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Quantitative risk management using P...

Liu, Peng.

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Quantitative risk management using Python = an essential guide for managing market, credit, and model risk /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Quantitative risk management using Python/ by Peng Liu.
Reminder of title:	an essential guide for managing market, credit, and model risk /
Author:	Liu, Peng.
Published:	Berkeley, CA :Apress : : 2025.,
Description:	xx, 238 p. :ill., digital ;24 cm.
[NT 15003449]:	Chapter 1: Introduction to Quantitative Risk Management -- Chapter 2: Fundamentals of Risk and Return in Finance -- Chapter 3: Managing Credit Risk -- Chapter 4: Managing Market Risk -- Chapter 5: Risk Management Using Financial Derivatives -- Chapter6: Static and Dynamic Hedging -- Chapter 7: Managing Model Risk in Finance.
Contained By:	Springer Nature eBook
Subject:	Risk management - Data processing. -
Online resource:	https://doi.org/10.1007/979-8-8688-1530-0
ISBN:	9798868815300

Quantitative risk management using Python = an essential guide for managing market, credit, and model risk /
Liu, Peng.

Quantitative risk management using Pythonan essential guide for managing market, credit, and model risk /[electronic resource] :by Peng Liu. - Berkeley, CA :Apress :2025. - xx, 238 p. :ill., digital ;24 cm.

Chapter 1: Introduction to Quantitative Risk Management -- Chapter 2: Fundamentals of Risk and Return in Finance -- Chapter 3: Managing Credit Risk -- Chapter 4: Managing Market Risk -- Chapter 5: Risk Management Using Financial Derivatives -- Chapter6: Static and Dynamic Hedging -- Chapter 7: Managing Model Risk in Finance.

Gain an understanding of various financial risks, the benefits of portfolio diversification, and the fundamental trade-off between risk and return. This book takes an in-depth journey into the world of quantitative risk management using Python, focusing on credit and market risk, with an extension to model risk. You'll start by reviewing the different types of financial risk, the benefit of diversification in a portfolio, and the fundamental trade-off between risk and return. The book then offers an in-depth look at managing credit and market risk in today's dynamic markets, all with practical Python implementations. Moving on, you'll examine common hedging strategies used to manage investment positions, along with practical implementations on evaluating risk-adjusted, as well as downside risk measures. Finally, you'll be introduced to common risks related to the development and use of machine learning models in finance. Whether you're a finance professional, academic, or student, Quantitative Risk Management Using Python will empower you to make informed decisions in today's complex financial landscape. You will: Explore techniques to assess and manage the risk of default by borrowers or counterparties. Identify, measure, and mitigate risks arising from fluctuations in market prices. Understand how derivatives can be employed for risk management purposes. Delve into both static and dynamic hedging techniques to protect investment positions, including practical applications for evaluating risk-adjusted and downside risk measures. Identify and address risks associated with the development and deployment of machine learning models in financial contexts.

ISBN: 9798868815300

Standard No.: 10.1007/979-8-8688-1530-0doiSubjects--Topical Terms:

749272
Risk management
--Data processing.

LC Class. No.: HD61

Dewey Class. No.: 658.1550285

Quantitative risk management using Python = an essential guide for managing market, credit, and model risk /
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520 $a Gain an understanding of various financial risks, the benefits of portfolio diversification, and the fundamental trade-off between risk and return. This book takes an in-depth journey into the world of quantitative risk management using Python, focusing on credit and market risk, with an extension to model risk. You'll start by reviewing the different types of financial risk, the benefit of diversification in a portfolio, and the fundamental trade-off between risk and return. The book then offers an in-depth look at managing credit and market risk in today's dynamic markets, all with practical Python implementations. Moving on, you'll examine common hedging strategies used to manage investment positions, along with practical implementations on evaluating risk-adjusted, as well as downside risk measures. Finally, you'll be introduced to common risks related to the development and use of machine learning models in finance. Whether you're a finance professional, academic, or student, Quantitative Risk Management Using Python will empower you to make informed decisions in today's complex financial landscape. You will: Explore techniques to assess and manage the risk of default by borrowers or counterparties. Identify, measure, and mitigate risks arising from fluctuations in market prices. Understand how derivatives can be employed for risk management purposes. Delve into both static and dynamic hedging techniques to protect investment positions, including practical applications for evaluating risk-adjusted and downside risk measures. Identify and address risks associated with the development and deployment of machine learning models in financial contexts.
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