國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Stochastic finance with Pythondesign...

Nag, Avishek.

Stochastic finance with Pythondesign financial models from probabilistic perspective /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Stochastic finance with Pythonby Avishek Nag.
其他題名:	design financial models from probabilistic perspective /
作者:	Nag, Avishek.
出版者:	Berkeley, CA :Apress :2024.
面頁冊數:	xiv, 396 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Python (Computer program language)
電子資源:	https://doi.org/10.1007/979-8-8688-1052-7
ISBN:	9798868810527$q(electronic bk.)

Stochastic finance with Pythondesign financial models from probabilistic perspective /
Nag, Avishek.

Stochastic finance with Pythondesign financial models from probabilistic perspective /[electronic resource] :by Avishek Nag. - Berkeley, CA :Apress :2024. - xiv, 396 p. :ill., digital ;24 cm.

Part I - Foundations & Pre-requisites -- Chapter 1 - Introduction -- Chapter 2 - Finance Basics & Data Sources -- Chapter 3 - Probability -- Chapter 4 - Simulation -- Chapter 5 - Stochastic Process -- Part II - Basic Asset Price Modelling -- Chapter 6 - Diffusion Model -- Chapter 7 - Jump Models -- Part III - Financial Options Modelling -- Chapter 8 - Options & Black-Scholes Model -- Chapter 9 - PDE, Finite-Difference & Black-Scholes Model -- Part IV - Portfolios -- Chapter 10 - Portfolio Optimization.

Journey through the world of stochastic finance from learning theory, underlying models, and derivations of financial models (stocks, options, portfolios) to the almost production-ready Python components under cover of stochastic finance. This book will show you the techniques to estimate potential financial outcomes using stochastic processes implemented with Python. The book starts by reviewing financial concepts, such as analyzing different asset types like stocks, options, and portfolios. It then delves into the crux of stochastic finance, providing a glimpse into the probabilistic nature of financial markets. You'll look closely at probability theory, random variables, Monte Carlo simulation, and stochastic processes to cover the prerequisites from the applied perspective. Then explore random walks and Brownian motion, essential in understanding financial market dynamics. You'll get a glimpse of two vital modelling tools used throughout the book - stochastic calculus and stochastic differential equations (SDE). Advanced topics like modeling jump processes and estimating their parameters by Fourier-transform-based density recovery methods can be intriguing to those interested in full-numerical solutions of probability models. Moving forward, the book covers options, including the famous Black-Scholes model, dissecting it from both risk-neutral probability and PDE perspectives. A chapter at the end also covers the discovery of portfolio theory, beginning with mean-variance analysis and advancing to portfolio simulation and the efficient frontier.

ISBN: 9798868810527$q(electronic bk.)

Standard No.: 10.1007/979-8-8688-1052-7doiSubjects--Topical Terms:

215247
Python (Computer program language)

LC Class. No.: HG104

Dewey Class. No.: 332.0285

Stochastic finance with Pythondesign financial models from probabilistic perspective /
LDR:03100nmm a2200325 a 4500 001 673723
003 DE-He213
005 20241214115234.0
006 m d
007 cr nn 008maaau
008 250422s2024 cau s 0 eng d
020 $a 9798868810527$q(electronic bk.)
020 $a 9798868810510$q(paper)
024 7 $a 10.1007/979-8-8688-1052-7 $2 doi
035 $a 979-8-8688-1052-7
040 $a GP $c GP
041 0 $a eng
050 4 $a HG104
072 7 $a UMX $2 bicssc
072 7 $a COM051360 $2 bisacsh
072 7 $a UMX $2 thema
082 0 4 $a 332.0285 $2 23
090 $a HG104 $b .N147 2024
100 1 $a Nag, Avishek. $3 987316
245 1 0 $a Stochastic finance with Python $h [electronic resource] : $b design financial models from probabilistic perspective / $c by Avishek Nag.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2024.
300 $a xiv, 396 p. : $b ill., digital ; $c 24 cm.
505 0 $a Part I - Foundations & Pre-requisites -- Chapter 1 - Introduction -- Chapter 2 - Finance Basics & Data Sources -- Chapter 3 - Probability -- Chapter 4 - Simulation -- Chapter 5 - Stochastic Process -- Part II - Basic Asset Price Modelling -- Chapter 6 - Diffusion Model -- Chapter 7 - Jump Models -- Part III - Financial Options Modelling -- Chapter 8 - Options & Black-Scholes Model -- Chapter 9 - PDE, Finite-Difference & Black-Scholes Model -- Part IV - Portfolios -- Chapter 10 - Portfolio Optimization.
520 $a Journey through the world of stochastic finance from learning theory, underlying models, and derivations of financial models (stocks, options, portfolios) to the almost production-ready Python components under cover of stochastic finance. This book will show you the techniques to estimate potential financial outcomes using stochastic processes implemented with Python. The book starts by reviewing financial concepts, such as analyzing different asset types like stocks, options, and portfolios. It then delves into the crux of stochastic finance, providing a glimpse into the probabilistic nature of financial markets. You'll look closely at probability theory, random variables, Monte Carlo simulation, and stochastic processes to cover the prerequisites from the applied perspective. Then explore random walks and Brownian motion, essential in understanding financial market dynamics. You'll get a glimpse of two vital modelling tools used throughout the book - stochastic calculus and stochastic differential equations (SDE). Advanced topics like modeling jump processes and estimating their parameters by Fourier-transform-based density recovery methods can be intriguing to those interested in full-numerical solutions of probability models. Moving forward, the book covers options, including the famous Black-Scholes model, dissecting it from both risk-neutral probability and PDE perspectives. A chapter at the end also covers the discovery of portfolio theory, beginning with mean-variance analysis and advancing to portfolio simulation and the efficient frontier.
650 0 $a Python (Computer program language) $3 215247
650 0 $a Financial engineering $x Data processing. $3 648143
650 1 4 $a Python. $3 763308
650 2 4 $a Corporate Finance. $2 swd $3 406349
650 2 4 $a Financial Technology and Innovation. $3 918801
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/979-8-8688-1052-7
950 $a Professional and Applied Computing (SpringerNature-12059)