Quant Marathon - Courses

Linear/probabilistic statistics and decision theory for estimation/sequential decisions
The course covers in depth the Mathematical Statistics for Finance topics in the Lab.

In particular, Mathematical Statistics for Finance covers the following topics:

• The “probabilistic” ecosystem: pdf, cdf, cf, marginalization, push-forward, grade, elicitability, copula, conditioning
  
• The “mean-covariance/linear” ecosystem: mean vector, covariance matrix, ellipsoid, affine equivariance, correlation, linear prediction, whitening
  
• Decision theory: state, action, loss, risk, Bayes, frequentist, intervention, observation
  
• Estimation theory and estimation risk assessment: data, estimator, estimative decision problem, predictive decision problem, excess risk, bias-variance, approximation-estimation, model selection
  
• Inference: views, Black-Litterman, Minimum relative entropy

After the course you will obtain a Statement of Completion. Furthermore, you can choose to complete an optional code-based project that will count as one of the two Practical Projects towards the ARPM Certification.

Analytically tractable foundations of advanced AI
The course covers in depth the Linear Mean-Covariance Statistics topics in the Lab.

In particular, Linear Mean-Covariance Statistics covers the following topics:

• Elliptical distributions and notable instances: normal, Student, Cauchy
  
• Linear factor models: regression, principal component analysis, factor analysis
  
• Estimation of the “mean-covariance/linear” ecosystem: historical, maximum likelihood, Bayesian
  
• Random matrix theory, shrinkage and factor selection techniques: Marchenko-Pastur, James-Stein, Ledoit-Wolf, lasso, Ridge

After the course you will obtain a Statement of Completion. Furthermore, you can choose to complete an optional code-based project that will count as one of the two Practical Projects towards the ARPM Certification.

Static, causal, advanced AI modeling
The course covers in depth the Probabilistic Machine Learning topics in the Lab.

In particular, Probabilistic Machine Learning covers the following topics:

• Machine learning models for point and probabilistic prediction, including engineering and enhancements: feature bases, trees, neural networks, gradient boosting, lasso/ridge regularization, random forests, etc.
  
• Supervised learning models, including regression and classification: least-squares, generalized linear models (GLM), Perceptron, support vector machine (SVM), logistic, trees
  
• Unsupervised learning models, including autoencoders and graphical models: k-means clustering, Bayesian and Markov networks
  
• Financial applications of machine learning models for hedging, credit default and clustering

After the course you will obtain a Statement of Completion. Furthermore, you can choose to complete an optional code-based project that will count as one of the two Practical Projects towards the ARPM Certification.

Dynamic, causal, advanced AI modeling
The course covers in depth the Time Series and Sequential Decisions topics in the Lab.

In particular, Time Series and Sequential Decisions covers the following topics:

• Multivariate econometrics, with focus on the most relevant discrete time processes: random walk, vector autoregression, linear state space models, markov chains, GARCH
  
• Continuous time processes: Brownian motion, Poisson, Levy, Ornstein-Uhlenbeck, stochastic volatility
  
• Markov decision processes with partially observable states

After the course you will obtain a Statement of Completion. Furthermore, you can choose to complete an optional code-based project that will count as one of the two Practical Projects towards the ARPM Certification.

Present value and future random payoff of individual instruments
The course covers in depth the Financial Engineering topics in the Lab.

In particular, Financial Engineering covers the following topics:

• Valuation across financial instruments, including linear pricing theory foundations, risk-neutral valuation for derivatives, capital asset pricing framework.
  
• Identification, modeling and forecasting of key risk drivers for the returns of equities, fixed income, derivatives, credit, high frequency, foreign exchange
  
• Repricing techniques: Monte Carlo full repricing, analytical Greeks approximations

After the course you will obtain a Statement of Completion. Furthermore, you can choose to complete an optional code-based project that will count as one of the two Practical Projects towards the ARPM Certification.

Risk measures, performance attribution, (stochastic) stress-testing
The course covers in depth the Portfolio and Enterprise Risk Management topics in the Lab.

In particular, Portfolio and Enterprise Risk Management covers the following topics:

• Aggregation, which is the computation of a portfolio’s value, including enterprise portfolios, and its ex-ante return distribution in both regular and stress markets
  
• Ex-ante evaluation, which is the computation of risk measures such as utility-based measures, VaR, CVaR, and more general spectral measures
  
• Ex-ante attribution, which is the decomposition of the ex-ante return and risk measures into key risk factors

After the course you will obtain a Statement of Completion. Furthermore, you can choose to complete an optional code-based project that will count as one of the two Practical Projects towards the ARPM Certification.

Static and dynamic strategies with optimal trade execution
The course covers in depth the Portfolio Construction and Trading topics in the Lab.

In particular, Portfolio Construction and Trading covers the following topics:

• One-period portfolio construction: total return and relative value mean-variance, fundamental law of active management, tails and non-normality
  
• Dynamic portfolio construction: cross-sectional strategies, time series strategies
  
• Execution: market impact modeling, order scheduling, order placement

After the course you will obtain a Statement of Completion. Furthermore, you can choose to complete an optional code-based project that will count as one of the two Practical Projects towards the ARPM Certification.

Functional analysis, Optimization theory, probability
The course covers in depth the Mathematics topics in the Lab.

The Mathematics Primer covers the foundations which underpin the data science and quantitative finance courses.

• Linear algebra: vector spaces, linear operators, geometry, matrix decomposition, matrix operations
  
• Calculus: differentiation, Taylor expansion, integration, monotone and convex functions
  
• Optimization: smooth programming, convex programming, quadratic regularization, selection problems
  
• Statistics: representations of distributions, conditioning, normal, lognormal, binary mixture distributions

Asset classes fundamentals, performance definitions
The course covers in depth the Finance topics in the Lab.

The Finance Primer covers the fundamental concepts on financial products (value, P&L, fixed income, derivatives) which underpin the quantitative finance courses

• Finance foundations: Value, transaction value, position of financial instruments; cashflows; market microstructure
  
• Peformance foundations for single instrument positions: payoff, holding and trading P&L, return
  
• Asset classes: equity, fixed income, derivatives
  
• Introduction to credit risk

Syntax, data types, structures, functions
The course covers in depth the Python topics in the Lab.

The Python Primer covers the basics of coding in Python

• Basics of Python programming
  
• Control flow for decision making, loops for repetitive tasks and functions for simplifying calculations
  
• Numerical and linear algebraic computations, statistical data analysis, and data visualization
  
• Machine learning methods