Geon Ho Choe – författare

This self-contained book is the first of a two-volume set providing a thorough introduction to quantitative finance, covering both theoretical and computational methods. This volume covers stochastic analysis, option pricing theory, optimal portfolio investment, and bond pricing. Computer simulations in Matlab and Python are provided to illustrate theoretical ideas. Background in mathematics is included in the appendices and the level of familiarity with computer programming is kept to a minimum.

This self-contained book is the second of a two-volume set providing a thorough introduction to quantitative finance, covering both theoretical and computational methods. This volume covers numerical methods, including numerical solutions of ordinary and partial differential equations such as the Black–Scholes–Merton equation, as well as stochastic differential equations, Monte Carlo methods, estimation of implied volatility, stochastic volatility models, and Fourier transform methods for option pricing. The numerical methods are implemented in both Matlab and Python. Background in mathematics is included in the appendices and the level of familiarity with computer programming is kept to a minimum.

This self-contained book is the second of a two-volume set providing a thorough introduction to quantitative finance, covering both theoretical and computational methods. This volume covers numerical methods, including numerical solutions of ordinary and partial differential equations such as the Black–Scholes–Merton equation, as well as stochastic differential equations, Monte Carlo methods, estimation of implied volatility, stochastic volatility models, and Fourier transform methods for option pricing. The numerical methods are implemented in both Matlab and Python. Background in mathematics is included in the appendices and the level of familiarity with computer programming is kept to a minimum.

This book is an introduction to stochastic analysis and quantitative finance; it includes both theoretical and computational methods. Topics covered are stochastic calculus, option pricing, optimal portfolio investment, and interest rate models. Also included are simulations of stochastic phenomena, numerical solutions of the Black–Scholes–Merton equation, Monte Carlo methods, and time series. Basic measure theory is used as a tool to describe probabilistic phenomena. The level of familiarity with computer programming is kept to a minimum. To make the book accessible to a wider audience, some background mathematical facts are included in the first part of the book and also in the appendices. This work attempts to bridge the gap between mathematics and finance by using diagrams, graphs and simulations in addition to rigorous theoretical exposition. Simulations are not only used as the computational method in quantitative finance, but they can also facilitate an intuitive and deeper understanding of theoretical concepts.  Stochastic Analysis for Finance with Simulations is designed for readers who want to have a deeper understanding of the delicate theory of quantitative finance by doing computer simulations in addition to theoretical study. It will particularly appeal to advanced undergraduate and graduate students in mathematics and business, but not excluding practitioners in finance industry.

This book is an introduction to stochastic analysis and quantitative finance; it includes both theoretical and computational methods. Topics covered are stochastic calculus, option pricing, optimal portfolio investment, and interest rate models. Also included are simulations of stochastic phenomena, numerical solutions of the Black–Scholes–Merton equation, Monte Carlo methods, and time series. Basic measure theory is used as a tool to describe probabilistic phenomena. The level of familiarity with computer programming is kept to a minimum. To make the book accessible to a wider audience, some background mathematical facts are included in the first part of the book and also in the appendices. This work attempts to bridge the gap between mathematics and finance by using diagrams, graphs and simulations in addition to rigorous theoretical exposition. Simulations are not only used as the computational method in quantitative finance, but they can also facilitate an intuitive and deeper understanding of theoretical concepts.  Stochastic Analysis for Finance with Simulations is designed for readers who want to have a deeper understanding of the delicate theory of quantitative finance by doing computer simulations in addition to theoretical study. It will particularly appeal to advanced undergraduate and graduate students in mathematics and business, but not excluding practitioners in finance industry.  

Ergodic theory is hard to study because it is based on measure theory, which is a technically difficult subject to master for ordinary students, especially for physics majors. Many of the examples are introduced from a different perspective than in other books and theoretical ideas can be gradually absorbed while doing computer experiments. Theoretically less prepared students can appreciate the deep theorems by doing various simulations. The computer experiments are simple but they have close ties with theoretical implications. Even the researchers in the field can benefit by checking their conjectures, which might have been regarded as unrealistic to be programmed easily, against numerical output using some of the ideas in the book. One last remark: The last chapter explains the relation between entropy and data compression, which belongs to information theory and not to ergodic theory. It will help students to gain an understanding of the digital technology that has shaped the modern information society.