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.

Predicting stock index volatility: can market volume help?

This paper explores a number of statistical models for predicting the daily stock return volatility of an aggregate of all stocks traded on the NYSE. An application of linear and non-linear Granger causality tests highlights evidence of bidirectional causality, although the relationship is stronger from volatility to volume than the other way around. The out-of-sample forecasting performance of various linear, GARCH, EGARCH, GJR and neural network models of volatility are evaluated and compared. The models are also augmented by the addition of a measure of lagged volume to form more general ex-ante forecasting models. The results indicate that augmenting models of volatility with measures of lagged volume leads only to very modest improvements, if any, in forecasting performance.

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.

Can profitable trading strategies be derived from investment best-sellers?

A glance along the finance shelves at any bookshop reveals a large number of books that seek to show readers how to ‘make a million’ or ‘beat the market’ with allegedly highly profitable equity trading strategies. This paper investigates whether useful trading strategies can be derived from popular books of investment strategy, with What Works on Wall Street by James P. O'Shaughnessy used as an example. Specifically, we test whether this strategy would have produced a similarly spectacular performance in the UK context as was demonstrated by the author for the US market. As part of our investigation, we highlight a general methodology for determining whether the observed superior performance of a trading rule could be attributed in part or in entirety to data mining. Overall, we find that the O'Shaughnessy rule performs reasonably well in the UK equity market, yielding higher returns than the FTSE All-Share Index, but lower returns than an equally weighted benchmark

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.

Low-cost momentum strategies

This paper analyses the impact of trading costs on the profitability of momentum strategies in the United Kingdom and concludes that losers are more expensive to trade than winners. The observed asymmetry in the costs of trading winners and losers crucially relates to the high cost of selling loser stocks with small size and low trading volume. Since transaction costs severely impact net momentum profits, the paper defines a new low-cost relative-strength strategy by shortlisting from all winner and loser stocks those with the lowest total transaction costs. While the study severely questions the profitability of standard momentum strategies, it concludes that there is still room for momentum-based return enhancement, should asset managers decide to adopt low-cost relative-strength strategies.

Idiosyncratic volatility and the pricing of poorly-diversified portfolios

This article examines the role of idiosyncratic volatility in explaining the cross-sectional variation of size- and value-sorted portfolio returns. We show that the premium for bearing idiosyncratic volatility varies inversely with the number of stocks included in the portfolios. This conclusion is robust within various multifactor models based on size, value, past performance, liquidity and total volatility and also holds within an ICAPM specification of the risk–return relationship. Our findings thus indicate that investors demand an additional return for bearing the idiosyncratic volatility of poorly-diversified portfolios.

Do long-short speculators destabilize commodity futures markets?

This paper contributes to the debate on the effects of the financialization of commodity futures markets by studying the conditional volatility of long–short commodity portfolios and their conditional correlations with traditional assets (stocks and bonds). Using several groups of trading strategies that hedge fund managers are known to implement, we show that long–short speculators do not cause changes in the volatilities of the portfolios they hold or changes in the conditional correlations between these portfolios and traditional assets. Thus calls for increased regulation of commodity money managers are, at this stage, premature. Additionally, long–short speculators can take comfort in knowing that their trades do not alter the risk and diversification properties of their portfolios.

Comparing tropical precipitation simulated by the Met Office NWP and climate models with satellite observations

Forecasts of precipitation and water vapor made by the Met Office global numerical weather prediction (NWP) model are evaluated using products from satellite observations by the Special Sensor Microwave Imager/Sounder (SSMIS) and Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) for June–September 2011, with a focus on tropical areas (308S–308N). Consistent with previous studies, the predicted diurnal cycle of precipitation peaks too early (by ;3 h) and the amplitude is too strong over both tropical ocean and land regions. Most of the wet and dry precipitation biases, particularly those over land, can be explained by the diurnal-cycle discrepancies. An overall wet bias over the equatorial Pacific and Indian Oceans and a dry bias over the western Pacific warmpool and India are linked with similar biases in the climate model, which shares common parameterizations with the NWP version. Whereas precipitation biases develop within hours in the NWP model, underestimates in water vapor (which are assimilated by the NWP model) evolve over the first few days of the forecast. The NWP simulations are able to capture observed daily-to-intraseasonal variability in water vapor and precipitation, including fluctuations associated with tropical cyclones.

Are investors guided by the news disclosed by companies or by journalists?

Most previous studies demonstrating the influential role of the textual information released by the media on stock market performance have concentrated on earnings-related disclosures. By contrast, this paper focuses on disposal announcements, so that the impacts of listed companies’ announcements and journalists’ stories can be compared concerning the same events. Consistent with previous findings, negative words, rather than those expressing other types of sentiment, statistically significantly affect adjusted returns and detrended trading volumes. However, extending previous studies, the results of this paper indicate that shareholders’ decisions are mainly guided by the negative sentiment in listed companies’ announcements rather than that in journalists’ stories. Furthermore, this effect is restricted to the announcement day. The average market reaction–measured by adjusted returns–is inversely related only when the announcements are ignored by the media, but the dispersion of market reaction–measured by detrended trading volume–is positively affected only when announcements are followed up by journalists.

The Met Office unified model global atmosphere 4.0 and JULES global land 4.0 configurations

We describe Global Atmosphere 4.0 (GA4.0) and Global Land 4.0 (GL4.0): configurations of the Met Office Unified Model and JULES (Joint UK Land Environment Simulator) community land surface model developed for use in global and regional climate research and weather prediction activities. GA4.0 and GL4.0 are based on the previous GA3.0 and GL3.0 configurations, with the inclusion of developments made by the Met Office and its collaborators during its annual development cycle. This paper provides a comprehensive technical and scientific description of GA4.0 and GL4.0 as well as details of how these differ from their predecessors. We also present the results of some initial evaluations of their performance. Overall, performance is comparable with that of GA3.0/GL3.0; the updated configurations include improvements to the science of several parametrisation schemes, however, and will form a baseline for further ongoing development.