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The recent unprecedented levels reached by financial ratios have led to a re‐examination of their time‐series properties, with evidence of long memory and nonlinearity reported. The purpose of this paper is to re‐examine the nature of these series in the light of potential time‐variation in the unconditional mean.

The paper uses econometric techniques designed to capture fractional integration, nonlinearity and time‐variation in the unconditional mean level of a series.

Reported results support such time‐variation, with cyclical behaviour evident in the unconditional mean of each ratio. Evidence of nonlinearity is still apparent in the mean‐adjusted series.

A key result that arises is that accounting for this time‐variation appears to provide improved long horizon returns predictability.

The paper demonstrates that a nonlinear model incorporating a time‐varying mean improves returns predictability. This is of interest to market participants.

In this paper weekly volatility forecasts are considered with applications to risk management; in particular hedge ratios and VaR calculations, with the aim of identifying the most appropriate model for risk management practice.

The study considers a variety of models, including those typically employed within the risk management industry, such as averaging and smoothing techniques, as well as those favored in academic circles, such as the GARCH genre of models, and a more recent realized volatility approach which incorporates both the simplicity in construction favored by the finance industry and the flexibility and theoretical underpinnings recommended by academics.

The results support the view that this realized volatility measure provides not only superior volatility forecasts per se, but also allows for improved hedge ratio and VaR calculations.

The research findings carry practical implications for the conduct of risk management, namely that volatility forecasts are best obtained using the realized volatility approach.

It is therefore proposed that a future direction for risk management practice may be to utilize such measures, while more generally it is hoped that such approaches may improve the cross‐fertilization of ideas and practice between the academic and practitioner communities.

The purpose of this paper is to examine the dynamic dependence structure in credit risk between the money market and the derivatives market during 2004-2009. The authors use the TED spread to measure credit risk in the money market and CDS index spread for the derivatives market.

The dependence structure is measured by a time-varying Gaussian copula. A copula is a function that joins one-dimensional distribution functions together to form multivariate distribution functions. The copula contains all the information on the dependence structure of the random variables while also removing the linear correlation restriction. Therefore, provides a straightforward way of modelling non-linear and non-normal joint distributions.

The results show that the correlation between these two markets while fluctuating with a general upward trend prior to 2007 exhibited a noticeably higher correlation after 2007. This points to the evidence of credit contagion during the crisis. Three different phases are identified for the crisis period which sheds light on the nature of contagion mechanisms in financial markets. The correlation of the two spreads fell in early 2009, although remained higher than the pre-crisis level. This is partly due to policy intervention that lowered the TED spread while the CDS spread remained higher due to the Eurozone sovereign debt crisis.

The paper examines the relationship between the TED and CDS spreads which measure credit risk in an economy. This paper contributes to the literature on dynamic co-movement, contagion effects and risk linkages.

In order to assess the informational efficiency of African equity markets (AEMs), the purpose of this paper is to examine long memory in both equity returns and volatility using auto‐regressive fractionally integrated moving average (ARFIMA)‐FIGARCH/hyperbolic GARCH (HYGARCH) models.

In order to test for long memory, the behaviour of the auto‐correlation function for 11 AEMs is examined. Following the graphical analysis, the authors proceed to estimate ARFIMA‐FIGARCH and ARFIMA‐HYGARCH models, specifically designed to capture long‐memory dynamics.

The results show that these markets (largely) display a predictable component in returns; while evidence of long memory in volatility is very mixed. In comparison, results from the control of the UK and USA show short memory in returns while evidence of long memory in volatility is mixed. These results show that the behaviour of equity market returns and risks are dissimilar across markets and this may have implications for portfolio diversification and risk management strategies.

The results of the analysis may have important implications for portfolio diversification and risk management strategies.

The importance of this paper lies in it being the first to systematically analyse long‐memory dynamics for a range of AEMs. African markets are becoming increasingly important as a source of international portfolio diversification and risk management. Hence, the results here have implication for the conduct of international portfolio building, asset pricing and hedging.

We test for the validity of the smoothing and signalling hypotheses of dividend determination.

Using a VAR framework we examine the dynamic behaviour of share prices, dividends and earnings for 137 UK manufacturing and service companies, observed over the period 1970‐2003.

There is strong evidence of a contemporaneous relationship between prices, dividends and earnings, and little evidence of independence between these variables. Some evidence in favour of both the smoothing and the signalling hypothesis is obtained from causality tests, with perhaps more support for the latter hypothesis. However, there is considerable diversity in the causal relationships between prices, dividends and earnings.

No single hypothesis regarding the determination of dividends, and the predictive power of dividends for earnings and prices appears to dominate.

The results presented here are of interest to markets agents in that while they suggest there is no single transition mechanism linking prices, dividends and earnings, nevertheless these three variables are strongly correlated and exhibit varying degrees of causality.

The purpose of this paper is to examine the nature of equity ownership of state-owned enterprises (SOEs) for over 2,000 listed firms in China. The paper examines both the pattern of state ownership and the dynamics of stock returns and volatility. Firms under the control of SOEs dominate the Chinese stock markets and currently account for over three-quarters of total market capitalisation. Central SOEs are focused in strategic industries, while Local SOEs concentrate on pillar industries relating to consumer goods and services.

The authors obtain firm-level data from the Shanghai and Shenzhen stock markets and using panel estimation techniques examine the dynamics of returns, volatility and their relationship.

The authors report an increase in state control among listed firms compared to earlier reported figures. This is contradictory to the expectation of a lower state influence following China joining of the World Trade Organisation in 2001. In examining the behaviour of stock returns the authors find evidence of daily and monthly autocorrelations that are larger and of a different sign to that reported for western markets. The authors also report evidence of volatility persistence but little evidence of volatility asymmetry, again in contrast to that often reported for other markets. Finally, the authors find evidence of either no or a negative relationship between returns and volatility (risk) that differs from our usual view of risk aversion.

It is hoped, knowledge of these dynamics will increase the understanding of the Chinese equity market, which in turn is important for those engaged in international portfolio management and micro-structure modelling.

Reappraises the stylised facts of the contemporary UK business cycle and the robustness of associated sample moments to detrending under the Hodrick‐Prescott (HP) filter and an unobserved components (UC) model based on the structural time series mode of Harvey and advocated in this context by Harvey and Jaeger. For the majority of series considered, findings broadly confirm the earlier HP‐based results of Blackburn and Ravn, but important differences with previous results are reported for labour productivity, the real wage and the real interest rate. However, under neither detrending method are the anticipated cross‐correlations between output and the pivotal variables in standard real business cycle (RBC) models (labour productivity, real wages, the real interest rate and nominal variables) simultaneously confirmed. Indeed, on balance, these results may be interpreted as more suggestive of an orthodox demand‐led or policy‐induced cycle.

This paper aims to ask whether a range of stock market factors contain information that is useful to investors by generating a trading rule based on one-step-ahead forecasts from rolling and recursive regressions.

Using USA data across 3,256 firms, the authors estimate stock returns on a range of factors using both fixed-effects panel and individual regressions. The authors use rolling and recursive approaches to generate time-varying coefficients. Subsequently, the authors generate one-step-ahead forecasts for expected returns, simulate a trading strategy and compare its performance with realised returns.

Results from the panel and individual firm regressions show that an extended Fama-French five-factor model that includes momentum, reversal and quality factors outperform other models. Moreover, rolling based regressions outperform recursive ones in forecasting returns.

The results support notable time-variation in the coefficients on each factor, whilst suggesting that more distant observations, inherent in recursive regressions, do not improve predictive power over more recent observations. Results support the ability of market factors to improve forecast performance over a buy-and-hold strategy.

The results presented here will be of interest to both academics in understanding the dynamics of expected stock returns and investors who seek to improve portfolio performance through highlighting which factors determine stock return movement.

The authors investigate the ability of risk factors to provide accurate forecasts and thus have economic value to investors. The authors conducted a series of moving and expanding window regressions to trace the dynamic movements of the stock returns average response to explanatory factors. The authors use the time-varying parameters to generate one-step-ahead forecasts of expected returns and simulate a trading strategy.

The purpose of this paper is to estimate volatility in African stock markets (ASMs), taking account of periodic level shifts in the mean level of volatility, where the regime shifts are determined endogenously.

Volatility estimates are incorporated into standard volatility models to assess the impact of structural breaks on volatility persistence, long memory and forecasting performance for ASMs.

The results presented here indeed suggest that persistence and long memory in volatility are overestimated when regime shifts are not accounted for. In particular, application of breakpoint tests and a moving average procedure suggest that unconditional volatility displays substantial time variation.

A modification of the standard generalised autoregressive conditional heteroscedasticity model to allow for time variation in the unconditional variance generates improved volatility forecasting performance for some African markets.

This paper describes one of the first studies to incorporate endogenously determined regime shifts into volatility estimates and assess the impact of structural breaks on volatility persistence, long memory and forecasting performance for ASMs.