Three Essays in Financial Economics

Thesis advisor: Zhijie XiaoThis dissertation consists of three independent studies in Financial Economics. The first chapter focuses on the predictive power of the implied correlation index on the future S&P 500 Index returns. The second chapter investigates a nonlinear contemporary relationship between stock returns and oil price changes. The last chapter discusses the relationship between impact trading costs and a number of market factors that affect the costs. In the first chapter, I investigate the predictive power of the implied correlation index on the future S&P 500 Index returns. This new index was launched by Chicago Board Option Exchange following 2007-2008 financial crisis. As it is derived from the S&P 500 Index option price and the option prices of the largest 50 S&P Index stocks, it is widely regarded by market participants as a gauge of average expected future stock return correlation. Because of its role in measuring systematic risks, any changes in this index may provide useful information about the future market movements. Motivated by this index's forward-looking characteristic, I propose a linear regression where the future S&P 500 Index multi-period return is regressed on a number of controls such as the current period changes of S&P index and the implied correlation index etc. I use weekly data and three different sample splits for in-sample estimation and out-of-sample performance evaluation. I find that the implied correlation index is informative for the period 2007-2009 in predicting the S&P 500 Index returns of 28 to 39 weeks. My model consistently outperforms the random walk model using the Superior Predictive Ability test. This implied correlation index is also useful in predicting the S&P index future multi-week returns for the period 2009-2011 and a longer time span from 2007-2011. I also do a test for the Efficient Market Hypothesis by incorporating the implied volatility index in the regression. There is no evidence supporting the view that the market is efficient for those time periods. In the second chapter, I estimate a nonlinear contemporary relationship between stock returns and oil price shocks. Previous studies on this issue suffer a number of limitations. For example, they do not control the factors potentially driving the economy and the oil market simultaneously. Although, Kilian and Park (2009) does a good job in identifying the relationship by distinguishing different oil market shocks, they use a linear regression framework and do not address the contemporary relationship. Considering the different impacts of different-size oil shocks on stock returns, I propose a two-step estimation procedure for identifying their relationship. In the first step, I follow Kilian and Park's methodology, i.e. a structural vector autoregression, to estimate the demand-specific oil shocks. During the second step, I use a nonparametric quantile regression to estimate the relationship between stock returns and the estimated exogenous oil price shocks. This way, I can control for the factors that simultaneously drive the economy and the oil market and am able to identify a nonlinear relationship of stock returns with oil shocks at the same time. The result shows that different-size oil price changes do have quite different impacts on stock returns. I also find an asymmetric effect of large oil shocks on large stock returns. Specifically, the positive impact of the large negative oil shocks on stock returns is much bigger than the negative impact of the large positive oil shocks on stock returns. I carry out a robust check by running regressions for a number of different model setups and the result persists. I also compare my model with Kilian and Park' SVAR model and it turns out that my model is a big improvement on their model in explaining the stock return variations. The third and last chapter focuses on impact trading cost and its relationship with several market factors. In this chapter, I focus on one of financial market microstructure issues, the immediate impact trading cost for major NASDAQ stocks. The immediate impact cost is the extra cost that market traders pay when they execute a large volume transaction without delay during the time when the market is less liquid. Because the market depth is defined to be the market's ability to sustain relatively large market orders without impacting the price of the security, this cost is closely linked to the trading volume. When trading volume becomes large, market liquidity gets worse and therefore the relationship between immediate impact cost and trading volume is virtually nonlinear. People trading in the market are interested in this relationship because they hope to figure out the best strategies in the situation where they want to execute a large volume order when the market is not deep. Another measure of market depth or liquidity people often use is market spread. Because it is the compensation for market makers' willingness to hold an imbalanced portfolio when the market is not liquid, it is regarded as another important factor linked to the impact cost. In this chapter, I use a nonparametric model to estimate the unknown relationship between immediate impact cost and market factors such as trading volume, market spread etc. for the major NASDAQ stocks. The result shows that, for many stock transactions, there is a certain volume threshold of trading volume beyond which impact costs increase dramatically. I find that for 99% of trading, immediate execution is optimal. I also identify a negative relationship between the occurrence likelihood of a large trading cost and the stock market cap.Thesis (PhD) — Boston College, 2012.Submitted to: Boston College. Graduate School of Arts and Sciences.Discipline: Economics