Browsing by Author "Habib, Abraham"
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Item Behavior of monthly total returns of U.S. government bonds : 1926-2007(American Society of. Business and Behavioral Sciences (ASBBS), 2010-03) Hamid, Shaikh A.; Habib, AbrahamWe explore for presence of monthly seasonality in monthly total returns of U.S. long term government bonds from January 1926 to December 2007. We test three types of effects with respect to monthly seasonality. We further partition the data into three sub-periods and explore monthly seasonality. In addition, we explore monthly seasonality based on Republican and Democratic presidencies. We look at the nature of monthly returns during contraction and expansion periods, as well as periods of crisis. The mean of monthly total returns of long term government bonds for the entire data set (0.47%) was significantly greater than zero. The mean of monthly returns of none of the months was significantly greater than the mean of the other eleven months stacked together. We find evidence of month effect with respect to variances of monthly returns. When we partition the data into three sub-periods, we do not find any discernible monthly seasonality. We also find the mean of monthly returns during the Republican presidencies to be significantly higher than during the Democratic presidencies. Government bond returns were on average significantly higher during contraction periods than during expansion periods. The Great Depression was good for the bond market; war periods were comparatively not as attractive for bond investing because governments tend to peg interest rates during such periods. Though not fully efficient, the U.S. long term government bond market exhibits a high degree of efficiency.Item Can neural networks learn the Black-Scholes model? A simplified approach(Southern New Hampshire University, 2005) Hamid, Shaikh A.; Habib, AbrahamNeural networks have been shown to learn complex relationships. It would be interesting to see if the networks can be trained to learn the nonlinear relationship underlying Black-Scholes type models. Interesting hypothetical questions that can be raised are: If option pricing model had not been developed, could a technique like neural networks have learnt the nonlinear form of the Black-Scholes type model to yield the fair value of an option? Could the networks have learnt to produce efficient implied volatility estimates? Our results from a simplified neural networks approach are rather encouraging, but more for volatility outputs than for call prices.