A Novel Global Equity Index for Spatial Correlation Analysis

Transcription

1 Finance and Banking (EAR6Paris Conference) ISBN: Paris, France. -3 July, 06. Paper ID: P659 A Novel Global Equy Index for Spatial Correlation Analysis Weiguo Zhang, School of Business Administration, South China. Universy of Technology, Guangzhou, P.R. China. wgzhang@scut.edu.cn Yong-Jun Liu, School of Business Administration, South China Universy of Technology, Guangzhou, P.R. China. bmyjliu@scut.edu.cn Abstract This paper studied the correlation of stock indexes from different countries. Based on spatial correlation theory and the stock index influence factors, we explore the space-time relationship of stock indexes in 43 countries by using the spatial panel econometric model, and we get the following conclusions: Stock index has significant spatial clustering; Correlation characteristics among global stock index are of great guiding significance to the international diversification investments; The stock index correlation also affects the return rates of stock market and financial asset pricing. Key Words: Global equy index, spatial correlation, spatial panel econometric model, global stock index JEL Classification: C6,G, D8 and C63

2 Finance and Banking (EAR6Paris Conference) ISBN: Paris, France. -3 July, 06. Paper ID: P659. Introduction The stock market is the macroeconomic "barometer" and "alarm", and raises the attention of government and investors. Also, becomes a hot issue of economic research and the scientific system. As the index of market price variation, the stock index can reflect the changing rules of the stock market in time. No matter from the view of management or investment, analyzing the relationship among stock index and s effecting factors is very important. At present, many scholars have studied the correlation of stock indexes from different countries. Lin et al. (04) analyzed the linkage structure among East Asian stock markets in different suations. Shen et al. (03) researched on the relationship among the stock markets in Europe, the Uned States, and China. Furthermore, some scholars studied the correlation from one or several effecting factors to explain how they impact on the stock index. Some scholars introduced spatial econometric analysis into the stock index. Pan and Zhang (0) used the spatial econometric to analyze how the macro factors affecting on the global stock index. Pui Sun Tam (04) analyzed the relationship among the East Asia's stock markets from the dimension of space or time respectively. Gema Fernández- Avilés (0) explored the linkage among 7 countries stock markets by spatial across modeling. Ouyang et al. ( 04)analyzed the relationship of the revenue rates of stock markets in Greater China plate(including Shanghai, Shenzhen, Taiwan, Hongkong). Besides, Xu et al. (0) studied the linkage of the revenue rate between CSI 300 stock index futures and the S&P 500 index. Huang and Yi (04) tended to explore the correlation of the stock index risk. In short, many scholars have studied on the correlation of stock indexes and have achieved fruful results, but there are still something to be improved: such as the research sample is too small which is confined to certain areas or a few countries, the research only from one or few several effecting factors to analyze the correlation, or some from the aspect of space cross section or time series respectively. For these reasons, this paper will use the spatial panel econometric model which considers spatial and temporal factors together to study the relationship of stock indexes among 43 countries (Argentina Australia Austria Belgium Brazil Canada Chile China Denmark Egypt Finland France Germany Greece India Indonesia Ireland Israel Italy Japan Republic of Korea Lebanon Malaysia Mexico Netherlands New Zealand Nigeria Norway Philippines Poland Portugal Russian Federation Saudi Arabia Singapore Spain Sweden Swzerland Thailand Turkey Ukraine Uned Arab Emirates Uned States Venezuela.), in order to figure out whether the stock indexes are relevant or whether there

3 Finance and Banking (EAR6Paris Conference) ISBN: Paris, France. -3 July, 06. Paper ID: P659 is a certain law of time and space in stock index, and try to put forward some suggestions and countermeasures.. Analysis of Spatial Correlation Theory and the Stock Index Influence Factors According to Tobler's First Law of Geography: The state of myriad things in the world can be only described by a three-dimensional coordinate system and a one-dimensional time coordinate, states in similar time or space distance are related to each other. Stock index should not be considered as independent wh each other in different countries. At the same time, the influent factors and theirs influence degrees of the stock indexes are different, showing spatial and temporal differences. Therefore, the stock index could not meet the hypothesis of OLS and fails to adopt the tradional regression analysis. Factors affecting the stock index can be divided into macro and micro levels. This paper uses the macro effects such as GDP, CPI instead of micro effects such as national system, culture background and so on, accounting that macro factors are basically the same but the micro factors have very different affecting on 43 countries stock indexes. Therefore, this paper only chooses the macroscopic factors which includes the economic growth (GDP), money supply (M), the rate of inflation (CPI), the level of short-term interest rates (R),the exchange rate (ExR), the State Reserve (Re), total import and export trade (Trade) etc.. 3. Model Specification and Estimation Methods The basic form of spatial panel econometric regression model is showed as follows: y y X, i t i j t jt, ~ N 0, I ) t () () ( n Where y is a n vector, is a k parameter vector which is related to the exogenous variables X, and are two spatial weight matrices, and are the space interferences, is a parameter in correlation space error. t and t represent the specific effects of individual and time point respectively. In the model (), when 0 the model is an ordinary panel data model and can be solved by the OLS regression; when, 0, the model changes into Spatial 0, Error Model (SEM); when 0, 0 the model becomes the Spatial Lag Model (SLM) and when, 0the model is the Spatial Durbin Model (SDM). 0, In the process of regression analysis, the model () can be expressed in natural log form like in model () to make the calculation simpler: lnstockindex lnexr 5 lnr 6 lngdp i 7 lntrade lnm ( ) lncpi lnstockindex 3 (jt) lnr 4 t ( ) () 3

4 Finance and Banking (EAR6Paris Conference) ISBN: Paris, France. -3 July, 06. Paper ID: P659 the left of the model () is natural logarhm of the stock index, the right of the model () is the impact factors. The meanings of other parameters are the same as (). To determine which model should be chosen, several indicators are usually used including Moran 's (spatial correlation coefficient) test, the Lagrange multiplier (Lagrange Multiplier, LMLAG, LMERR and LR-test) and the Robust Lagrangemultiplier (Robust Lagrange sound Multiplier, R-LMLAG, R-LMERR), degree of fting (R ) and so on. In the processing of regression, the spatial panel model needs to determine the spatial weight. The following four types of weight are mainly adopted: () the adjacent space weight based on the value of K which is set as 6 in this paper; () the adjacent space weight based on Queen; (3) weight based on geographic distance ;(4) weight based on economic distance. The final weight to choose determined by the Moran s I value, the variable coefficient and the value of t or p, also in accordance wh economic theory and realy. In the panel model, involves spatial data from 43 countries and the time span from - month in 03.The results of spatial weight and spatial analysis are from software such as ArcGIS, Geoda095i, Matlab8.0 and Gauss and so on. This paper finally chooses economic distance based on GDP as the ultimate spatial weights and calculate the Moran's I value. The result shows that the value reaches to 0.35, which fully shows that stock index are considerable spatial correlation among 43 countries. The correlation cluster distribution is shown as in Figure : Figure : The correlation cluster distribution in 43 countries 3 U.S. Canada Une states Turkey Russia Federation China Republic ofkorea Japan Thailand Mexico Brazil Nigeria Egypt Saudi Arabia Malaysia Indonesia Philippines Figure Legends Chile Argentina Some countries list:.venezuela 8.Germany.Portugal 9.Swizerland 3.Spain 0.Denmark 4.France.Norway 5.Ireland.Sweden 6.Ukraine 3.Finland 7.Poland Australia NewZealand From Figure, can be seen that there are mainly three types of cluster: High value- High value of stock index cluster (represented by the red spots), Low value-low value 4

5 Finance and Banking (EAR6Paris Conference) ISBN: Paris, France. -3 July, 06. Paper ID: P659 cluster (blue patches), and random distribution (blank spots in the figure). Of course, some local areas are shown to be the type of Low value-high value or High value-low value (represented by the pale blue patch and purple pink respectively). Countries belonging to the type of High value-high value cluster include America, Canada, Mexico, Argentina, and Australia China in Asia, and some countries in northern Europe. The stock index in these countries is relatively high, probably yields to theirs correct policy in international reserves and interest rates. Countries belonging to the Low value - low value cluster mainly include Russia, Southeast Asia and several small countries in Africa. In these cluster countries, their stock index is relatively low which maybe the total volume of trade in these countries is relatively small. The stock index in the USA and Brazil are not significantly gathered together. The reason may be that the regression model () takes less factors into account, such as omting the national system or cultural factors. All the analysis about the cluster type is based on the results of regression analysis. The international reserves and short-term interest rates have significantly posive relationship wh stock index, and the coefficient are 0.05 and 0.68 respectively; the total trade has a posive effect on the stock index on the level of 5% significance, and s coefficient is Conclusions From the angle of macro effecting factors, this paper explores the space - time relationship of stock indexes in 43 countries by using the spatial panel econometric model, and we get the following conclusions:. Stock index has significant spatial clustering. Using spatial econometrics model to study the volatily of the stock index can obtain more robust conclusions than the OLS model.. Correlation characteristics among global stock index are of great guiding significance to the international diversification investments, due to the reason that investors often do not consider the spatial correlation. According to the local spatial correlation, investments can have assets in different countries to reduce the spatial correlation risk. Also, according to the global spatial correlation, investments can grasp the trends of changing in the market as a whole to increase the benefs of investment. 3. The stock index correlation also affects the return rates of stock market and financial asset pricing, which may affect the ratio of value and loan as well as the effective of risk management. References Anselin, L., R. Florax 995, (.Introduction. In L. Anselin and R. Florax (Eds.), New Directions in Spatial.Econometrics.Berlin: Springer-Verlag. 5

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