Causal effects of the United States and Japan on Pacific-Rim stock markets: nonparametric quantile causality approach

This article adopts a nonparametric quantile causality approach to examine the causal effects of the U.S. and Japan stock markets on the stock markets of the Pacific-Rim region. This approach allows us to detect not only nonlinear causalities in conditional return (mean) and conditional volatility (variance) but also the asymmetries of causalities under extreme market conditions (bullish vs. bearish states). Our results provide significant evidence of causality in return and volatility at different points of the conditional distributions of returns, with the greater effects from the U.S. than from Japan. Asymmetric quantile causality patterns are particularly pronounced in the case of Japan.     

Herding on lottery-type stocks: evidence from the Chinese stock market

Previous studies have provided evidence that investors have gambling propensity in the stock market and exhibit a preference for lottery-type stocks. In this study, we use high total skewness and high maximum daily return (MAX) to measure lottery-type stocks and examine whether investors do exhibit distinct herding pattern in these stock types. Empirical results show that investors display stronger herding among lottery-type stocks, thereby indicating that such stocks induce correlated behaviour with the investors. In addition, we find that stocks with the highest skewness exhibit stronger herding under upmarkets, whereas stocks with the lowest skewness display stronger herding under downmarkets. Regarding the highest MAX portfolio, no significant herding asymmetry is seen between upmarket and downmarket. The results reported in this article demonstrate that comovement in stock returns may be partly attributed to the nonstandard preferences of investors in the stock market.     

Spillovers from the USA to stock markets in Asia: a quantile regression approach

This article analyses return spillovers from the USA to stock markets in Asia by means of quantile regressions. Traditional studies consider spillovers as effects of the conditional means of foreign returns onto the conditional means of chronologically succeeding domestic markets’ returns. We, by contrast, study the full range of quantiles of the conditional distribution of the domestic markets’ returns. This enables us to document the detailed structure of spillovers across return quantiles. Generally, we find spillovers from the USA to Asia to be negative. Specifically, however, we reveal an asymmetric structure of spillovers with an increasing negative magnitude from lower to upper return quantiles. Theoretically, this pattern is consistent with an asymmetric overreaction of traders in Asia to news from the US market. Extensions from the baseline model further suggest the presence of contagion throughout the financial crisis of 2007–2008 as well as of calm-down effects over weekends.     

On the detection of extreme movements and persistent behaviour in Mediterranean stock markets: a wavelet-based approach

We combine the global Hurst exponent and Morlet wavelet multi-resolution analysis (MRA) to investigate the dynamic behaviour of six selected stock markets in the Mediterranean region. Specifically, we employ the resonance coefficients and their power spectra to identify potential extreme movements and long-term dependence in stock returns. Using weekly data for the period 2005 to 2010, our results reveal that the wavelet MRA is able to reconstruct the effects of major extreme shocks on stock returns of studied markets, such as the Asian financial crisis, the 9/11 terrorist attacks and the 2007–2009 financial crisis. Moreover, the wavelet-based global Hurst exponent indicates the presence of long-term dependencies in stock returns of all the considered markets, except for France where the anti-persistent behaviour is detected. Overall, our findings are useful to assess the stock market efficiency and provide new insights into stock market dynamics over different time scales.     

A nonparametric kernel regression approach for pricing options on stock market index

Previous options studies typically assume that the dynamics of the underlying asset price follow a geometric Brownian motion (GBM) when pricing options on stocks, stock indices, currencies or futures. However, there is mounting empirical evidence that the volatility of asset price or return is far from constant. This article, in contrast to studies that use parametric approach for option pricing, employs nonparametric kernel regression to deal with changing volatility and, accordingly, prices options on stock index. Specifically, we first estimate nonparametrically the volatility of asset return in the GBM based on the Nadaraya–Watson (N–W) kernel estimator. Then, based on the N–W estimates for the volatility, we use Monte Carlo simulation to compute option prices under different settings. Finally, we compare the index option prices under our nonparametric model with those under the Black–Scholes model and the Stein–Stein model.     

Stock Returns and Volatility: an empirical study of Chinese stock markets

This paper uses GARCH models to analyse the relationship between returns and volatility on the Shanghai and Shenzhen Stock Exchanges in China. Empirical estimates using the sample data from 21 May 1992 to 2 February 1996 suggest that the variances of the returns in the two markets are best modeled by the GARCH-M (1,1) specification. Volatility transmission between the two markets (the volatility spill-over effect) is also found to exist. The results of one month ahead ex ante forecasts show that the conditional variances of the returns of the two stock markets exhibit a similar pattern.     

Linkages in international stock markets: evidence from a classification procedure

In this article, we propose a new approach to evaluate the predictable components in stock indices using a boosting-based classification technique, and we use this method to examine causality among the three main stock market indices in the world during periods of large positive and negative price changes. The empirical evidence seems to indicate that the Standard & Poors 500 index contains incremental information that is not present in either the FTSE 100 index (Financial Times Stock Exchange Index) or the Nikkei 225 index, and that could be used to enhance the predictability of the large positive and negative returns in the three main stock market indices in the world. This in turn would suggest a causality relationship running from the Standard & Poors 500 index to both the FTSE 100 and the Nikkei 225 indices.     

Cointegration networks in stock markets

We use a novel approach based on a combination of network and cointegration analysis to examine linkages between stock markets across market cycles. Our results show that long-run linkages are likely to be global rather than regional and that market turbulence increases linkages. However, we find no widespread common stochastic trends between markets and neither are we able to draw a conclusion that major financial markets display influences network linkages.     

Evolutionary stable stock markets

Summary. This paper shows that a stock market is evolutionary stable if and only if stocks are evaluated by expected relative dividends. Any other market can be invaded in the sense that there is a portfolio rule that, when introduced on the market with arbitrarily small initial wealth, increases its market share at the incumbents expense. This mutant portfolio rule changes the asset valuation in the course of time. The stochastic wealth dynamics in our evolutionary stock market model is formulated as a random dynamical system. Applying this theory, necessary and sufficient conditions are derived for the evolutionary stability of portfolio rules when relative dividend payoffs follow a stationary Markov process. These local stability conditions lead to a unique evolutionary stable portfolio rule according to which assets are evaluated by expected relative dividends (with respect to the objective probabilities).Received: 7 October 2003, Revised: 18 January 2005, JEL Classification Numbers: G11, D52, D81. Correspondence to: Klaus Reiner Schenk-HoppéWe are grateful to Jarrod Wilcox and William Ziemba for valuable comments. Financial support by the national center of competence in research Financial Valuation and Risk Management is gratefully acknowledged. The national centers in research are managed by the Swiss National Science Foundation on behalf of the federal authorities.     

Herding effects in order driven markets: The rise and fall of gurus

We introduce an order driver market model with heterogeneous traders that imitate each other on a dynamic network structure. The communication structure evolves endogenously via a fitness mechanism based on agents performance. We assess under which assumptions imitation, among noise traders, can give rise to the emergence of gurus and their rise and fall in popularity over time. We study the wealth distribution of gurus, followers and non followers and show that traders have an incentive to imitate and a desire to be imitated since herding turns out to be profitable. The model is then used to study the effect that different competitive strategies (i.e. chartist & fundamentalist) have on agents performance. Our findings show that positive intelligence agents cannot invade a market populated by noise traders when herding is high.     

Nonlinearities in emerging stock markets: evidence from Europe's two largest emerging markets

Recent developments in time series analysis allow proper modelling of nonlinearities in economic and financial variables. A growing body of research was dedicated to investigation of potential nonlinearities in conditional mean of many economic and financial variables, mainly concentrating in developed economies. However, nonlinearities in financial variables in developing economies have not been fully examined yet. In this article we investigate potential nonlinearity and cyclical behaviour of stock returns in Europe's two largest emerging stock markets, mainly in the Greek and Turkish stock markets. Specifically, we use STAR family models, which allow to model nonlinearities in the conditional mean, for modelling monthly returns on stock exchange indices of the Athens Stock Exchange and Istanbul Stock Exchange. Although we find no nonlinearity in conditional variance, we do find strong evidence in favour of nonlinear adjustment of stock returns. It is found that allowing for nonlinearity in conditional mean results in a superior model and provides good out-of-sample forecasts, which contradicts to efficient market hypothesis.     

Long memory revisit in Chinese stock markets: Based on GARCH-class models and multiscale analysis

In the present work we propose the rescaled range analysis (R/S), modified R/S method and detrended fluctuation analysis (DFA) to investigate the long memory property of Chinese stock markets based on the conditional and actual volatility series, and show that the stock markets in China display moderate positive degree of long memory. For the robustness, we implement the multiscale analysis on dynamic changes of time-varying Hurst exponents by applying the rolling window method based on DFA. Our results reveal that APGARCH model with the superior forecasting ability captures the long memory property better than other GARCH-class models for different time scale interval. Interestingly, the time-varying Hurst exponents of the sudden “jumps” for the conditional volatility calculated by the DFA method using the APGARCH model are smaller than that of the actual volatility series, which indicates that APGARCH model may underestimate the long memory property in the Chinese stock market. Our evidences provide new perspectives for the financial market forecasting.     

Herding,anti-herding behaviour in metal commodities futures: a novel portfolio-based approach

The purpose of this article is twofold. Motivated by the heated debate on the financialization of commodities, we examine the existence of herding behaviour in metal commodities futures. In order to identify any time-dependent properties reflected in time-varying parameters, we employ the overlapping rolling window regression technique. The empirical evidence confirms a time-varying anti-herding behaviour before the global financial crisis and the absence of herding or anti-herding behaviour during the crisis. Next we attempt to formally establish the link between the documented anti-herding behaviour and portfolio management with the use of dynamic conditional correlations via the DCC-GARCH family multivariate modelling. After specifying the correlations, an in-sample recursive dynamic Markowitz portfolio is constructed and monitored. By doing so, we attribute the anti-herding behaviour to different portfolio positioning and rebalancing. On the other hand, in the absence of herding or anti-herding behaviour, we document a shift in the correlations and covariances of the commodity futures especially during the crisis, resulting in a decrease of the portfolio weights together with a substantial cash flow towards the risk-free asset.     

2002股市十事件

<正> 公司治理心有余而力不足 1月7日,中国证监会以2002年“第一号文件”的形式与国家经贸委联合颁布了《上市公司治理准则》。上市公司治理结构的改进在我国有很强的现实性和针对性。《上市公司治理准则》是我国第一个全面、系统地规范上市公司行为的重要文件,是监管部门规范上市公司治理的又一重要举措,也是我国加入WTO后加快上市公司监管与国际接轨的重要措施。 4月26日,中国证监会、国家经贸委联合发出《关于开展上市公司建立现代企     

A nonlinear Granger causality test between stock returns and investor sentiment for Chinese stock market: a wavelet-based approach

In this article, we re-examine the causality between the stock returns and investor sentiment in China. The number of net added accounts is used as a proxy for investor sentiment. To mimic the different investment horizons of market participants, we use the wavelet method to decompose stock returns and investor sentiment into time series with different frequencies. Additionally, we test for nonlinear causal relationships based on Taylor series approximation. Our results indicate that there is a one-directional linear causality from stock returns to investor sentiment on the original series, while there is a strong bi-directional nonlinear causality between stock returns and investor sentiment at different timescales.     

Measuring competition in microfinance markets: a new approach

This paper employs a relatively new method of competition measurement, the Boone indicator, for data on 521 microfinance institutions (MFIs) in ten vibrant microfinance markets: Bangladesh, India, Nepal, Indonesia, the Philippines, Bolivia, Ecuador, Nicaragua, Mexico and Peru. This approach is able to measure competition on a yearly basis in market segments without considering the entire market, as other well-known methods – for instance, the Panzar-Rosse model – require. Stochastic frontier (SF) models have been employed to estimate the translog cost function (TCF) and then marginal costs are computed. The potential endogeneity of performance and costs are overcome by utilising a two-step GMM estimator. Results show that competition levels vary from country to country, and over the period 2003–2010 India and Nicaragua had the most competitive microfinance loan markets. Competition among the microfinance institutions in Bangladesh and Bolivia declined significantly over time, which may be due to the partial reconstitution of market power by the giant MFIs in these countries. Competition in other countries remained mostly unchanged over the years, in line with the consolidation and revitalisation of respective microfinance markets.     

US economic policy uncertainty and co-movements between Chinese and US stock markets

This paper investigates the impact of innovations in US economic policy uncertainty on the co-movements of China's A/B stock markets with the US stock market. We show that it is the absolute changes in the US economic policy uncertainty index that have a negative impact on the co-movements. The finding is robust to the asymmetric effects of non-policy-uncertainty shocks, to a break in the correlation structure, and to the four Chinese A/B stock markets investigated. Our results provide the first evidence regarding how stock market correlations are driven by policy-related uncertainty shocks in the international context.     

Calendar effects in Latin American stock markets

One of the most well-documented empirical regularities in international finance is the presence of calendar effects in historical stock returns. The literature focuses mainly on developed countries, and in general, emerging markets have not received much attention on this issue. We aim to bridge this gap by documenting the existence of significant and robust calendar effects for the main stock markets in Latin America. Upon performing an extreme bounds analysis that adjusts our estimations for model uncertainty, we find a significantly negative Monday effect, generally compensated by a significantly positive Friday effect. These effects are robust to model specification and are stable through time. Even though not as widespread, we also find evidence for a robust turn-of-the-month effect.