- Mathematical Finance
- Financial Economics and Asset Management
- Financial Econometrics and Asset Pricing
- Bayesian Statistics and Monte-Carlo Methods
- Financial Markets
- Volatility Theory
- Lévy processes and Jump Diffusion Models
- Numerical Finance
- Interest Rate Modeling Theory
- Empirical Market Microstructure
- High-Frequency Trading
- Reinforcement Learning
- Risk Management
- Machine Learning and Financial Applications
- Github Repositories
- Blogs
- Bonus
- Guide How To Get Into Stochastic Analysis - This Reddit thread is a good start to learn about the basic topics in stochastic analysis and probability theory.
- Analysis, Measure, and Probability: A visual introduction by Marcus Pivato
- Measure Theoretic Probability by P.J.C. Spreij
- Lectures by Claudio Landim
- Convex Optimization - Basics
- Convex Optimization - Advanced
- Convex Optimization – Boyd and Vandenberghe
- Lectures by David Prömel
- Lectures by Chelkak Dmitry
- Lectures by Pasquale Cirillo
- A course by Andrea Agazzi
- Stochastic Calculus An Introduction Through Theory and Exercises by Paolo Baldi
- Introduction To Stochastic Calculus With Applications by Fima C Klebaner
- Brownian Motion, Martingales, and Stochastic Calculus by Jean-François Le Gall
- Stochastic Differential Equations An Introduction with Applications by Bernt K. Oksendal
- A Course on Rough Paths With an Introduction to Regularity Structures by Peter K. Friz Martin Hairer
- Lectures by Andrew L. Allan
- Differential Equations Driven by Rough Paths
- Introduction to Malliavin Calculus by martin hairer
- Introduction to Malliavin Calculus by David Nualart, Eulalia Nualart
- Stochastic Controls Hamiltonian Systems and HJB Equations by Jiongmin Yong, Xun Yu Zhou
- Deterministic and Stochastic Control, Application to Finance
- Introduction to stochastic control of mixed diffusion processes, viscosity solutions and applications in finance and insurance
- Continuous-time stochastic control and optimization with financial applications by Huyen Pham
- Stochastic optimization in continuous time-CUP by Chang F.-R
- Lectures by Neil Walton
- College de France Lectures
- UM6P 4 parts Lectures
- Probabilistic Theory of Mean Field Games with Applications I Mean Field FBSDEs, Control, and Games by René Carmona,François Delarue
- Mean Field Games-Springer by Yves Achdou, Pierre Cardaliaguet, François Delarue, Alessio Porretta, Filippo Santambrogio
- The Master Equation and the Convergence Problem in Mean Field Games
- Lectures by Brittany Hamfeldt
- Model-free Hedging: A Martingale Optimal Transport Viewpoint by Pierre Henry-Labordere
- Lectures by F. Santambrogio
- Marco Cuturi
- Investment Management with Python and Machine Learning Specialization
- A course by Peter Ireland
- Financial Decisions and Markets: A Course in Asset Pricing by John Y. Campbell
- Asset pricing and portfolio choice theory by Kerry E. Back
- Portfolio theory by P.J.C. Spreij
- Quantitative Portfolio Management with Applications in Pierre Brugièere
- Quantitative Financial Economics by Cuthberson, Nitzsche
- Foundations for financial economics
- Financial Econometrics Models and Methods by Oliver Linton
- Asset Pricing Theory by Claus Munk
- Amazing Notes by Paul Soderlind
- Asset Pricing by JOHN H. COCHRANE
- Financial Econometrics by Kevin Sheppard
- Time Series Analysis with R by Kevin Kotzé
- Modelling Financial Times Series by Stephen J. Taylor
- Multivariate Time Series Analysis With R and Financial Applications by Ruey S. Tsay
- Applied time series analysis a practical guide to modeling and forecasting by Mills, Terence C
- Hidden Markov Models for Time Series An Introduction Using R by Langrock, Roland MacDonald, Iain L. Zucchini, W
- Nonlinear time series analysis by Chen, Rong Tsay, Ruey S
- Garch Models Structure, Statistical Inference and Financial Applications by Christian Francq, Jean-Michel Zakoian
- Essentials of Time Series for Financial Applications by Massimo Guidolin, Manuela Pedio
- The elements of statistical learning by hastie tibshirani and friedman
- An introduction to statistical learning with applications in R: by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani
- Statistical Foundations Of Data Science by Jianqing Fan, Runze Li, Cun-Hui Zhang, Hui Zou
- High-Dimensional Probability by Roman Vershynin
- High-dimensional statistics a non-asymptotic viewpoint by Wainwright, Martin J
- Asymptotic Statistics by A. W. van der Vaart
- Statistical Learning with Sparsity The Lasso and Generalizations by Trevor Hastie, Robert Tibshirani, Martin Wainwright
- Introduction to High-Dimensional Statistics by Christophe Giraud
- Probabilistic Machine Learning - Kevin Patrick Murphy
- Dive into deep learning
- Deep Learning Do It Yourself!
- Alfredo Canziani and Yann LeCun’s Deep Learning Course at CDS
- Python Programming for Economics and Finance
- Quantitative Economics with Python
- Advanced Quantitative Economics with Python
- Financial econometrics
- Portfolio theory
- Portfolio optimization
- Elementary stochastic process
- Time series with R
- Time series analysis
- Data science
- Financial Time Series
- The Bayesian Choice by Christian P. Robert
- Courses and Lectures with code by Mattias Villani
- Bayesian Essentials with R by Jean-Michel Marin, Christian P. Robert
- Monte Carlo Methods in Financial Engineering Paul Glasserman
- Monte Carlo Methods and Stochastic Algorithms by BERNARD LAPEYRE
- Monte Carlo methods by François Portier
- Bootstrap and resampling methodsby François Portier
Hidden Markov models (HMM)
- Stochastic Finance - Open the PDF file with Adobe Acrobat Reader
- Stochastic Calculus for Finance I The Binomial Asset Pricing Model (Springer Finance) by Steven E. Shreve
- Solutions by Yan Zeng
- Stochastic Calculus for Finance II Continuous-Time Models (Springer Finance) (v. 2) by Steven E. Shreve
- Solutions by Yan Zeng
- Financial Markets in Continuous Time by Monique Jeanblanc and Rose-Anne Dana
- Arbitrage Theory in Continuous Time by Tomas Bjork
- Mathematics of Financial Markets by Robert J. Elliott, P. Ekkehard Kopp
- Introduction to Stochastic Calculus Applied to Finance by Damien Lamberton Bernard Lapeyre
- Financial Statistics and Mathematical Finance Methods, Models and Applications by Ansgar Steland
- The Volatility Surface A Practitioners Guide (Wiley Finance) by Jim Gatheral, Nassim Nicholas Taleb
- Baruch MFE 2019 Fall - R Notebooks
- Stochastic Volatility Modeling by Bergomi, Lorenzo
- Local/Stochastic Volatility and Applications with R - Open the PDF file (Stochastic Finance) with Adobe Acrobat Reader
- Rough volatility : An overview by Jim Gatheral
- Rough Volatility Literature
- Rough Volatility Lecture 1 by Jim Gatheral
- Rough Volatility Lecture 2 by Jim Gatheral
- Rough Volatility Lecture 3 by Jim Gatheral
- Rough Volatility Lecture 4 by Jim Gatheral
- Rough Volatility Lecture 5 by Jim Gatheral
- Financial Modelling with Jump Processes by Peter Tankov
- Applied Stochastic Control of Jump Diffusions
- Lévy Processes and Stochastic Calculus
- Notes by Alexandre Popier
- Malliavin calculus for Levy processes with applications to finance by Giulia Nunno, Bernt Øksendal, Frank Proske
- Computational Methods for Quantitative Finance Finite Element Methods for Derivative Pricing by Norbert Hilber, Oleg Reichmann, Christoph Schwab, Christoph Winter
- Tools for Computational Finance by Rudiger U. Seydel
- Financial Modeling A Backward Stochastic Differential Equations Perspective
- Optimization Methods in Finance by Gerard Cornuéjols Javier Peña Reha Tütüncü
- Numerical Methods and Optimization in Finance by Manfred Gilli Dietmar Maringer Enrico Schumann
- Implementing models in quantitative finance methods and cases by Gianluca Fusai, Andrea Roncoroni
- Numerical Solution of Stochastic Differential Equations with Jumps in Finance
- An Introduction to Computational Stochastic PDEs by Gabriel J. Lord, Catherine E. Powell, Tony Shardlow
- Numerical probability an introduction with applications to finance by Pagès, Gilles
- Computational Finance by Christian Bayer
- An Introduction to Computational Finance Without Agonizing Pain by Peter Forsyth
- Mathematical Modeling and Computation in Finance With Exercises and Python and MATLAB Computer Codes by Cornelis W. Oosterlee, Lech A. Grzelak
- Computational Finance Course
- Financial Engineering Course
- Website
- Derivatives Analytics with Python Data Analysis, Models, Simulation, Calibration and Hedging by Yves Hilpisch
- Computational Methods for Option Pricing by Yves Achdou, Olivier Pironneau
- C++ For Quantitative Finance by Halls-Moore
- C++ For Financial Mathematics by John Armstrong
- C++ Design Patterns and Derivatives Pricing by Joshi Mark
- Modern Computational Finance AAD and Parallel Simulations by Antoine Savine
- Modern Computational Finance Scripting for Derivatives and xVA by Antoine Savine, Jesper Andreasen
- Introduction to C++ for Financial Engineers
- Financial instrument pricing using C++
- C# for Financial Markets by Daniel J. Duffy, Andrea Germani
- Monte Carlo Frameworks Building Customisable High-performance C++ Applications by Daniel J. Duffy, Joerg Kienitz
- Interest Rate Models - Theory and Practice With Smile, Inflation and Credit by Damiano Brigo, Fabio Mercurio - THE BIBLE
- An Elementary Introduction To Stochastic Interest Rate Modeling by Nicolas Privault
- Term-Structure Models A Graduate Course by Damir Filipovic
- Interest Rate Swaps and Their Derivatives A Practitioners Guide by Amir Sadr
- Financial Markets Microstructure by Egor Starkov and Lectures
- Seminar
- MTH 9879 Market Microstructure Models Notes and R notebooks
- Securities Trading Principles and Procedures by Joel Hasbrouck
Electronic markets and limit order book. High frequency data. Statistical and structural models (Roll and its generalizations). Asymmetric information models (Glosten-Milgrom, Kyle). Information share. Inventory management models. Market making. Statistical limit order book models. Trading models: Market impact and order flow. Trading costs. Optimal execution. High Frequency Trading. High Frequency Econometrics: Realized volatility and covariance, Microstructure noise. Point processes in finance (Hawkes processes and ACD models).
- Empirical Market Microstructure The Institutions, Economics, and Econometrics of Securities Trading by Joel Hasbrouck
- Market Liquidity Theory, Evidence, and Policy by Ailsa Röell, Marco Pagano, and Thierry Foucault
- Market Microstructure in Practice by Charles-Albert Lehalle, Sophie Laruelle, Charles-Albert Lehalle, Sophie Laruelle
- Trades, Quotes and Prices Financial Markets Under the Microscope
Basic elements of graph theory. Random walks on graphs. Centrality measures. Scale free networks and small world graphs. Models of random graphs: Erdos Renyi graphs, Exponential random graphs, Stochastic block model, configuration model. Maximum entropy principle and networks. Networks from time series.
- Networks by Mark Newman
- Statistical Analysis Of Network Data With R by Eric D. Kolaczyk, Gábor Csárdi
Mechanisms for systemic risk and models: Bank runs, leverage cycles, Interbank networks, Fire sales spillovers. Econometric approaches to systemic risk: CoVar, MES,SRISK, Granger causality networks. High frequency systemic risk: flash crashes, liquidity crises, systemic cojumps.
- Algorithmic and High-Frequency Trading by Álvaro Cartea, Sebastian Jaimungal, José Penalva
- Algorithmic Trading Slides and Python Notebooks
- Introduction to Reinforcement Learning by Richard Sutton and Andrew Barto
- REINFORCEMENT LEARNING AND OPTIMAL CONTROL - Book and Lectures
- Foundations of Reinforcement Learning with Applications in Finance
- Handbook of Financial Risk Management
- Risk Management & Financial Regulation
- Advanced Asset Management
- Introduction to Risk Parity and Budgeting
- Loss Models: From Data to Decisions, 5th Edition
- Statistics and Data Analysis for Financial Engineering with R examples by David Ruppert, David S. Matteson
- Analyzing Dependent Data with Vine Copulas A Practical Guide With R by Claudia Czado
- Copula Methods in Finance by Umberto Cherubini, Elisa Luciano, Walter Vecchiato
- Elements of Copula Modeling with R by Marius Hofert, Ivan Kojadinovic, Martin Machler, Jun Yan
Polynomial curve fitting, foundations of statistical learning, no free lunch theorem, local volatility, interpolation of volatility surfaces, universal approximation, approximation by deep neural networks, empirical risk minimization, ridge regression, nonlinear regression, convex optimization, gradient descent, stochastic gradient descent, non-convex optimization, calibration of financial models, machine learning techniques for option pricing, deep model calibration.
BSDE approach to option pricing, deep solvers for BSDEs, Euler-Maruyama discretization of forward SDEs, existence and uniqueness of backward SDEs, linear BSDEs, applications in option pricing, comparison principles, Euler-Maruyama discretization of backward SDEs, classical solutions of semilinear PDEs, convergence rates of deep solvers for backward SDEs, scope and limitations.
Discrete time optimal stopping, Snell envelope, optimal stopping times, American put option, martingale duality, parametric approximation methods, regression based approximation methods, Longstaff-Schwartz algorithm, martingales from stopping rules, deep optimal stopping, low rank tensors, signatures and rough paths, optimal stopping with signatures.
Optimal liquidation problems, Markov decision processes, dynamic programming, Bellman equation, tabular methods, Q-learning, Monte Carlo methods, temporal difference methods, optimal liquidation revisited, optimal investment, deep Q-learning.
Papers with Code (https://paperswithcode.com/)
- A curated list of insanely awesome libraries, packages and resources for Quants
- Awesome lists about all kinds of interesting topics
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