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Essays in Mathematical Finance and Machine Learning.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Essays in Mathematical Finance and Machine Learning.
Author:	Zhang, Zhang.
Published:	Ann Arbor : ProQuest Dissertations & Theses, 2021
Description:	202 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-02, Section: B.
Notes:	Advisor: Madan, Dillip B.
Contained By:	Dissertations Abstracts International83-02B.
Subject:	Applied mathematics.
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28412026
ISBN:	9798534661118

Essays in Mathematical Finance and Machine Learning.
Zhang, Zhang.

Essays in Mathematical Finance and Machine Learning. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 202 p.

Source: Dissertations Abstracts International, Volume: 83-02, Section: B.

Thesis (Ph.D.)--University of Maryland, College Park, 2021.

This item must not be sold to any third party vendors.

This dissertation consists of three independent essays. Chapter 1, “Exploring Machine Learning in Fixed Income Market” designs a decision support framework that can be used to provide suggested indications of future U.S. on-the-run 10Y Treasury market direction along with the associated probability of making that move. My primary innovation is proposing a framework for applying machine learning methods to U.S. fixed income market. The framework includes a newly proposed performance metric that combines profitability and randomness to select proper outperform models and a sliding window cross-validation method for streaming data learning. I find the Random Forest method provides a decent Sharpe ratio for trading U.S. 10Y Treasury in a “quarantined” testing set but underperforms on Spread trading (10Y Treasury and an asset swap) and Volatility trading (1M10Y Swaption Straddle). Chapter 2, “A Robust Trend Following Framework: Theory and Application” constructs a trend-following signal based on statistical theory and analytically analyzes its properties. I manage to reconcile our model's theoretical results with stylized facts about trend-following investing – the presence of a "CTA smile". Leveraging on the theoretical results, we proposed a prototype trend-following framework that is diversified across time-frames and assets. I also discuss the portfolio and risk management of the trend-following strategy. I illustrate the risk-budgeting approach can be used to enhance the trend-following framework. Different approaches to control the costs have also been discussed. Chapter 3, “Markov Modulated Bilateral Gamm Mean Reversion Model” proposed a Markov modulated Bilateral gamma mean-reversion model. Market practitioners argue the market has high volatility regimes and low volatility regimes. I argue the model can capture the mean reversion, asymmetries of returns of up moves and down moves, and other empirical regularities. I derived the characteristic function and provide preliminary parameter estimates by calibrating the model to VIX Index upon the assumption of stationary distribution to avoid using filter methodologies.

ISBN: 9798534661118Subjects--Topical Terms:

377601
Applied mathematics.
Subjects--Index Terms:

Fixed income market

Essays in Mathematical Finance and Machine Learning.
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520 $a This dissertation consists of three independent essays. Chapter 1, “Exploring Machine Learning in Fixed Income Market” designs a decision support framework that can be used to provide suggested indications of future U.S. on-the-run 10Y Treasury market direction along with the associated probability of making that move. My primary innovation is proposing a framework for applying machine learning methods to U.S. fixed income market. The framework includes a newly proposed performance metric that combines profitability and randomness to select proper outperform models and a sliding window cross-validation method for streaming data learning. I find the Random Forest method provides a decent Sharpe ratio for trading U.S. 10Y Treasury in a “quarantined” testing set but underperforms on Spread trading (10Y Treasury and an asset swap) and Volatility trading (1M10Y Swaption Straddle). Chapter 2, “A Robust Trend Following Framework: Theory and Application” constructs a trend-following signal based on statistical theory and analytically analyzes its properties. I manage to reconcile our model's theoretical results with stylized facts about trend-following investing – the presence of a "CTA smile". Leveraging on the theoretical results, we proposed a prototype trend-following framework that is diversified across time-frames and assets. I also discuss the portfolio and risk management of the trend-following strategy. I illustrate the risk-budgeting approach can be used to enhance the trend-following framework. Different approaches to control the costs have also been discussed. Chapter 3, “Markov Modulated Bilateral Gamm Mean Reversion Model” proposed a Markov modulated Bilateral gamma mean-reversion model. Market practitioners argue the market has high volatility regimes and low volatility regimes. I argue the model can capture the mean reversion, asymmetries of returns of up moves and down moves, and other empirical regularities. I derived the characteristic function and provide preliminary parameter estimates by calibrating the model to VIX Index upon the assumption of stationary distribution to avoid using filter methodologies.
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