Simultaneous monetary policy announcements and international stock markets response: An intraday analysis

This paper investigates the return and volatility response of major European and US equity indices to monetary policy surprises by utilizing extensive intraday data on 5-min price quotes along with a comprehensive dataset on monetary policy decisions and macroeconomic news announcements. The results indicate that the monetary policy decisions generally exert immediate and significant influence on stock index returns and volatilities in both European and the US markets. The findings also show that press conferences held by the European Central Bank (ECB) that follow monetary policy decisions on the same day have a clear impact on European index return volatilities. This implies that they convey additional important information to market participants. Overall, our analysis suggests that the use of high frequency data is critical to separate the effect of monetary policy actions from those of macroeconomic news announcements on stock index returns and volatilities.     

Consumption-based CAPM models: International evidence

We examine the performance of several types of the consumption-based CAPM (C-CAPM) models to explore if consumption factors matter for determining excess returns across 17 MSCI country indexes. While the classic world C-CAPM does exhibit some power in explaining cross-sectional variations of expected excess returns, the model seems to require an implausibly large coefficient of risk aversion. The more sophisticated models including the heterogeneous C-CAPM, the world surplus consumption and the habit-formation models provide more reasonable estimates and add substantial explanatory power for the variation in the cross section of excess stock returns. Our results suggest that country-specific consumption risk is not fully diversified thus implying that stock returns are related to idiosyncratic consumption risk.     

MODELLING THE ADAPTATION OF BUSINESS CONTINUITY PLANNING BY BUSINESSES USING NEURAL NETWORKS

Business continuity planning is an important element of business continuity management and is regarded as a fundamental step towards reducing the negative impacts of business disruptions caused by internal and external hazardous events. Many businesses are not prepared for such events, and very few studies have tried to examine and model the factors that contribute to business continuity management planning by various companies. In this paper we propose and develop a feed‐forward neural network for modelling businesses continuity planning by businesses based on a dataset of 283 businesses operating in the Greater Toronto Area in Ontario, Canada. The fully connected neural network applied was trained on 65 % of the dataset records using different subsets of input variables. In order to preserve the generalization ability of the trained network, 15 % of the dataset records were used as a validation set for early stopping during the network's training process. Prediction capability of the trained networks was evaluated on 20 % and never‐seen records of the dataset. The classification ability of the networks was then analysed using receiver operating characteristic and detection error trade‐off curves, where the results obtained were promising. The equal error rate for the best models was 12 %, which reflects a very good accuracy of these models in predicting the existence of business continuity planning for a generic company. Copyright © 2011 John Wiley & Sons, Ltd.     

A Cramér-type large deviation theorem for sums of functions of higher order non-overlapping spacings

Let U ₁, U ₂, . . . , U _n–1 be an ordered sample from a Uniform [0,1] distribution. The non-overlapping uniform spacings of order s are defined as \({G_{i}^{(s)} =U_{is} -U_{(i-1)s}, i=1,2,\ldots,N^\prime, G_{N^\prime+1}^{(s)} =1-U_{N^\prime s}}\) with notation U ₀ = 0, U _n = 1, where \({N^\prime=\left\lfloor n/s\right\rfloor}\) is the integer part of n/s. Let \({ N=\left\lceil n/s\right\rceil}\) be the smallest integer greater than or equal to n/s, f _m (u), m = 1, 2, . . . , N, be a sequence of real-valued Borel-measurable functions. In this article a Cramér type large deviation theorem for the statistic \({f_{1,n} (nG_{1}^{(s)})+\cdots+f_{N,n} (nG_{N}^{(s)} )}\) is proved.     

The impact of news,oil prices,and global market developments on Russian financial markets1

This paper analyses the impact of news, oil prices, and international financial market developments on daily returns on Russian bond and stock markets. First, regarding returns, energy news affects returns, while news from the war in Chechnya is not significant. Market volatility does not appear to be sensitive to either type of news. Second, a significant effect of the growth in oil prices on Russian stock returns is detected. Third, the international influence on Russian financial markets depends upon the degree of financial liberalization. The higher the degree of financial liberalization, the stronger is the impact of US stock returns on Russian financial markets. In addition, banking reform and interest rate liberalization efforts seem to dictate the globalization of Russian stock markets, while it is the progress in liberalizing securities markets and non‐bank financial institutions that matters more for the globalization of Russian bond markets.     

Information flows within and across sectors in Chinese stock markets

We examine the patterns of information flows within and across sectors of the two Chinese stock exchanges in Shanghai and Shenzhen during 1994–2001. Using the generalized forecast error variance decomposition, we find a high degree of interdependence, indicating that the sectors are highly integrated and sector prices reflect information from other sectors. Industry is the most influential sector in both exchanges, while Finance in Shenzhen is the least integrated with other sectors. Implications of the findings for investors and policymakers are also discussed.     

Information transmission between the Gulf equity markets of Saudi Arabia and Bahrain

This paper examines whether the emerging Gulf markets of Saudi Arabia and Bahrain in conjunction with the US market exhibit cointegrating relationship. Additionally, the transmission of information and volatility spillover between the Gulf markets is explored using a bivariate EGARCH model. We find that although the markets are not cointegrated, the Gulf markets do share information flows. Specifically, we observe an asymmetric spillover of volatility from the smaller though more liberal and accessible Bahraini market to the larger and less accessible Saudi market. The observed difference in information processing may partly be due to a well-developed Bahraini financial sector that encourages wider participation by international investors who play a significant role in assimilating new information.     

Estimation of the scale matrix of a multivariate t-model under entropy loss

This paper deals with the estimation of the scale matrix of a multivariatet-model with unknown location vector and scale matrix to improve upon the usual estimators based on the sample sum of product matrix. The well-known results of the estimation of the scale matrix of the multivariate normal model under the assumption of entropy loss function have been generalized to that of a multivariatet-model. The paper is based on the first author’s unpublished Ph.D. dissertation ‘Estimation of the Scale Matrix of a Multivariate T-model’, University of Western Ontario, Canada. Present address: School of Mathematics and Statistics, The University of Sydney, NSW 2006, Australia.