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Intensive overview training on data science for finance, quantitative risk modeling and portfolio construction

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In-depth, master-level, 4-course program in modern quantitative finance with emphasis on data science

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Short math/coding courses to prepare for the Quant Bootcamp or the Quant Marathon

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Overview

Video lectures

Theory

Case studies

C.I. The 10-Step Checklist

[ C.1 ] Summary

[ C.2 ] Historical Checklist

[ C.2.1 ] Risk drivers identification

[ C.2.2 ] Quest for invariance

[ C.2.3 ] Estimation

[ C.2.4 ] Projection

[ C.2.5 ] Pricing at the horizon

[ C.2.6 ] Aggregation

[ C.2.7 ] Ex-ante evaluation

[ C.2.8 ] Ex-ante attribution

[ C.2.8.1 ] Ex-ante attribution: performance

[ C.2.8.2 ] Ex-ante attribution: risk

[ C.2.9 ] Construction

[ C.2.10 ] Execution

[ C.3 ] Monte Carlo Checklist

[ C.3.1 ] Risk drivers identification

[ C.3.1.1 ] Market

[ C.3.1.2 ] Credit

[ C.3.2 ] Quest for invariance

[ C.3.5 ] Pricing at the horizon

[ C.3.6 ] Aggregation

[ C.3.7 ] Ex-ante evaluation

[ C.3.8 ] Ex-ante attribution

[ C.3.8.1 ] Ex-ante attribution: performance

[ C.3.8.2 ] Ex-ante attribution: risk

[ C.3.9 ] Construction

[ C.3.10 ] Execution

C.II. Factor models and learning

[ C.4 ] Summary

[ C.5 ] Principal component analysis of the yield curve

[ C.5.1 ] Cross-sectional structure of the yield curve covariance

[ C.5.2 ] Finite set of times to maturity

[ C.5.3 ] The continuum limit

[ C.6 ] Machine learning for hedging

[ C.6.1 ] Least squares regression

[ C.6.1.1 ] Theoretical optimum

[ C.6.1.2 ] Linear least squares regression

[ C.6.1.3 ] Least squares regression tree

[ C.6.2 ] Least absolute distance regression

[ C.6.2.1 ] Theoretical optimum

[ C.6.2.2 ] Linear least absolute distance regression

[ C.6.2.3 ] Least absolute distance regression tree

[ C.7 ] Regression in the stock market

[ C.7.1 ] Time series models

[ C.7.2 ] Maximum likelihood

[ C.7.2.1 ] The model

[ C.7.2.2 ] Normal assumption

[ C.7.2.3 ] Student t assumption

[ C.7.3 ] Factor selection for regression

[ C.7.3.1 ] Stepwise regression selection

[ C.7.3.2 ] Lasso regression

[ C.7.3.3 ] Ridge regression

[ C.7.4 ] Bayesian

[ C.7.4.1 ] Normal conditional likelihood

[ C.7.4.2 ] Normal-inverse-Wishart prior distribution

[ C.7.4.3 ] Normal-inverse-Wishart posterior distribution

[ C.7.4.4 ] Student t predictive distribution

[ C.7.4.5 ] Classical equivalent

[ C.7.4.6 ] Uncertainty

[ C.7.5 ] Mixed approach

[ C.8 ] Credit default classification

[ C.8.1 ] Background

[ C.8.2 ] Fit and assessment

[ C.8.3 ] Logistic regression

[ C.8.4 ] Interactions

[ C.8.5 ] Encoding

[ C.8.6 ] Regularization

[ C.8.7 ] Trees

[ C.8.8 ] Gradient boosting

[ C.8.9 ] Cross-validation

[ C.9 ] Clustering for the stock market

[ C.9.1 ] k-means clustering

[ C.9.2 ] Shrinkage

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