Hai-Chin Yu

Phase distribution and phase correlation of financial time series

The scaling, phase distribution, and phase correlation of financial time series are investigated based on the Dow Jones Industry Average and NASDAQ 10-min intraday data for a period from 1 Aug. 1997 to 31 Dec. 2003. The returns of the two indices are shown to have nice scaling behaviors and belong to stable distributions according to the criterion of Lévys alpha stable distribution condition. An approach catching characteristic features of financial time series based on the concept of instantaneous phase is further proposed to study the phase distribution and correlation. Analysis of the phase distribution concludes that return time series fall into a class which is different from other nonstationary time series. The correlation between returns of the two indices probed by the distribution of phase difference indicates that there was a remarkable change of trading activities after the event of the 9/11 attack, and this change persisted in later trading activities.

Does the weekday effect of the yen/dollar spot rates exist in Tokyo, London, and New York? An analysis of panel probability distribution

Using probability distribution techniques, this article explores whether any differences exist between the returns and volatility of yen/dollar spot markets in Tokyo, London and New York. After the intraday returns were fit into probability distributions, New York is found to have the highest return, followed by London, and then Tokyo. In estimating the peaks and widths of the distributions of volatility, Tokyo is found to have the lowest volatility in the log-normal distribution, while London and New York show similar volatility distributions, implying similar investor risk-return preference behaviour in the London and New York markets. The findings also imply that arbitrage opportunities between London and New York could be trivial. After estimating the panel distribution from Monday to Friday across the three markets, we found that the Monday effect disappears. Instead, Tuesday shows negative and significantly lower returns. The Friday (weekend) effect no longer exists. Instead, Thursday shows a positive and significantly higher return than the other weekdays. Finally, the three major currency markets did not become more volatile after Japans deregulation in the foreign currency market in April 1998. On the contrary, they show less volatile behaviour than before deregulation. The probability distributions of volatility on different weekdays did not change significantly after deregulation. 1 This article had been presented in 13th Conference on Panel Data at the University of Cambridge 2006, and The Joint 14th Annual PBFEA and 2006 Annual FeAT Conference at Taipei.

Keiretsu Style Main Bank Relationships, R&D Investment, Leverage, and Firm Value: Quantile Regression Approach

Using quantile regression, our results provide explanations for the inconsistent findings that use conventional OLS regression in the extant literature. While the direct effects of RD while firms’ advantages with low RD whereas it is decreasing in high Q firms. Main banks add value for low to median Q firms, while value is destroyed for high Q firms. Meanwhile, we find the interacted effect of main bank and R&D investment which increases with firm value, only appears in medium quantiles, instead of low or high quantiles. Results of this work provide relevant implications for policy makers. Finally, we document that industry quantile effect is larger than the industry effect itself, given that most of the firms in higher quantiles gain from industry effects while lower quantile firms suffer negative effects. We also find the results of OLS are seriously influenced by outliers. In stark contrast, quantile regression results are impervious to either inclusion or exclusion outliers.