Browsing by Author "Dobelman, John A."

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    Design and Validation of Ranking Statistical Families for Momentum-Based Portfolio Selection
    (2013-07-24) Tooth, Sarah; Thompson, James R.; Dobelman, John A.; Williams, Edward E.
    In this thesis we will evaluate the effectiveness of using daily return percentiles and power means as momentum indicators for quantitative portfolio selection. The statistical significance of momentum strategies has been well-established, but in this thesis we will select the portfolio size and holding period based on current (2012) trading costs and capital gains tax laws for an individual in the United States to ensure the viability of using these strategies. We conclude that the harmonic mean of daily returns is a superior momentum indicator for portfolio construction over the 1970-2011 backtest period.
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    Modeling SPX Volatility to Improve Options Pricing
    (Rice University, 2021) Aiman, Jared; Iglesias, Vicente; Sarkar, Sumit; Ensor, Katherine; Dobelman, John A.
    In this project, we develop a model to predict future stock market volatility and facilitate more accurate options pricing. The Black Scholes model gives an expected premium for an options contract; however, it uses an unknown fixed parameter referred to as volatility. We advance this by using a modified Glosten-Jagannathan-Runkle Generalized Autoregressive Conditional Heteroskedasticity (GJR-GARCH) model that uses previous returns, as well as the market’s expectation of future volatility, to better predict future volatility. Additionally, we apply an Autoregressive Moving Average (ARMA) model to predict the value of future stock prices. We find that our model is able to model volatility better than using either the market volatility or a traditional GJR-GARCH model alone. This is particularly true due to our model’s ability to capture the dependence between the S&P 500 returns and the changes in the market’s expectation of volatility.