Quantitative Finance, Volume Three: Econometrics, Portfolio Construction, and Risk cover

Volume 3 of 4 · PDF edition

Econometrics, Portfolio Construction, and Risk

Estimate financial relationships, construct portfolios, measure performance, and manage market, credit, counterparty, liquidity, operational, and model risk.

Part of Quantitative Finance. The online chapters are free to read; the PDF edition is yours to keep, with future updates included.

Build the judgment to work confidently in quantitative finance.

Understand the assumptions behind a pricing model, the evidence behind a strategy, and the limits of a result. The book connects mathematical foundations to practical quantitative work so you can explain your choices and investigate what needs to change.

See what you’ll learn

What you will learn

  • Fit time-series, volatility, regression, factor, and calibration models without losing sight of estimation error and stability.
  • Turn forecasts and views into portfolios using mean-variance optimization, factor models, Black-Litterman, robust optimization, and risk parity.
  • Measure and control market, credit, counterparty, liquidity, operational, and model risk within a practical governance framework.

Who this volume is for

  • Portfolio managers, risk professionals, researchers, and analysts who want to connect statistical models with allocation and risk decisions.

Prerequisites: A working knowledge of statistics is useful. The probability, statistics, and linear algebra chapters in Volume 1 provide a suitable refresher.

Volume 3 PDF + code

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Keep the PDF and companion notebooks for offline reading, with future updates included. The corresponding chapters stay free online.

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Reader verdict

5.0

out of 5

12 five-star reader reviews

Genuine feedback, with names abbreviated for privacy.

Meet the community
“I am enjoying reading your writings. I appreciate the effort you put into it. I want to drop a note of thanks. Thank you!”
Krishnan S.Researcher
“I would like to thank you for your work. Specifically, the chapter: 'LIME Explainability: Complete Guide to Local Interpretable Model-Agnostic Explanations' was extremely helpful for me in understanding LIME and communicating to colleagues. Your work is inspiring and valuable. Thank you for doing it.”
Will G.Data Scientist
“I just came across your website where your books are available for free to read. Thank you so much for making this available to the public. This will help a lot of people.”
Vikas C.AI/ML Engineer
“I found your book Language AI Handbook's explanations of Transformer architectures and production deployment strategies to be exceptionally clear and insightful”
San Z.Researcher
“Your writing feels like a treasure trove of great material. So, thank you for compiling everything on your website!”
Griffin P.Quantitative Researcher
“Really appreciate the way you have explained the concepts. Simple to understand, yet builds up knowledge exponentially, all while reinforcing it with examples repeatedly. I've read the last two chapters `74.Self-attention & 75.Q,K,V` and I've to admit, my concepts have never been clearer. You really are doing an amazing job breaking down maths and complex concepts like a story!”
Muhammad H.AI/ML Engineer
“Thank you Michael. It's top!”
David X.Data Scientist
“I read a few of your blogs on NLP and oh WOW, they really are something, now I exactly know how all of the terms Entropy, Cross-entropy and how it is connected to Perplexity, will definitely read more of your blogs.”
Kanishk K.Reader
“I came across your collection of online handbooks covering quantitative finance, AI, and machine learning. I wanted to reach out and thank you for making such high-quality material openly available. They are incredibly helpful for my CFA Level II review and for brushing up ahead of ML interviews.”
Karim Z.Reader
“Thanks so much for establishing such an amazing community. I appreciate the opportunity to be a learner and a contributor to the community.”
Cyrille L.AI/ML Engineer
“Thanks for putting cool stuff out into the world.”
Charlie B.Applied AI
“I came across one of your books, Language AI Handbook, and man, I'm loving it. I've read many books but have never found such a comprehensive book on large models that covers everything from start to finish with such depth.”
Uon L.Reader
Read all 12 reader reviews
“I would like to thank you for your work. Specifically, the chapter: 'LIME Explainability: Complete Guide to Local Interpretable Model-Agnostic Explanations' was extremely helpful for me in understanding LIME and communicating to colleagues. Your work is inspiring and valuable. Thank you for doing it.”
Will G.Data Scientist
“Really appreciate the way you have explained the concepts. Simple to understand, yet builds up knowledge exponentially, all while reinforcing it with examples repeatedly. I've read the last two chapters `74.Self-attention & 75.Q,K,V` and I've to admit, my concepts have never been clearer. You really are doing an amazing job breaking down maths and complex concepts like a story!”
Muhammad H.AI/ML Engineer
“Thanks so much for establishing such an amazing community. I appreciate the opportunity to be a learner and a contributor to the community.”
Cyrille L.AI/ML Engineer
“I just came across your website where your books are available for free to read. Thank you so much for making this available to the public. This will help a lot of people.”
Vikas C.AI/ML Engineer
“I am enjoying reading your writings. I appreciate the effort you put into it. I want to drop a note of thanks. Thank you!”
Krishnan S.Researcher
“Thank you Michael. It's top!”
David X.Data Scientist
“Thanks for putting cool stuff out into the world.”
Charlie B.Applied AI
“I found your book Language AI Handbook's explanations of Transformer architectures and production deployment strategies to be exceptionally clear and insightful”
San Z.Researcher
“Your writing feels like a treasure trove of great material. So, thank you for compiling everything on your website!”
Griffin P.Quantitative Researcher
“I came across your collection of online handbooks covering quantitative finance, AI, and machine learning. I wanted to reach out and thank you for making such high-quality material openly available. They are incredibly helpful for my CFA Level II review and for brushing up ahead of ML interviews.”
Karim Z.Reader
“I read a few of your blogs on NLP and oh WOW, they really are something, now I exactly know how all of the terms Entropy, Cross-entropy and how it is connected to Perplexity, will definitely read more of your blogs.”
Kanishk K.Reader
“I came across one of your books, Language AI Handbook, and man, I'm loving it. I've read many books but have never found such a comprehensive book on large models that covers everything from start to finish with such depth.”
Uon L.Reader

Exact contents

18 chapters across 3 parts

Every linked chapter is free to read online.

Part VIII: Econometrics and Statistical Modeling

  1. 01
    Time-Series Models for Financial Data

    AR, MA, ARMA, ARIMA models for stationary and non-stationary financial time series.

  2. 02
    Modeling Volatility and GARCH Family

    ARCH, GARCH models for time-varying volatility, volatility clustering, and forecasting.

  3. 03
    Regression Analysis for Financial Relationships

    OLS regression, factor exposures, hypothesis testing, and addressing time-series regression issues.

  4. 04
    Principal Component Analysis and Factor Extraction

    Dimension reduction, identifying principal components in yield curves and equity returns.

  5. 05
    Calibration and Parameter Estimation in Models

    Fitting models to market data, optimization techniques, overfitting concerns, and model stability.

Part IX: Portfolio Theory and Investment Analysis

  1. 01
    Modern Portfolio Theory and Mean-Variance Optimization

    Markowitz framework, efficient frontier, diversification benefits, and portfolio optimization.

  2. 02
    Capital Market Theory - CAPM and the Efficient Frontier

    CAPM equilibrium, market portfolio, Security Market Line, beta, alpha, and model limitations.

  3. 03
    Arbitrage Pricing Theory and Multi-Factor Models

    APT framework, multi-factor models, Fama-French factors, and factor risk premia.

  4. 04
    Portfolio Performance Measurement

    Sharpe ratio, information ratio, maximum drawdown, benchmarking, and risk-adjusted returns.

  5. 05
    Performance Attribution and Investment Alpha

    Brinson attribution, factor-based attribution, separating alpha from beta exposures.

  6. 06
    Advanced Portfolio Construction Techniques

    Black-Litterman model, robust optimization, risk parity, and practical constraints.

Part X: Risk Management and Financial Risk Controls

  1. 01
    Overview of Financial Risks and Regulatory Frameworks

    Market, credit, liquidity, operational, model risk; regulatory standards and risk management objectives.

  2. 02
    Market Risk Measurement - VaR and Beyond

    Value-at-Risk calculation methods, Expected Shortfall, stress testing, and scenario analysis.

  3. 03
    Credit Risk Fundamentals

    Probability of Default, Loss Given Default, Exposure at Default, and expected loss calculations.

  4. 04
    Credit Risk Modeling Approaches

    Structural models (Merton), reduced-form models, credit scoring, and portfolio credit risk.

  5. 05
    Counterparty Risk and CVA

    Counterparty default risk, Credit Valuation Adjustment, exposure profiles, and CVA hedging.

  6. 06
    Liquidity Risk and Other Non-Market Risks

    Market and funding liquidity risk, operational risk, model risk, and qualitative assessments.

  7. 07
    Risk Management Practices and Policies

    Risk limits, hedging strategies, CRO role, risk committees, and firm-wide risk culture.

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Author and edition details

About the author and this edition

Michael Brenndoerfer, author of Quantitative Finance

Michael Brenndoerfer

Michael has spent more than a decade working across software engineering, data, AI, and business. He writes to understand difficult ideas more deeply and to share what he learns in a clear, practical way.

Important: This book is for education and research. It does not provide investment, trading, legal, tax, or other professional advice, and examples are not recommendations.

Edition
First edition
Published
Last reviewed

Version history

Kept current, not frozen in time

Library access includes every future volume and update. When a volume changes, its updated PDF appears in My books at no extra cost.

Current release

Edition 2026.08.1

Editorial improvements.

Earlier releases2
  1. 2026.08.0

    Editorial improvements throughout the book, with clearer explanations, refined presentation, improved plots and visualizations, and refreshed companion code.

  2. 2026.07.0

    Initial complete-book and four-volume PDF release with companion notebooks.