東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Non-linearity in econometric modelin...

Maitra, Sarit.

Linked to FindBook

Google Book

Amazon

博客來

Non-linearity in econometric modeling. = a practical approach /. Vol. 1

Record Type:	Electronic resources : Monograph/item
Title/Author:	Non-linearity in econometric modeling./ by Sarit Maitra.
Reminder of title:	a practical approach /
Author:	Maitra, Sarit.
Published:	Cham :Springer Nature Switzerland : : 2025.,
Description:	xix, 188 p. :ill., digital ;24 cm.
[NT 15003449]:	Importance of Filters in Data Processing Pipeline discusses about the challenges of dealing with real-world data and application of Kalman Filter to improve the reliability and accuracy of models -- Volatility Modeling discusses the common problem with volatility or variance and covers how volatility can be computed and modeled -- Hybrid Volatility Modeling discusses while GARCH volatility models remain valuable, a combination of GARCH and Neural Networks can offer better output considering the availability of data, computational power, and algorithmic advancements -- Dynamic Volatility and Option Valuation provides a practical and theoretical framework for pricing and analyzing options, utilizing advanced volatility modeling techniques -- Markov Switching Models, Threshold Auto Regressive Models, and Smooth Transition Model discusses the application Markov Switching Auto Regressive Model (MSAR) and Smooth Transition Auto Regressive (STAR) Model.
Contained By:	Springer Nature eBook
Subject:	Econometric models. -
Online resource:	https://doi.org/10.1007/978-3-032-06462-2
ISBN:	9783032064622

Non-linearity in econometric modeling. = a practical approach /. Vol. 1
Maitra, Sarit.

Non-linearity in econometric modeling.a practical approach /Vol. 1[electronic resource] :by Sarit Maitra. - Cham :Springer Nature Switzerland :2025. - xix, 188 p. :ill., digital ;24 cm. - Dynamic modeling and econometrics in economics and finance,v. 332363-8370 ;. - Dynamic modeling and econometrics in economics and finance ;v. 33..

Importance of Filters in Data Processing Pipeline discusses about the challenges of dealing with real-world data and application of Kalman Filter to improve the reliability and accuracy of models -- Volatility Modeling discusses the common problem with volatility or variance and covers how volatility can be computed and modeled -- Hybrid Volatility Modeling discusses while GARCH volatility models remain valuable, a combination of GARCH and Neural Networks can offer better output considering the availability of data, computational power, and algorithmic advancements -- Dynamic Volatility and Option Valuation provides a practical and theoretical framework for pricing and analyzing options, utilizing advanced volatility modeling techniques -- Markov Switching Models, Threshold Auto Regressive Models, and Smooth Transition Model discusses the application Markov Switching Auto Regressive Model (MSAR) and Smooth Transition Auto Regressive (STAR) Model.

Nonlinear models are indispensable in modern finance, yet their reliance on numerical root-finding methods introduces layers of complexity that demand careful attention. This textbook offers a comprehensive and accessible guide to understanding these challenges and applying advanced econometric techniques to real-world financial and economic time series data. Designed for students, professionals, and researchers with a foundational background in statistics, econometrics, and finance, this book bridges the gap between theory and practice. It introduces key concepts progressively, making it suitable for both intermediate and advanced readers. Each chapter is written in clear, approachable language, ensuring that even those with limited prior experience in econometrics can grasp and apply the material effectively. The book is organized into five chapters that progressively guide readers through key concepts in financial time series modeling. It begins with Chapter 1, which introduces data filtering techniques, emphasizing the Kalman Filter's role in improving model accuracy. Chapter 2 explores volatility modeling, addressing common challenges in measuring and interpreting variance in financial data. Chapter 3 builds on this by presenting hybrid approaches that combine GARCH models with neural networks to enhance predictive performance. Chapter 4 applies dynamic volatility models to option valuation, offering both theoretical insights and practical tools. Finally, Chapter 5 delves into regime-switching models, including MSAR (Markov Switching Auto Regressive) and STAR (Smooth Transition Auto Regressive), to capture nonlinear behaviors and structural shifts in time series data. Together, these chapters form a cohesive narrative on modeling the dynamic behavior of financial time series, with a particular emphasis on volatility and structural shifts. Whether you're a finance professional, economist, or data scientist, this book is an essential resource for mastering the tools and techniques that drive modern financial analysis.

ISBN: 9783032064622

Standard No.: 10.1007/978-3-032-06462-2doiSubjects--Topical Terms:

542933
Econometric models.

LC Class. No.: HB141

Dewey Class. No.: 330.015195

Non-linearity in econometric modeling. = a practical approach /. Vol. 1
LDR:04093nmm a2200337 a 4500 001 2422612
003 DE-He213
005 20260102120903.0
006 m d
007 cr nn 008maaau
008 260505s2025 sz s 0 eng d
020 $a 9783032064622 $q (electronic bk.)
020 $a 9783032064615 $q (paper)
024 7 $a 10.1007/978-3-032-06462-2 $2 doi
035 $a 978-3-032-06462-2
040 $a GP $c GP
041 0 $a eng
050 4 $a HB141
072 7 $a KCH $2 bicssc
072 7 $a BUS021000 $2 bisacsh
072 7 $a KCH $2 thema
082 0 4 $a 330.015195 $2 23
090 $a HB141 $b .M232 2025
100 1 $a Maitra, Sarit. $3 3786087
245 1 0 $a Non-linearity in econometric modeling. $n Vol. 1 $h [electronic resource] : $b a practical approach / $c by Sarit Maitra.
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2025.
300 $a xix, 188 p. : $b ill., digital ; $c 24 cm.
490 1 $a Dynamic modeling and econometrics in economics and finance, $x 2363-8370 ; $v v. 33
505 0 $a Importance of Filters in Data Processing Pipeline discusses about the challenges of dealing with real-world data and application of Kalman Filter to improve the reliability and accuracy of models -- Volatility Modeling discusses the common problem with volatility or variance and covers how volatility can be computed and modeled -- Hybrid Volatility Modeling discusses while GARCH volatility models remain valuable, a combination of GARCH and Neural Networks can offer better output considering the availability of data, computational power, and algorithmic advancements -- Dynamic Volatility and Option Valuation provides a practical and theoretical framework for pricing and analyzing options, utilizing advanced volatility modeling techniques -- Markov Switching Models, Threshold Auto Regressive Models, and Smooth Transition Model discusses the application Markov Switching Auto Regressive Model (MSAR) and Smooth Transition Auto Regressive (STAR) Model.
520 $a Nonlinear models are indispensable in modern finance, yet their reliance on numerical root-finding methods introduces layers of complexity that demand careful attention. This textbook offers a comprehensive and accessible guide to understanding these challenges and applying advanced econometric techniques to real-world financial and economic time series data. Designed for students, professionals, and researchers with a foundational background in statistics, econometrics, and finance, this book bridges the gap between theory and practice. It introduces key concepts progressively, making it suitable for both intermediate and advanced readers. Each chapter is written in clear, approachable language, ensuring that even those with limited prior experience in econometrics can grasp and apply the material effectively. The book is organized into five chapters that progressively guide readers through key concepts in financial time series modeling. It begins with Chapter 1, which introduces data filtering techniques, emphasizing the Kalman Filter's role in improving model accuracy. Chapter 2 explores volatility modeling, addressing common challenges in measuring and interpreting variance in financial data. Chapter 3 builds on this by presenting hybrid approaches that combine GARCH models with neural networks to enhance predictive performance. Chapter 4 applies dynamic volatility models to option valuation, offering both theoretical insights and practical tools. Finally, Chapter 5 delves into regime-switching models, including MSAR (Markov Switching Auto Regressive) and STAR (Smooth Transition Auto Regressive), to capture nonlinear behaviors and structural shifts in time series data. Together, these chapters form a cohesive narrative on modeling the dynamic behavior of financial time series, with a particular emphasis on volatility and structural shifts. Whether you're a finance professional, economist, or data scientist, this book is an essential resource for mastering the tools and techniques that drive modern financial analysis.
650 0 $a Econometric models. $3 542933
650 1 4 $a Econometrics. $3 542934
650 2 4 $a Quantitative Economics. $3 3538570
650 2 4 $a Statistics in Business, Management, Economics, Finance, Insurance. $3 3538572
650 2 4 $a Corporate Finance. $2 swd $3 1655050
650 2 4 $a Machine Learning. $3 3382522
650 2 4 $a Risk Management. $3 608953
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Dynamic modeling and econometrics in economics and finance ; $v v. 33. $3 3804507
856 4 0 $u https://doi.org/10.1007/978-3-032-06462-2
950 $a Economics and Finance (SpringerNature-41170)