This thesis consists of three studies which cover topics in the trading volume-market return volatility linkage, stock market return-aggregate mutual fund flow relationship as well as market return volatility-aggregate mutual fund flow interaction. Chapter 2 investigates the issue of volume-volatility linkage in the US market for the period 1990-2012 (S&P 500) and 1992-2012 (Dow Jones). We construct four sub-samples depending on three different structural points (the Asian Financial Crisis, the Dot-Com Bubble and the 2007 Financial Crisis).
By employing univariate and bivariate GARCH processes, we find positive (negative) bidirectional linkages between these two aforementioned variables in various cases of the estimation, while a mixed one is observed in the remainder of these cases.
Chapter 3 examines the issue of temporal ordering of the range-based stock market return (S&P 500 index) and aggregate mutual fund flow in the U.S. market for the period 1998-2012. We construct nine sub-samples represented by three fundamental cases of the whole data set. In addition, we take into consideration three essential indicators when In this thesis, we have considered issues in the field of trading volume, market return volatility, aggregate mutual fund flow and stock market return.
Chapter 2 has simultaneously investigated the dynamics and interactions of the volume-volatility link. We have been able to highlight different keys of behavioral features which were presented across the various univariate and bivariate formulations. We have considered several changes according to different chosen samples and discussed how these changes would affect the linkages amongst these two variables.
In particular, we have taken into account the case of structural breaks and employed different specifications of the univariate and bivariate GARCH processes in order to obtain all the changeable results.
We have employed a long span of daily data (1990-2012 for S&P 500, 1992-2012 for Dow Jones respectively) with four sub-sample periods. As a result, we have observed a mixed bidirectional feedback between volume and volatility (volatility affects volume positively whereas the reverse impact is of its opposite sign) in a variety of these selected sub-samples, while a negative (or positive) bidirectional linkage has been detected in the other sub-samples (volume has a negative or positive impact on volatility and vice versa).
In chapter 3, we have examined the dynamic interactions between stock market return and U.S. aggregate mutual fund flow. We have taken into consideration the 2000 Dot-Com Bubble, the 2007 Financial Crisis as well as the 2009 European Sovereign Debt Crisis, and discussed how these changes have affected the relationship among the variables mentioned previously.We have obtained a long span of daily data (from February 3rd 1998 to March 20th 2012), divided the whole data set into three different cases with nine sub-samples and applied the bivariate VAR model with four different GARCH processes for the purpose of capturing all the changeable results.
We have observed a bidirectional mixed feedback between return and flow for the majority of the samples obtained. In particular, the lagged values of flow have negatively affected return whereas the lagged values of return have a positive impact on flow. Nevertheless, we have detected a positive bi-directional causality between flow and return with respect to some sub-periods of up-/down-market movement.
Chapter 4 has studied the dynamic causalities between market return volatility and aggregate mutual fund flow in the U.S. market. With similarity to the sub-samples obtained in chapter 3, we have additionally employed two different measurements of market return volatility.
We have observed a negative bi-directional causality between volatility and flow in most cases of up-/down- market movements. This means that volatility has a negative impact on flow (particularly flow into mutual funds) and vice versa. However, a positive bi-directional causality has been noticed in some sub-samples of cyclical behavior. In other words, flow (specifically flow out of mutual funds) has a positive effect on volatility and vice versa.
In addition, we have presented a bidirectional mixed feedback between flow and volatility in the rest of the estimations. More specifically, volatility affects flow negatively whereas the reverse impact is of its opposite sign.
Last but not least, most of the bidirectional effects have been found to be quite robust to the dynamics of the different GARCH models employed in this thesis.