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In this paper, the dynamic correlation of Japanese stock returns is estimated by using the dynamic conditional correlation (DCC-GARCH) model to study their correlation dynamics empirically. It is difficult to fit the model to the whole stock market jointly at the same time; therefore, a network-based clustering is applied for the dimensionality reduction of the sample data. Two types correlation structures are estimated: homogeneous groups of stocks in a balanced size are created by clustering to observe within-group correlation, while a single portfolio that comprises group portfolio returns is also created to observe between-group correlation. The estimation result reveals dynamic changes in correlation intensity represented by the largest eigenvalue of the estimated correlation matrix. A higher level of correlation intensity and volatility are observed during the crisis periods, namely after both the Lehman collapse and the Great East Japan Earthquake, for the between- and within-group correlations. It is also confirmed that the pattern of correlation change is significantly different between the groups. The proposed method is useful for monitoring dynamic correlation of asset returns efficiently in a large scale of portfolio. [ABSTRACT FROM AUTHOR]

In this paper, we examine the behavior of stock returns and trading volume across the-day-of-the-week in the context of the Japanese Market. Several hypotheses are used to explain the day-of-the-week effect. Results indicate that Mondays have abnormal losses and low trading volume. Over other days the returns and the trading volume increase significantly once the market thickens, prices become more informative and the information effect diminishes. Our results do not support the outliers' hypothesis, the half-of-the-month hypothesis and the autocorrelation hypothesis. They are, however, consistent with the adverse selection and the overconfidence hypotheses. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, I use stock return data to test an intertemporal model of the current account. I find that the model performs well in three countries: the U.K., Canada, and Japan. Hall [Hall, R.E., 1978. Stochastic implication of the life cycle-permanent income hypothesis: theory and evidence. J. Polit. Econ. 86 (6), 971–987] points out that because stock price predicts the future state of the economy, it predicts consumption. Assuming that consumption depends on permanent income, my empirical finding indicates that a representative agent smoothes consumption based on stock market information. In other words, stock market returns yield information about permanent income. [Copyright &y& Elsevier]

This paper examines autocorrelation and cross-autocorrelation patterns for selected Asian stock returns. Special attention is given to examination of Asian stock returns and the impact on them of the past information. By employing a class of asymmetric specification of conditional mean and conditional variance models, we find the autocorrelation coefficient to be negative for the Japanese market and positive for the rest of the Asian markets studied. Our findings suggest that the Asian markets respond sensitively to the US market, especially on the down side. The asymmetric effects are found to be present in both mean and variance equations. The evidence is consistent with behavior in which investors in Asian markets tend to react more significantly to negative stock news originating from US sources than they do to positive news. [ABSTRACT FROM AUTHOR]

In this article, we first investigate the Gaussianity of Japanese stock return time series (214 daily, 18 weekly) by the Gaussianily test proposed by Kariya, Tsay, Terui and Li (1994) comprehensively and consistently. And it is observed that all the series are not Gaussian when the 6th order moment structures are taken into account, Up to the 4th order moments there are some series which are compatible with the Gaossianity. Secondly, we apply five well-known nonlinearity tests for stationary time series to the data set and examine the specific nonlinearity of the series. Some series strongly exhibit the specific types of nonlinearity. Typically the Nikkei daily index shows the TAR (Threshold Autoregressive) type nonlinearity. Comparing daily return series with weekly series, it is also shown that a central limit effect is working on the weekly stock returns, where daily information is accumulated over a week, in the sense that weekly returns are relatively closer to Gaussian. [ABSTRACT FROM AUTHOR]

This paper examines the dynamic behavior of the stock return volatility for Canada, Japan, Germany, and the United Kingdom. The evidence indicates that international stock return volatility is mainly influenced by the U.S. stock return volatility and the exchange rate volatility, supporting the international capital market integration hypothesis. There seems to be some correlation between stock return volatility and macroeconomic volatility, but the effect is relatively weaker. In addition to the economic fundamentals, the noise component is found to be time varying, confirming the AR(MA)CH specifications in the stock return models. [ABSTRACT FROM AUTHOR]