語系:
繁體中文
English
說明(常見問題)
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Quantitative risk management using Python = an essential guide for managing market, credit, and model risk /
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Quantitative risk management using Python/ by Peng Liu.
其他題名:
an essential guide for managing market, credit, and model risk /
作者:
Liu, Peng.
出版者:
Berkeley, CA :Apress : : 2025.,
面頁冊數:
xx, 238 p. :ill., digital ; : 24 cm.;
Contained By:
Springer Nature eBook
標題:
Risk management - Data processing. -
電子資源:
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 Python
an 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:
583302
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 /
LDR
:03038nam a2200325 a 4500
001
1166634
003
DE-He213
005
20250903130201.0
006
m d
007
cr nn 008maaau
008
251217s2025 cau s 0 eng d
020
$a
9798868815300
$q
(electronic bk.)
020
$a
9798868815294
$q
(paper)
024
7
$a
10.1007/979-8-8688-1530-0
$2
doi
035
$a
979-8-8688-1530-0
040
$a
GP
$c
GP
041
0
$a
eng
050
4
$a
HD61
072
7
$a
UMX
$2
bicssc
072
7
$a
COM051360
$2
bisacsh
072
7
$a
UMX
$2
thema
082
0 4
$a
658.1550285
$2
23
090
$a
HD61
$b
.L783 2025
100
1
$a
Liu, Peng.
$3
897904
245
1 0
$a
Quantitative risk management using Python
$h
[electronic resource] :
$b
an essential guide for managing market, credit, and model risk /
$c
by Peng Liu.
260
$a
Berkeley, CA :
$b
Apress :
$b
Imprint: Apress,
$c
2025.
300
$a
xx, 238 p. :
$b
ill., digital ;
$c
24 cm.
505
0
$a
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.
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.
650
0
$a
Risk management
$x
Data processing.
$3
583302
650
0
$a
Python (Computer program language)
$3
566246
650
1 4
$a
Python.
$3
1115944
650
2 4
$a
Programming Language.
$3
1365750
650
2 4
$a
Financial Services.
$3
1108918
710
2
$a
SpringerLink (Online service)
$3
593884
773
0
$t
Springer Nature eBook
856
4 0
$u
https://doi.org/10.1007/979-8-8688-1530-0
950
$a
Professional and Applied Computing (SpringerNature-12059)
筆 0 讀者評論
多媒體
評論
新增評論
分享你的心得
Export
取書館別
處理中
...
變更密碼[密碼必須為2種組合(英文和數字)及長度為10碼以上]
登入