퀀트 혹은 퀀트가 되고자 하는 사람을 위한 목록

1.
아주 오래전 금융공학에 관심을 가졌을 때 찾았던 곳이 삼성경제연구소가 운영하는 SERI에 둥지를 틀었던 ‘금융공학포럼’입니다. 기억을 더듬어 보면 ‘금융공학 Best’로 좋은 자료들을 정리하여 도움을 주고자 하였습니다. 그렇지만 체계적인 수준은 아니었습니다.

아래에서 소개하는 Qunatnet과 Quantstart의 목록은 각 분야별로 괜찮은 자료를 정리해서 보여줍니다. 국내도 이런 류가 나왔으면 좋겠네요.

먼저 유명한 Quantnet이 정리한 목록입니다. Master reading list for Quants, MFE (Financial Engineering) students에 올라온 내용입니다. Attachment는 위를 방문하면 받을 수 있습니다.

FREE QUANT CAREERGUIDES

• What do quant do? A guide by Mark Joshi. Download
• Paul & Dominic’s Guide to Quant Careers (see attachment)
• Career in Financial Markets 2011- a guide by efinancialcareers. Download
• Interview Preparation Guide by Michael Page: Quantitative Analysis. Download
• Interview Preparation Guide by Michael Page: Quantitative Structuring. Download
• Paul & Dominic’s Job Hunting in Interesting Times Second Edition (see attachment)
• Peter Carr’s A Practitioner’s Guide to Mathematical Finance (see attachment)
• Max Dama’s Guide to Automated Trading (see attachment)

CAREER AS A QUANT

• The Complete Guide to Capital Markets for Quantitative Professionals
• Financial Engineering: The Evolution of a Profession
• My Life as a Quant: Reflections on Physics and Finance
• The Quants: How a New Breed of Math Whizzes Conquered Wall Street and Nearly Destroyed It
• How I Became a Quant: Insights from 25 of Wall Street’s Elite
• The Big Short: Inside the Doomsday Machine
• Nerds on Wall Street: Math, Machines and Wired Markets
• Physicists on Wall Street and Other Essays on Science and Society

BOOKS FOR QUANT INTERVIEWS

• Quant Job Interview Questions And Answers by Mark Joshi
• Frequently Asked Questions in Quantitative Finance by Wilmott
• Heard on The Street: Quantitative Questions from Wall Street Job Interviews by Timothy Crack
• Cracking the Coding Interview: 150 Programming Questions and Solutions by Gayle Laakmann McDowell
• A Practical Guide To Quantitative Finance Interviews by Xinfeng Zhou
• Basic Black-Scholes: Option Pricing and Trading by Timothy Crack
• Fifty Challenging Problems in Probability with Solutions by Frederick Mosteller
• Vault Guide to Advanced Finance & Quantitative Interviews

GOOD BOOKS TO READ BEFORE STARTING MFE PROGRAM

• A Primer For The Mathematics Of Financial Engineering, Second Edition
• Paul Wilmott on Quantitative Finance 3 Volume Set (2nd Edition)
• An Introduction to the Mathematics of Financial Derivatives, Second Edition by Salih Neftci
• Options, Futures, and Other Derivatives (8th Edition) by John Hull
• Principles of Financial Engineering, Second Edition by Salih Neftci
• Elementary Stochastic Calculus With Finance in View by Thomas Mikosch
• The Concepts and Practice of Mathematical Finance by Mark Joshi
• Financial Options: From Theory to Practice by Stephen Figlewski
• Financial Calculus : An Introduction to Derivative Pricing by Martin Baxter
• A Course in Financial Calculus by Etheridge Alison
• The Mathematics of Financial Derivatives: A Student Introduction by Paul Wilmott
• Frequently Asked Questions in Quantitative Finance by Paul Wilmott
• Derivatives Markets by Robert L. McDonald
• An Undergraduate Introduction to Financial Mathematics by Robert Buchanan

GENERAL READING ON WALL STREET

• Liar’s Poker: Rising Through the Wreckage on Wall Street
• Monkey Business: Swinging Through the Wall Street Jungle
• Reminiscences of a Stock Operator
• Working the Street: What You Need to Know About Life on Wall Street
• Fiasco: The Inside Story of a Wall Street Trader
• Den of Thieves
• When Genius Failed: The Rise and Fall of Long-Term Capital Management
• Traders, Guns & Money: Knowns and unknowns in the dazzling world of derivatives
• The Greatest Trade Ever: The Behind-the-Scenes Story of How John Paulson Defied Wall Street and Made Financial History
• Goldman Sachs : The Culture of Success
• The House of Morgan: An American Banking Dynasty and the Rise of Modern Finance
• Wall Street: A History: From Its Beginnings to the Fall of Enron
• The Murder of Lehman Brothers: An Insider’s Look at the Global Meltdown
• On the Brink: Inside the Race to Stop the Collapse of the Global Financial System
• House of Cards: A Tale of Hubris and Wretched Excess on Wall Street
• Too Big to Fail: The Inside Story of How Wall Street and Washington Fought to Save the Financial System-and Themselves
• Liquidated: An Ethnography of Wall Street
• Fortune’s Formula: The Untold Story of the Scientific Betting System That Beat the Casinos and Wall Street

PROGRAMMING
C++ (ordered by level of difficulty)

• Problem Solving with C++ (8th Edition) by Walter Savitch
• C++ How to Program (8th Edition) by Harvey Deitel
• Absolute C++ (5th Edition) by Walter Savitch
• Thinking in C++: Introduction to Standard C++, Volume One by Bruce Eckel
• Thinking in C++: Practical Programming, Volume Two by Bruce Eckel
• The C++ Programming Language: Special Edition by Bjarne Stroustrup (C++ inventor)
• Effective C++: 55 Specific Ways to Improve Your Programs and Designs by Scot Myers
• C++ Primer (4th Edition) by Stanley Lippman
• C++ Design Patterns and Derivatives Pricing (2nd edition) by Mark Joshi
• Financial Instrument Pricing Using C++ by Daniel Duffy

C# (ordered by level of difficulty)

• C# 2010 for Programmers (4th Edition)
• Computational Finance Using C and C# by George Levy
• C# in Depth, Second Edition by Jon Skeet

F# (ordered by level of difficulty)

• Programming F#: An introduction to functional language by Chris Smith
• F# for Scientists by Jon Harrops (Microsoft Researcher)
• Real World Functional Programming: With Examples in F# and C#
• Expert F# 2.0 by Don Syme
• Beginning F# by Robert Pickering

Matlab (ordered by level of difficulty)

• Matlab: A Practical Introduction to Programming and Problem Solving
• Numerical Methods in Finance and Economics: A MATLAB-Based Introduction (Statistics in Practice)

Excel

• Excel 2007 Power Programming with VBA by John Walkenbach
• Excel 2007 VBA Programmer’s Reference
• Financial Modeling by Simon Benninga
• Excel Hacks: Tips & Tools for Streamlining Your Spreadsheets
• Excel 2007 Formulas by John Walkenbach

VBA

• Advanced modelling in finance using Excel and VBA by Mike Staunton
• Implementing Models of Financial Derivatives: Object Oriented Applications with VBA

Python

• Learning Python: Powerful Object-Oriented Programming
• Python Cookbook

FINITE DIFFERENCES

• Option Pricing: Mathematical Models and Computation, by P. Wilmott, J.N. Dewynne, S.D. Howison
• Pricing Financial Instruments: The Finite Difference Method, by Domingo Tavella, Curt Randall
• Finite Difference Methods in Financial Engineering: A Partial Differential Equation Approach by Daniel Duffy

MONTE CARLO

• Monte Carlo Methods in Finance, by Peter Jäcke (errata available at jaeckel.org)
• Monte Carlo Methodologies and Applications for Pricing and Risk Management , by Bruno Dupire (Editor)
• Monte Carlo Methods in Financial Engineering, by Paul Glasserman
• Monte Carlo Frameworks in C++: Building Customisable and High-performance Applications by Daniel J. Duffy and Joerg Kienitz

STOCHASTIC CALCULUS

• Stochastic Calculus and Finance by Steven Shreve (errata attached)
• Stochastic Differential Equations: An Introduction with Applications by Bernt Oksendal

VOLATILITY

• Volatility and Correlation, by Riccardo Rebonato
• Volatility, by Robert Jarrow (Editor)
• Volatility Trading by Euan Sinclair

INTEREST RATE

• Interest Rate Models – Theory and Practice, by D. Brigo, F. Mercurio updates available on-line Professional Area of Damiano Brigo’s web site
• Modern Pricing of Interest Rate Derivatives, by Riccardo Rebonato
• Interest-Rate Option Models, by Riccardo Rebonato
• Efficient Methods for Valuing Interest Rate Derivatives, by Antoon Pelsser
• Interest Rate Modelling, by Nick Webber, Jessica James

FX

• Foreign Exchange Risk, by Jurgen Hakala, Uwe Wystup
• Mathematical Methods For Foreign Exchange, by Alexander Lipton

STRUCTURED FINANCE

• The Analysis of Structured Securities: Precise Risk Measurement and Capital Allocation (Hardcover) by Sylvain Raynes and Ann Rutledge
• Salomon Smith Barney Guide to MBS & ABS, Lakhbir Hayre, Editor
• Securitization Markets Handbook, Structures and Dynamics of Mortgage- and Asset-backed securities by Stone & Zissu
• Securitization, by Vinod Kothari
• Modeling Structured Finance Cash Flows with Microsoft Excel: A Step-by-Step Guide (good for understanding the basics)
• Structured Finance Modeling with Object-Oriented VBA (a bit more detailed and advanced than the step by step book)

STRUCTURED CREDIT

• Collateralized Debt Obligations, by Arturo Cifuentes
• An Introduction to Credit Risk Modeling by Bluhm, Overbeck and Wagner (really good read, especially on how to model correlated default events & times)
• Credit Derivatives Pricing Models: Model, Pricing and Implementation by Philipp J. Schönbucher
• Credit Derivatives: A Guide to Instruments and Applications by Janet M. Tavakoli
• Structured Credit Portfolio Analysis, Baskets and CDOs by Christian Bluhm and Ludger Overbeck

RISK MANAGEMENT/VAR

• VAR Understanding and Applying Value at Risk, by various authors
• Value at Risk, by Philippe Jorion
• RiskMetrics Technical Document RiskMetrics Group
• Risk and Asset Allocation by Attilio Meucci

SAS/S/S-PLUS

• The Little SAS Book: A Primer, Fourth Edition by Lora D. Delwiche and Susan J. Slaughter
• Modeling Financial Time Series with S-PLUS
• Statistical Analysis of Financial Data in S-PLUS
• Modern Applied Statistics with S

HANDS ON

• Implementing Derivative Models, by Les Clewlow, Chris Strickland
• The Complete Guide to Option Pricing Formulas, by Espen Gaarder Haug

NOT ENOUGH YET?

• Energy Derivatives: Pricing and Risk Management, by Les Clewlow, Chris Strickland
• Hull-White on Derivatives, by John Hull, Alan White 1899332456
• Exotic Options: The State of the Art, by Les Clewlow (Editor), Chris Strickland (Editor)
• Market Models, by C.O. Alexander
• Pricing, Hedging, and Trading Exotic Options, by Israel Nelken
• Modelling Fixed Income Securities and Interest Rate Options, by Robert A. Jarrow
• Black-Scholes and Beyond, by Neil A. Chriss
• Risk Management and Analysis: Measuring and Modelling Financial Risk, by Carol Alexander
• Mastering Risk: Volume 2 – Applications: Your Single-Source Guide to Becoming a Master of Risk, by Carol Alexander

2.
다음은 Quantnet이 만든 ‘2013-2014 QuantNet International Guide to Financial Engineering Programs’입니다. Big Data in Finance가 관심을 끌었습니다.

3.
마지막은 Quantstart의 목록입니다. QUANTITATIVE FINANCE ARTICLES의 목록입니다. Quantnet과 같은 듯 다릅니다.

Careers Advice

• Junior Quant Jobs Beginning a career in Financial Engineering after a PhD
• Understanding How to Become a Quantitative Analyst
• What are the Different Types of Quantitative Analysts?
• What Classes Should You Take To Become a Quantitative Analyst?
• Why Study for a Mathematical Finance PhD?
• My Experiences as a Quantitative Developer in a Hedge Fund
• Which Programming Language Should You Learn To Get A Quant Developer Job?
• Can You Still Become a Quant in Your Thirties?
• Self-Study Plan for Becoming a Quantitative Developer
• Self-Study Plan for Becoming a Quantitative Analyst
• Getting a Job in a Top Tier Quant Hedge Fund

Quant Reading Lists

• Quant Reading List Derivative Pricing
• Quant Reading List C++ Programming
• Quant Reading List Numerical Methods
• Quant Reading List Python Programming
• 5 Important But Not So Common Books A Quant Should Read Before Applying for a Job
• 5 Top Books for Acing a Quantitative Analyst Interview
• Top 5 Finite Difference Methods books for Quant Analysts
• Top 5 Essential Beginner C++ Books for Financial Engineers
• Quantitative Finance Reading List
• Top 10 Essential Resources for Learning Financial Econometrics
• Free Quantitative Finance Resources

Algorithmic Trading

• Beginner’s Guide to Quantitative Trading
• How to Identify Algorithmic Trading Strategies
• Successful Backtesting of Algorithmic Trading Strategies – Part I
• Can Algorithmic Traders Still Succeed at the Retail Level?
• Successful Backtesting of Algorithmic Trading Strategies – Part II
• Securities Master Databases for Algorithmic Trading
• Securities Master Database with MySQL and Python
• Sharpe Ratio for Algorithmic Trading Performance Measurement
• Top 5 Essential Beginner Books for Algorithmic Trading
• Interactive Brokers Demo Account Signup Tutorial
• Best Programming Language for Algorithmic Trading Systems?
• Installing a Desktop Algorithmic Trading Research Environment using Ubuntu Linux and Python
• Basics of Statistical Mean Reversion Testing

The Binomial Model

• Introduction to Option Pricing with Binomial Trees
• Hedging the sale of a Call Option with a Two-State Tree
• Risk Neutral Pricing of a Call Option with a Two-State Tree
• Replication Pricing of a Call Option with a One-Step Binomial Tree
• Multinomial Trees and Incomplete Markets
• Pricing a Call Option with Two Time-Step Binomial Trees
• Pricing a Call Option with Multi-Step Binomial Trees
• Derivative Pricing with a Normal Model via a Multi-Step Binomial Tree
• Risk Neutral Pricing of a Call Option with Binomial Trees with Non-Zero Interest Rates

Stochastic Calculus

• Introduction to Stochastic Calculus
• The Markov and Martingale Properties
• Brownian Motion and the Wiener Process
• Stochastic Differential Equations
• Geometric Brownian Motion
• Ito’s Lemma
• Deriving the Black-Scholes Equation

Numerical PDE

• Derivative Approximation via Finite Difference Methods
• Solving the Diffusion Equation Explicitly
• Crank-Nicholson Implicit Scheme
• Tridiagonal Matrix Solver via Thomas Algorithm

C++ Implementation

• C++ Virtual Destructors: How to Avoid Memory Leaks
• Passing By Reference To Const in C++
• Mathematical Constants in C++
• STL Containers and Auto_ptrs – Why They Don’t Mix
• European vanilla option pricing with C++ and analytic formulae
• European vanilla option pricing with C++ via Monte Carlo methods
• Digital option pricing with C++ via Monte Carlo methods
• Double digital option pricing with C++ via Monte Carlo methods
• Tridiagonal Matrix Algorithm (“Thomas Algorithm”) in C++
• Matrix Classes in C++ – The Header File
• Matrix Classes in C++ – The Source File
• C++ Standard Template Library Part I – Containers
• Asian option pricing with C++ via Monte Carlo Methods
• Floating Strike Lookback Option Pricing with C++ via Analytic Formulae
• Statistical Distributions in C++
• Function Objects (“Functors”) in C++ – Part 1
• Random Number Generation via Linear Congruential Generators in C++
• C++ Explicit Euler Finite Difference Method for Black Scholes
• C++ Standard Template Library Part II – Iterators
• Implied Volatility in C++ using Template Functions and Interval Bisection
• Generating Correlated Asset Paths in C++ via Monte Carlo
• C++ Standard Template Library Part III – Algorithms
• What’s New in the C++11 Standard Template Library?
• Eigen Library for Matrix Algebra in C++
• Implied Volatility in C++ using Template Functions and Newton-Raphson
• Heston Stochastic Volatility Model with Euler Discretisation in C++
• Jump-Diffusion Models for European Options Pricing in C++
• Calculating the Greeks with Finite Difference and Monte Carlo Methods in C++

Python Implementation

• Options Pricing in Python
• European Vanilla Call-Put Option Pricing with Python
• LU Decomposition in Python and NumPy
• Cholesky Decomposition in Python and NumPy
• QR Decomposition with Python and NumPy
• Jacobi Method in Python and NumPy

Book Reviews

• Paul Wilmott Introduces Quantitative Finance – Paul Wilmott
• The Concepts and Practice of Mathematical Finance – Mark S. Joshi
• Financial Instrument Pricing Using C++ – Daniel J. Duffy
• Introduction to C++ for Financial Engineers: An Object-Oriented Approach – Daniel J. Duffy
• Effective C++: 55 Specific Ways to Improve Your Programs and Designs – Scott Meyers
• Heard on The Street: Quantitative Questions from Wall Street Job Interviews – Timothy Falcon Crack
• Frequently Asked Questions in Quantitative Finance – Paul Wilmott
• Quant Job Interview Questions And Answers – Mark S. Joshi, Nick Denson, Andrew Downes
• A Practical Guide To Quantitative Finance Interviews – Xinfeng Zhou
• Starting Your Career as a Wall Street Quant: A Practical, No-BS Guide to Getting a Job in Quantitative Finance – Brett Jiu
• Learning Python: Powerful Object-Oriented Programming – Mark Lutz
• C++ Design Patterns and Derivatives Pricing – Mark S. Joshi

4.
오래전에 Algorithmic Finance라는 온라인 학술서를 소개하였습니다.이후 세번 논문집을 내놓았습니다. 가끔 찾아보시길 바랍니다.

Modeling market impact and timing risk in volume time
Discovering the ecosystem of an electronic financial market with a dynamic machine-learning method
A multiscale model of high-frequency trading