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Quantitative Finance Awesome

A list of ressources for all topics related to quantitative finance!

Contents

Mathematical Finance

Measure theory

Optimization

Markov Chains and Stochastic Processes

Stochastic Calculus

Rough Path Theory

Malliavin Calculus

Fourier Analysis

Optimal and Stochastic Control

Stochastic Filtering

Mean Field Games

Optimal Transport

Financial Economics and Asset Management

Portfolio Theory

Financial Econometrics and Asset Pricing

Econometrics

Financial Econometrics

Time Series

Statistical Learning

Machine Learning

Python

R

Julia

Bayesian Statistics and Monte-Carlo Methods

Bayesian Statistics

Monte-Carlo Methods

Advanced Topics

Markov chain Monte Carlo (MCMC)

Bayesian Non-parametric Statistics

Hidden Markov models (HMM)

Extreme Value Theory

Kalman and Bayesian Filters

Financial Markets

Christoph Belak

Nicolas Privault

Genevieve Gauthier

Ansgar Jungel

Peter Tankov and Nizar Touzi

Martin Haugh

Damien Lmaberton

Book Recommendations

Volatility Theory

Lévy processes and Jump Diffusion Models

Numerical Finance

Fundamentals

Books

Courses

R

Python

Lech A. Grzelak

YVES J HILPISCH

C++/C#

Antoine Savine

Fabrice D. Rouah

Daniel J. Duffy

Java

Christian Fries

Interest Rate Modeling Theory

Empirical Market Microstructure

Courses

Market Microstructure

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).

Financial networks

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.

Systemic risk

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.

High-Frequency Trading

Sebastian Jaimungal

Reinforcement Learning

Books

Courses

Deep Reinforcement Learning

Risk Management

Thierry Roncalli

Books

Topics

Copulas Theory

Machine Learning and Financial Applications

Statistical learning, neural networks, and deep model calibration

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.

Backward SDEs and deep solvers for PDEs

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.

Optimal stopping and American options

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.

Markov decision processes and reinforcement learning

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.

Courses

Papers with Code (https://paperswithcode.com/)

Github Repositories

Blogs

Bonus

Contributing

Contributions of any kind welcome, just follow the guidelines!

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Study resources for quantitative finance

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