CoFES White Paper Series

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https://hdl.handle.net/1911/111011

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Browsing CoFES White Paper Series by Author "Ensor, Katherine"

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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.
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    Predicting Student Loan Debt: A Hierarchical Time Series Analysis
    (Rice University, 2021) Elsesser, George; Ensor, Katherine
    In recent years, and especially in response to the Covid-19 pandemic, much attention has been brought to the issue of rapidly increasing student debt. Yet in the field of time series analysis, there is a dearth of studies examining trends in student loan debt. This is likely due to the impression of simple, yet steep, linear increase in student loan debt over the last decade. However, trends in this type of debt are much more complicated when a complete picture of the hierarchical nature of this data is considered. One objective of this project is to generate accurate forecasts with the impact of Covid-19 in mind not only for outstanding student loan debt, but also for sub-categories of this value based on loan status: such as loans in default or loans in repayment. To facilitate this, traditional hierarchical forecasting methods were compared to newer methods, namely MinT and its recent adaptations. Our findings indicate that MinT forecast reconciliation with the use of structural scaling results in the mostaccurate forecasts across the aggregation structure. Although not the main focus of this study, the second-level forecasts indicate a forecasted 3 1% increase in default rates between the first quarter of 2020 the second quarter of 2022 and a 12% decrease in dollars outstanding for enrolled students during the same time period.
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    Understanding Houston’s Growth: Real Estate New Development Data Analysis
    (Rice University, 2024-04-20) Ruan, Jian; Chi, Eric; Ensor, Katherine; McGuffey, Elizabeth
    Market research serves as the foundation for successful real estate investment and development, guiding strategic site selection and opportunity identification. This study focuses on analyzing the evolving landscape of real estate development in Houston through a multifaceted approach encompassing geographic information systems (GIS) visualization to explore the spatial dynamics and diversity of new developments, time series analysis to examine temporal trends of new development applications, and machine learning techniques to predict future urban growth trajectories. K-means clustering is employed to segment the market and reveal unique characteristics based on development distribution, while the impact of new developments on nearby home values is quantified through linear panel regression and tree-based models, offering insights into the complex relationships between development and property valuation. By integrating these complementary analyses, this research aims to provide a comprehensive understanding of Houston’s real estate development landscape, informing strategic decision-making processes and contributing to the interdisciplinary discourse on urban growth dynamics, market trends, and the interplay between development and valuation.