The impact of US macroeconomic news on the Polish stock market

This paper investigates the impact of US macroeconomic news announcements on the intraday returns of Warsaw Stock Exchange indices. The WSE is the largest and the most liquid stock market among the new European Union member countries. By means of an event study we examine the response of three indices, namely the WIG20, the mWIG40 and the sWGI80, describing the stock price behavior of the largest, medium-sized and small firms, respectively. The results of the empirical analysis show that the stock prices of the largest firms react in the first minute after a news release. This indicates the relatively high efficiency of the WSE. The response of smaller firms’ stock returns is slower but more persistent. The most influential are the announcements of Nonfarm Payrolls describing the US labor market. The indices of the WSE react similarly to good and bad news about the US economy.     

Return anomalies on the Nikkei: Are they statistical illusions?

This study investigates the sensitivity of the long-term return anomaly observed on the Nikkei stock index to sample and method bias using daily data from the period 3 January 1980 to 31 October 2000. Initially, the CUSUM statistic is employed to identify sub-periods of sign shifts in the mean returns. We find that the null hypothesis of no long-term dependence is accepted for the whole sample and every sub-period using the modified rescaled range test, but not using the classical rescaled adjusted range test. We conclude that researchers may inadvertently introduce sample and method bias into their studies of the time series properties of the Nikkei unless sample period and method are considered.     

用修正重标极差法对上证指数长期记忆性的研究

本文以上证指数周收益率为研究对象,分别采用重标极差分析法和修正重标极差分析法,通过计算V统计量的值对其进行长期记忆性的检验。由于不能排除V统计量的值存在超出上侧分位点的可能性,本文进行了双侧检验,并分析了R/S分析法产生偏差的原因。得出上证指数周收益率时间序列并未表现出显著的长期记忆性的结论。     

Multifractal structure in Latin-American market indices

We study the multifractal nature of daily price and volatility returns of Latin-American stock markets employing the multifractal detrended fluctuation analysis. Comparing with the results obtained for a developed country (US) we conclude that the multifractality degree is higher for emerging markets. Moreover, we propose a stock market inefficiency ranking by considering the multifractality degree as a measure of inefficiency. Finally, we analyze the sources of multifractality quantifying the contributions of two factors, the long-range correlations of the time series and the broad fat-tail distributions. We find that the multifractal structure of Latin-American market indices can be mainly attributed to the latter.     

上海股市收益的多重分形分析——滑动窗MFDFA方法的应用

介绍了一种新的MFDFA(mu ltifractal detrended fluctuation analysis)方法,利用基于滑动窗技术的此MFDFA方法研究了上证综指日收益率序列的波动特征。结果表明:上证综指收益率序列具有多重分形特征,小幅波动具有持续性,大幅波动可能具有反持续性。     

Multifractal spectrum distribution based on detrending moving average

The time-singularity multifractal spectrum distribution (MFSD) has been proposed recently as a generalized singularity spectrum in a time varying framework. In this paper, we aim at putting forward a new algorithm i.e. MFSD based on detrending moving average (DMA-MFSD) to determine MFSD, which is also a generalization of multifractal detrending moving average (MF-DMA) method. We relate DMA-MFSD method to the MFSD based on the standard partition function, and prove that both approaches are equivalent for fractal time series with compact support. The performance of the DMA-MFSD methods with different moving windows is studied using synthetic fractional Gaussian noise (fGn), binomial multiplicative cascades (BMC) with analytical solutions and real sea clutter data. We find that the estimated DMA-MFSD is in good accordance with the detrended fluctuation analysis based multifractal spectrum distribution (DFA-MFSD) and the theoretical analysis. Overall, the backward DMA-MFSD method has the best performance, which provides the most accurate estimates of the time-singularity MFSD, while the centered DMA-MFSD method performs worse. In addition we find that the backward DMA-MFSD algorithm even outperforms the DFA-MFSD method in the computational complexity and precision.     

Moment condition failure in stock returns: UK evidence

We examine the issue of moments existence in the UK stock market. It is found that the second moment of stock returns is finite, and therefore, the infinite variance stable distribution is ruled out as a candidate for modelling stock returns. In contrast with the US evidence, we cannot rule out the possibility that the fourth moment is finite.     

Neural network forecasting for seasonal and trend time series

Neural networks have been widely used as a promising method for time series forecasting. However, limited empirical studies on seasonal time series forecasting with neural networks yield mixed results. While some find that neural networks are able to model seasonality directly and prior deseasonalization is not necessary, others conclude just the opposite. In this paper, we investigate the issue of how to effectively model time series with both seasonal and trend patterns. In particular, we study the effectiveness of data preprocessing, including deseasonalization and detrending, on neural network modeling and forecasting performance. Both simulation and real data are examined and results are compared to those obtained from the Box–Jenkins seasonal autoregressive integrated moving average models. We find that neural networks are not able to capture seasonal or trend variations effectively with the unpreprocessed raw data and either detrending or deseasonalization can dramatically reduce forecasting errors. Moreover, a combined detrending and deseasonalization is found to be the most effective data preprocessing approach.     

中国证券市场价格及波动率长记忆性实证研究

证券市场股价运动是否存在长记性,近年来的实证研究结论不一.其中重要原因是数据选择的局限和检验方法的不统一.通过自相关函数、修正的R/S和GPH方法对中国证券市场的日和五分钟数据进行了递进检验,得出了一些与众不同的结论.     

A theoretical analysis of trading rules: an application to the moving average case with Markovian returns

A general framework for analysing trading rules is presented. We discuss different return concepts and different statistical processes for returns. We then concentrate on moving average trading rules and show, in the case of moving average models of length two, closed form expressions for the characteristic function of realized returns when the underlying return process follows a switching Markovian Gaussian process. An example is included which illustrates the technique.     

Asymptotic Multivariate Dominance: A Financial Application

We propose a multivariate stochastic dominance relation aimed at ranking different financial markets/sectors from the point of view of a non-satiable risk averse investor. In particular, we assume that the vector of returns of a given market is in the domain of attraction of a symmetric stable Paretian law in order to take into account the asymptotic behaviour of the financial returns. We determine the stochastic dominance rule for stable symmetric distributions, where the stability parameter plays a crucial role. Consequently, the multivariate rule for ordering markets is based on a comparison between i) location parameters, ii) dispersion parameters, and iii) stability indices. Finally, we apply the method to the equity markets of the four countries with the highest gross domestic product in 2013, namely, the US, China, Japan and Germany. In this empirical comparison we examine the ex ante and ex post dominance between stock markets, either assuming that the returns are jointly (or conditionally, for a robust approach) Gaussian distributed, or in the domain of attraction of a stable sub-Gaussian law.     

Robust portfolio optimization with a hybrid heuristic algorithm

Estimation errors in both the expected returns and the covariance matrix hamper the construction of reliable portfolios within the Markowitz framework. Robust techniques that incorporate the uncertainty about the unknown parameters are suggested in the literature. We propose a modification as well as an extension of such a technique and compare both with another robust approach. In order to eliminate oversimplifications of Markowitz’ portfolio theory, we generalize the optimization framework to better emulate a more realistic investment environment. Because the adjusted optimization problem is no longer solvable with standard algorithms, we employ a hybrid heuristic to tackle this problem. Our empirical analysis is conducted with a moving time window for returns of the German stock index DAX100. The results of all three robust approaches yield more stable portfolio compositions than those of the original Markowitz framework. Moreover, the out-of-sample risk of the robust approaches is lower and less volatile while their returns are not necessarily smaller.     

Study of Saudi Arabian climatic conditions using Hurst exponent and climatic predictability index

This paper utilizes Hurst exponent to study the persistency of meteorological parameters individually and dependency of rainfall/precipitation on pressure and temperature using climate predictability index. For the purpose, daily averages of surface pressure and temperature and daily total rainfall data for a period of 7 years for three locations and 14 years for seven locations has been utilized. The Hurst exponents (H) for above mentioned meteorological parameters were calculated using rescaled range analysis (R/S) and absolute moments methods. These H values were used to calculate the fractal dimension D for pressure, temperature and rainfall data. Finally, these D’s were used to calculate the climate predictability index PI_C in terms of pressure predictability index (PI_P), temperature predictability index (PI_T) and rainfall predictability index (PI_R). The Hurst exponent analysis showed that H values for rainfall, relative humidity and wind speed time series data for all the stations were >0.5 which is indicative of persistence behavior of the parameters on the previous values while for pressure and temperature H values were <0.5 means anti-persistence behavior. The climate predictability index showed that in most of the cases the rainfall was dependent on both pressure and temperature predictability indices. In some cases it was more dependent on pressure index than the temperature and in some cases otherwise. In Saudi Arabia there is no prevalent or established rainy season and the present analysis could not result into concrete results. It is therefore recommended to analyze the data by breaking the entire data set into seasons and further into different years.     

Volatility forecasting using stochastic conditional range model with leverage effect

In this paper, we propose a stochastic conditional range model with leverage effect (henceforth SCRL) for volatility forecasting. A maximum likelihood method based on the particle filters is developed to estimate the parameters of the SCRL model. Simulation results show that the proposed methodology performs well. We apply the proposed model and methodology to four stock market indices, the Shanghai Stock Exchange Composite Index of China, the Hang Seng Index of Hong Kong, the Nikkei 225 Index of Japan, and the S&P 500 Index of US. Empirical results highlight the value of incorporating leverage effect into range modeling and forecasting. In particular, the results show that our SCRL model outperforms the conditional autoregressive range model, the conditional autoregressive range model with leverage effect, and the stochastic conditional range model in both in‐sample fit and out‐of‐sample forecast.     

Semiparametric Bootstrap Approach to Hypothesis Tests and Confidence Intervals for the Hurst Coefficient

A major application of rescaled adjusted range analysis (R–S analysis) is to the study of price fluctuations in financial markets. There, the value of the Hurst constant, H, in a time series may be interpreted as an indicator of the irregularity of the price of a commodity, currency or similar quantity. Interval estimation and hypothesis testing for H are central to comparative quantitative analysis. In this paper we propose a new bootstrap, or Monte Carlo, approach to such problems. Traditional bootstrap methods in this context are based on fitting a process chosen from a wide but relatively conventional range of discrete time series models, including autoregressions, moving averages, autoregressive moving averages and many more. By way of contrast we suggest simulation using a single type of continuous-time process, with its fractal dimension. We provide theoretical justification for this method, and explore its numerical properties and statistical performance by application to real data on commodity prices and exchange rates. This revised version was published online in June 2006 with corrections to the Cover Date.     

股票回报率与汇率的关系——基于中国的实证

考察了上海股票市场A股的回报率与人民币汇率的关系．首先,经过单位根检验发现：股票回报率与人民币名义汇率是一阶单整。接着,利用Engle—Granger协整检验得到：在5％的显著性水平下,股票回报率与人民汇率没有长期均衡关系,但不能够拒绝短期单方向的Granger因果关系,即人民币名义汇率是股票回报率的Granger原因．     

One‐way analysis of variance with long memory errors and its application to stock return data

Recent empirical results indicate that many financial time series, including stock volatilities, often have long‐range dependencies. Comparing volatilities in stock returns is a crucial part of the risk management of stock investing. This paper proposes two test statistics for testing the equality of mean volatilities of stock returns using the analysis of variance (ANOVA) model with long memory errors. They are modified versions of the ordinary F statistic used in the ANOVA models with independently and identically distributed errors. One has a form of the ordinary F statistic multiplied by a correction factor, which reflects slowly decaying autocorrelations, that is, long‐range dependence. The other is a test statistic such that the degrees of freedom of the denominator in the ordinary F test statistic is calibrated by the so‐called effective sample size. Empirical sizes and powers of the proposed test statistics are examined via Monte Carlo simulation. An application to German stock returns is presented. Copyright © 2007 John Wiley & Sons, Ltd.     

中国股票市场的分形结构——股票收益长期记忆特征的实证研究

股票市场收益的长期记忆特征对于系统非线性结构的确定以及市场有效性的研究具有重要的意义.针对上海和深圳A股算术加权和流通市值加权市场指数的周收益序列以及上证180指数和深圳成份指数中选取的12只代表性股票的周收益序列,采用重标级差分析(R/S分析)和ARF IM A模型对其进行了实证研究.从统计结果来看,样本序列呈现出尖峰和肥尾等有偏特征,明显不满足正态分布的假设,表明收益序列可能具有长程相关或记忆性.进一步的研究发现,沪深两市A股市场指数收益序列和大多数个股(10只股票)存在明显的长期记忆特征,收益分布表现出持久性.从划分不同时段的分析结果来看,中国股票市场渐进趋于弱势有效.     

Coarsening in one dimension: invariant and asymptotic states

We study a coarsening process of one-dimensional cell complexes. We show that if cell boundaries move with velocities proportional to the difference in size of neighboring cells, then the average cell size grows at a prescribed exponential rate and the Poisson distribution is precisely invariant for the distribution of the whole process, rescaled in space by its average growth rate. We present numerical evidence toward the following universality conjecture: starting from any finite mean stationary renewal process, the system when rescaled by e ^?2t converges to a Poisson point process. For a limited case, this makes precise what has been observed previously in experiments and simulations, and lays the foundation for a theory of universal asymptotic states of dynamical cell complexes.