Quantitative Risk Management Using Python: An Essential Guide for Managing Market, Credit, and Model Risk - Tapa blanda

Acerca del autor

Peng Liu is an Assistant Professor of Quantitative Finance (Practice) at Singapore Management University and an adjunct researcher at the National University of Singapore. He holds a Ph.D. in statistics from the National University of Singapore and has over 10 years of working experience across the banking, technology, and hospitality industries. Peng is the author of Bayesian Optimization (Apress, 2023) and Quantitative Trading Strategies Using Python (Apress, 2023)

De la contraportada

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.