-denominated exchange rates by calculating the largest Lyapunov exponent. Although in an earlier paper, strong evidence of nonlinearity has been shown, chaotic tendencies are noticeably absent from all series considered using this state-of-the-art technique. Doubt is cast on many recent papers which claim to have tested for the presence of chaos in economic data sets, based on what are argued here to be inappropriate techniques.

Linear and non-linear (non-)forecastability of high-frequency exchange rates

This paper forecasts Daily Sterling exchange rate returns using various naive, linear and non-linear univariate time-series models. The accuracy of the forecasts is evaluated using mean squared error and sign prediction criteria. These show only a very modest improvement over forecasts generated by a random walk model. The Pesaran–Timmerman test and a comparison with forecasts generated artificially shows that even the best models have no evidence of market timing ability.

Testing for non-linearity in daily sterling exchange rates

A number of tests for non-linear dependence in time series are presented and implemented on a set of 10 daily sterling exchange rates covering the entire post Bretton-Woods era until the present day. Irrefutable evidence of non-linearity is shown in many of the series, but most of this dependence can apparently be explained by reference to the GARCH family of models. It is suggested that the literature in this area has reached an impasse, with the presence of ARCH effects clearly demonstrated in a large number of papers, but with the tests for non-linearity which are currently available being unable to classify any additional non-linear structure.

A measure of persistence in daily pound exchange rates

An alternative procedure to that of Lo is proposed for assessing whether there is significant evidence of persistence in time series. The technique estimates the Hurst exponent itself, and significance testing is based on an application of bootstrapping using surrogate data. The method is applied to a set of 10 daily pound exchange rates. A general lack of long-term memory is found to characterize all the series tested, in sympathy with the findings of a number of other recent papers which have used Lo's techniques.

Hot and cold IPO markets : the case of the stock exchange of Mauritius

The aim of this study is to assess the characteristics of the hot and cold IPO markets on the Stock Exchange of Mauritius (SEM). The results show that the hot issues exhibit, on average, a greater degree of underpricing than the cold issues, although the hot issue phenomenon is not a significant driving force in explaining this short-run underpricing. The results are consistent with the predictions of the changing risk composition hypothesis in suggesting that firms going public during hot markets are on average relatively more risky. The findings also support the time adverse selection hypothesis in that the firms’ quality dispersion is statistically different between hot and cold markets. Finally, the study concludes that firms which go public during hot markets do not underperform those going public in cold markets over the longer term.