What are the quantitative finance books that we should all have in our shelves?

  • Which books/papers should we all have in our shelves?

    There are a couple that I use regularly such as:

    Which ones do you recommend for which topics?

    That question is very tailored: "statistics applied to finance" . I guess my question is broader. Some examples of answers from the question you mention could be potential answers here. But I am also looking for good references in many other areas: asset pricing, stochastic calculus, econometrics, optimization ...

    I definitely see some value in this, maybe the ‘best’ part should be removed and just ask for a list.

    Quantitative finance can refer to many different areas such as quantitative trading, financial derivative pricing, and quantitative risk management. You may need to be more specific.

    Maybe we can follow this model: What data sources are available online?, including categories such as "Derivatives Pricing", "Quantitative Trading", "Quantitative Asset Management", etc.

    I retracted my close vote

    What are the other resources that are similar to Cochrane's asset pricing classes? More precisely, what are the online resources that have online classes, quiz,homework and examination?

  • General Finance Textbooks

    • Options, Futures and Other Derivatives, John Hull
    • The Concepts and Practice of Mathematical Finance, Mark Joshi
    • Paul Wilmott on Quantitative Finance, Paul Wilmott

    Asset Pricing

    • Asset Pricing (Revised Edition), Cochrane, John H. Princeton University Press, 2009.
    • Financial Decisions and Markets: A Course in Asset Pricing, Campbell, John Y. Princeton University Press, 2017.
    • Asset pricing and portfolio choice theory, Back, Kerry. Oxford University Press, 2010.
    • Damodaran on Valuation, Damodaran, Aswath, Wiley Finance, 2006

    Asset Allocation

    • Introduction to Risk Parity and Budgeting, Roncalli, Thierry, 2013
    • Asset Management: A Systematic Approach to Factor Investing, Ang, Andrew, Financial Management Association, 2014
    • Expected Returns: An Investor's Guide to Harvesting Market Rewards, Illmanen, Anti, The Wiley Finance Series, 2011

    Option Pricing Theory and Stochastic Calculus

    • Financial Calculus: An Introduction to Derivative Pricing, Martin Baxter and Andrew Rennie
    • Arbitrage Theory in Continuous Time, Tomas Björk
    • Stochastic Calculus for Finance I: The Binomial Asset Pricing Model, Steven Shreve
    • Stochastic Calculus for Finance II: Continuous-Time Models, Steven Shreve
    • Martingale Methods in Financial Modelling, Marek Musiela and Marek Rutkowski
    • Mathematical Methods for Financial Markets, Monique Jeanblanc, Marc Yor, and Marc Chesney
    • Financial Modelling With Jump Processes, Rama Cont and Peter Tankov
    • Option Volatility and Pricing, Sheldon Natenberg

    Asset Classes

    Equity Derivatives:

    • Equity derivatives, Marcus Overhaus et al.
    • Equity Hybrid Derivatives, Marcus Overhaus et al.
    • The Volatility Surface, Jim Gatheral
    • Stochastic Volatility Modeling, Lorenzo Bergomi
    • Dynamic Hedging: Managing Vanilla and Exotic Options, Nassim Nicholas Taleb
    • Option Volatility & Pricing, Sheldon Natenberg
    • Option Valuation Under Stochastic Volatility: With Mathematica Code, Alan L. Lewis

    FX Derivatives:

    • Foreign Exchange Option Pricing, Iain J. Clark
    • FX Options and Smile Risk, Antonio Castagna
    • FX Options and Structured Products, Uwe Wystup

    Commodity Derivatives:

    • Commodity Option Pricing, Iain J. Clark
    • Commodities and Commodity Derivatives, Helyette Geman
    • Energy and Power Risk Management: New Developments in Modeling, Pricing, and Hedging, Alexander Eydeland, Krzysztof Wolyniec

    Interest Rate Derivatives:

    • Interest Rate Option Models, Rebonato
    • Interest Rate Models – Theory and Practice (with Smile, Inflation and Credit), Damiano Brigo and Fabio Mercurio
    • Interest Rate Modeling I, II & III, Leif B. G. Andersen and Vladimir V. Piterbarg
    • Pricing and Trading Interest Rate Derivatives, J H M Darbyshire

    Inflation Derivatives:

    • Interest Rate Models – Theory and Practice (with Smile, Inflation and Credit), Damiano Brigo and Fabio Mercurio

    Credit Derivatives:

    • Credit Risk - Modeling, Valuation & Hedging, Tomasz R. Bielecki and Marek Rutkowski
    • Modelling Single-name and Multi-name Credit Derivatives, Dominic O’Kane
    • Interest Rate Models – Theory and Practice (with Smile, Inflation and Credit), Damiano Brigo and Fabio Mercurio

    XVA:

    • XVA: Credit, Funding and Capital Valuation Adjustments, Andrew Green
    • Counterparty Credit Risk, Collateral and Funding, Damiano Brigo, Massimo Morini, and Andrea Pallavicini

    Quantitative Risk Management

    • Quantitative Risk Management: Concepts, Techniques and Tools, Alexander J. McNeil, Rudiger Frey, and Paul Embrechts

    Mathematics

    Probability and Stochastic Processes:

    • Probability, A.N. Shiryaev
    • Probability, Leo Breiman
    • Stochastic Calculus and Applications, Samuel N. Cohen and Robert J. Elliott
    • Stochastic Differential Equations, Bernt Oksendal
    • Diffusions Markov Processes and Martingales, L. C. G. Roger and D. Williams

    Statistics:

    • Statistical Inference, George Casella and Roger Berger

    • Theoretical Statistics - Topics for a Core Course, Robert W. Keener

    • Time Series Analysis, James Hamilton

    • The econometrics of financial markets, Campbell, John Y., Andrew Wen-Chuan Lo, and Archie Craig MacKinlay. Vol. 2. Princeton, NJ: Princeton University Press, 1997.

    • The Elements of Statistical Learning, Hastie, Tibshirani and Friedman

    • Handbook of Markov Chain Monte Carlo, Brooks, Steve, Gelman, Andrew, Jones, Galin , and Meng, Xiao-Li.

    • Analysis of Financial Time Series, Ruey S. Tsay

    Machine Learning:

    • Machine Learning: A Probabilistic Perspective, Kevin P Murphy

    • Pattern Recognition and Machine Learning, Christopher Bishop

    • Reinforcement Learning: An introduction, Richard S. Sutton and Andrew G. Barto

    • Advances in Financial Machine Learning, Marcos Lopez de Prado


    Programming

    • C++ Design Patterns and Derivatives Pricing, Mark Joshi
    • Python for Data Analysis, Wes McKinney
    • Applied Computational Economics and Finance, Mario J. Miranda and Paul L. Fackler
    • Modern Computational Finance, Antoine Savine

    Interviews

    • Quant Job Interview Questions and Answers, Mark Joshi
    • Heard on the Street: Quantitative Questions from Wall Street Job Interviews, Timothy Crack
    • 150 Most Frequently Asked Questions on Quant Interviews, Dan Stefanica, Radoš Radoičić, and Tai-ho Wang
    • An Interview primer for quantitative finance, Dirk Bester

    Being a Quant

    • My Life as a Quant: Reflections on Physics and Finance, Emanuel Derman
    • The Quants: How a New Breed of Math Whizzes Conquered Wall Street and Nearly Destroyed It, Scott Patterson
    • A Man for All Markets: From Las Vegas to Wall Street, How I Beat the Dealer and the Market, Edward Thorpe
    • The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution, Gregory Zuckerman

    Cultural Classics

    • Reminiscences of a Stock Operator, Jesse Livermore
    • Liar’s Poker, Michael Lewis
    • Against the Gods, Peter Bernstein

    Many titles are missing here and I must admit I haven't read them all but it's a start, please feel free to edit.

    I added a few more.

    Also added a few more. I might make a more detailed (handbook/paper based edit with seminal papers).

    The papers one should probably be a different question entirely. @phdstudent Can you ask that one too, I can make it CW then.

    1) Some section reorganization for more clarity; 2) some format editing; 3) Added Inflation Derivatives; 4) Included Brigo & Mercurio's also in Inflation and Credit as they provide good overview of topic.

    On another note, personally I am not sure whether probability and statistics books should be included, the sources there are much more diverse IMHO and we could end up with too many references.

    I think the math part should be included, but limited.

    I have added Alan Lewis' book, which I feel is excellent but undervalued, to the list for Equity Derivatives

    for clarity, I added Natenberg (something of an options bible) and Wilmott's texts to the list.

    Hands on Machine Learning for Algorithmic Trading by Stefan Jansen

    A great list, but "The Elements of Statistical Learning" by Hastie, Tibshirani and Friedman should be under machine learning, not statistics, or perhaps under both.

  • For equities specifically:

    Quantitative Equity Portfolio Management: Modern Techniques and Applications, Qian, Hua, Sorensen

    Active Portfolio Management, Grinold and Kahn

  • « Stochastic differential equations » by Oksendal is my best reference on SDE for practionners who want a rigorous statement of all important results in the topic while maintaining a decent size for the book. In addition it comes with solved exercises so this is a must.

  • Simon Benninga (2014) "Financial Modelling" Fourth edition - The MIT Press

    Highly recommended!

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Content dated before 7/24/2021 11:53 AM

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