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Supervising Professor: (Grace) Qing Hao Using mergers and acquisitions ( M&As) from 26 countries between 2000 and 2012, I examine the role of foreign and domestic institutional investors in cross-border M&As. I have several findings. First, both foreign and domestic institutional ownerships increase significantly during the period 2000-2012. Meanwhile, the volume of the cross-border M&As does not increase during the same time period. Second, domestic institutional investors facilitate both domestic and cross-border M&As. However, this seems to be inconsistent with the negative impact of domestic institutional ownership on the intensity of cross-border M&A activity, as reported in Ferreira et al. (2010). I discover that domestic institutional investors facilitate domestic M&As more effectively than cross-border M&As, which contributes to the finding in Ferreira et al. (2010). Third, domestic institutional investors can facilitate cross-border M&As more effectively when the acquirer country has greater financial freedom than the target country. Last but not least, while previous studies use either Tobit or Ordinary Least Squares regressions to examine the determinants of country-level volume and intensity of cross-border M&A activity, I show that zero-inflated Poisson regressions should be used instead. ixTABLE OF CONTENTS Acknowledgements.

In this work, we adopt a predictor-corrector technique to examine the accuracy of the Fractional Black-Scholes (FBS) model. Compared to the standard Black-Scholes (B-S) model, FBS model involves one additional parameter, a Hurst value (H) providing information whether the time series exhibits persistent or anti-persistent behavior. The FBS model, as a result, has been shown to provide more accurate predictions of option price [Heo et al. (2017) and reference therein]. Estimation accuracy of volatility and H values are key to better option price estimates. However, volatility and Hurst values are unknown prior to the closing time; consequently, the estimation of option prices relies heavily on the accuracy of volatilities and Hurst parameter estimation. In this study we compare option price estimation accuracy using three variations of calculating H values, and two volatility measures. We estimate two H values using historic data using one-month data (21 trading days) and three-month data (63 trading days), respectively, and by using predicted volatility estimates obtained using a binomial method, as a predictor and then used them to estimate implied H values. We subsequently correct the predicted volatility measure using the implied H value, the predictor-corrector technique. We investigate the accuracy of these FBS models and examine effectiveness of this predictor-corrector technique using Euro currency option (XDE) data traded in NASDAQ from November 2007 to June 2016.