Ebook: An Introduction to Continuous-Time Stochastic Processes: Theory, Models, and Applications to Finance, Biology, and Medicine

This concisely written book is a rigorous and self-contained introduction to the theory of continuous-time stochastic processes. A balance of theory and applications, the work features concrete examples of modeling real-world problems from biology, medicine, industrial applications, finance, and insurance using stochastic methods. No previous knowledge of stochastic processes is required.

Key topics covered include:

* Interacting particles and agent-based models: from polymers to ants

* Population dynamics: from birth and death processes to epidemics

* Financial market models: the non-arbitrage principle

* Contingent claim valuation models: the risk-neutral valuation theory

* Risk analysis in insurance

An Introduction to Continuous-Time Stochastic Processes will be of interest to a broad audience of students, pure and applied mathematicians, and researchers or practitioners in mathematical finance, biomathematics, biotechnology, and engineering. Suitable as a textbook for graduate or advanced undergraduate courses, the work may also be used for self-study or as a reference. Prerequisites include knowledge of calculus and some analysis; exposure to probability would be helpful but not required since the necessary fundamentals of measure and integration are provided.

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This concisely written book is a rigorous and self-contained introduction to the theory of continuous-time stochastic processes. A balance of theory and applications, the work features concrete examples of modeling real-world problems from biology, medicine, industrial applications, finance, and insurance using stochastic methods. No previous knowledge of stochastic processes is required.

Key topics covered include:

* Interacting particles and agent-based models: from polymers to ants

* Population dynamics: from birth and death processes to epidemics

* Financial market models: the non-arbitrage principle

* Contingent claim valuation models: the risk-neutral valuation theory

* Risk analysis in insurance

An Introduction to Continuous-Time Stochastic Processes will be of interest to a broad audience of students, pure and applied mathematicians, and researchers or practitioners in mathematical finance, biomathematics, biotechnology, and engineering. Suitable as a textbook for graduate or advanced undergraduate courses, the work may also be used for self-study or as a reference. Prerequisites include knowledge of calculus and some analysis; exposure to probability would be helpful but not required since the necessary fundamentals of measure and integration are provided.

Content:
Front Matter....Pages i-xi
Front Matter....Pages 1-1
Fundamentals of Probability....Pages 3-50
Stochastic Processes....Pages 51-126
The It? Integral....Pages 127-159
Stochastic Differential Equations....Pages 161-208
Front Matter....Pages 209-209
Applications to Finance and Insurance....Pages 211-238
Applications to Biology and Medicine....Pages 239-279
Back Matter....Pages 281-343

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This concisely written book is a rigorous and self-contained introduction to the theory of continuous-time stochastic processes. A balance of theory and applications, the work features concrete examples of modeling real-world problems from biology, medicine, industrial applications, finance, and insurance using stochastic methods. No previous knowledge of stochastic processes is required.

Key topics covered include:

* Interacting particles and agent-based models: from polymers to ants

* Population dynamics: from birth and death processes to epidemics

* Financial market models: the non-arbitrage principle

* Contingent claim valuation models: the risk-neutral valuation theory

* Risk analysis in insurance

An Introduction to Continuous-Time Stochastic Processes will be of interest to a broad audience of students, pure and applied mathematicians, and researchers or practitioners in mathematical finance, biomathematics, biotechnology, and engineering. Suitable as a textbook for graduate or advanced undergraduate courses, the work may also be used for self-study or as a reference. Prerequisites include knowledge of calculus and some analysis; exposure to probability would be helpful but not required since the necessary fundamentals of measure and integration are provided.

Content:
Front Matter....Pages i-xi
Front Matter....Pages 1-1
Fundamentals of Probability....Pages 3-50
Stochastic Processes....Pages 51-126
The It? Integral....Pages 127-159
Stochastic Differential Equations....Pages 161-208
Front Matter....Pages 209-209
Applications to Finance and Insurance....Pages 211-238
Applications to Biology and Medicine....Pages 239-279
Back Matter....Pages 281-343
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