東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

FindBook

Google Book

Amazon

博客來

Essays in Mathematical Finance and Machine Learning.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Essays in Mathematical Finance and Machine Learning./
作者:	Zhang, Zhang.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	202 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-02, Section: B.
Contained By:	Dissertations Abstracts International83-02B.
標題:	Applied mathematics. -
電子資源:	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:

2122814
Applied mathematics.
Subjects--Index Terms:

Fixed income market

Essays in Mathematical Finance and Machine Learning.
LDR:03409nmm a2200397 4500 001 2343826
005 20220513114328.5
008 241004s2021 ||||||||||||||||| ||eng d
020 $a 9798534661118
035 $a (MiAaPQ)AAI28412026
035 $a AAI28412026
040 $a MiAaPQ $c MiAaPQ
100 1 $a Zhang, Zhang. $3 3205920
245 1 0 $a Essays in Mathematical Finance and Machine Learning.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 202 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-02, Section: B.
500 $a Advisor: Madan, Dillip B.
502 $a Thesis (Ph.D.)--University of Maryland, College Park, 2021.
506 $a This item must not be sold to any third party vendors.
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.
590 $a School code: 0117.
650 4 $a Applied mathematics. $3 2122814
650 4 $a Artificial intelligence. $3 516317
650 4 $a Statistics. $3 517247
650 4 $a Finance. $3 542899
650 4 $a Deep learning. $3 3554982
650 4 $a Regression analysis. $3 529831
650 4 $a Investments. $3 566987
650 4 $a Generalized linear models. $3 3561810
650 4 $a Aggregates. $3 3564159
650 4 $a Approximation. $3 3560410
650 4 $a Noise. $3 598816
650 4 $a Performance evaluation. $3 3562292
650 4 $a Neural networks. $3 677449
650 4 $a Flexibility. $3 3560705
650 4 $a Classification. $3 595585
650 4 $a Support vector machines. $3 2058743
650 4 $a Variables. $3 3548259
650 4 $a Probability. $3 518898
650 4 $a Algorithms. $3 536374
650 4 $a Decision trees. $3 827433
653 $a Fixed income market
653 $a Decision support
653 $a Trend-following signal
653 $a Portfolio and risk management
653 $a Markov modulated Bilateral gamma mean-reversion model
690 $a 0364
690 $a 0800
690 $a 0501
690 $a 0508
690 $a 0463
710 2 $a University of Maryland, College Park. $b Applied Mathematics and Scientific Computation. $3 1021743
773 0 $t Dissertations Abstracts International $g 83-02B.
790 $a 0117
791 $a Ph.D.
792 $a 2021
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28412026