Series
Time-series
A series of posts related to the analysis of time-series.
- introduce the 4 statistical moments
- develop concepts on auto-correlation, stationarity and random-walk
- time-series decomposition
- ARIMA and families
Probability
A series of posts centered on basic probability concepts essentials for the study of quantitative finance.
Risk and Portfolio
A series of posts related to risk-management in portfolio construction.
Quant - Part 1
A series of posts when starting quant finance. Basic mathematical concepts and related code in R and Python when starting quantitative finance.
Quant - Part 2
A series of posts on some introductory concepts of stochastic calculus.
Quant - Part 3
A series of posts on the Black-Schole Equation and derivative pricing.
Machine Learning - Part 1
A series of posts on machine learning algorithms with a quant finance lens.
- KNN
- linear regression
- Kmeans
Machine Learning - Part 2
A series of posts on machine learning algorithms that focuses on trees, bagging and boosting.
Posts
Linear Regression
A dive into the math behind the linear regression algorithm.
KNN - K Nearest Neighbor
Using KNN in both python and R
05 - Arima
Introducing Arima - Autoregressive Integrated Moving Average.
Hotel forecasting
04 - Binomials models for Quantitative Finance
Creating a basic or binomial model on pricing an option.
02 - Statistical Moments
Introducing the first 4 moments of statistical analysis: mean, standard deviation, skewness and kurtosis. Showing how to use R and Python on these concepts. We then provide 2 methods to transform data in order to bring it closer to a normal distribution.
02 - Normality of asset returns
Checking the normality of asset returns visually and quantitatively.
04 - Time-series decomposition
Introducing time-series decomposition. We first show how to compose time-series using linear trend, seasonality and then white nosie.
Jensen's Inequality
02 - Stochastic Differential Equation - Part II
Some more examples of ito integrals.
01 - Stochastic Differential Equation - Part I
Introducing itô integrals.
04 -Martingales
Digging into Martingales. Making connections between martingales and itô integrals.
Modeling Option prices using Monte-Carlo simulations
Using the BSE and Monte-Carlo Methods to value option prices
Black-Schole Equation
Deriving the Black-Schole Equation and finding its solutions. Application with R and Python
05 - Trinomials models for Quantitative Finance
Creating a trinomial model and deriving the Forward Kolmogorov Equation.
03 - Random-walks & Brownian Motions
RW (discrete) and BM (continuous) constitute the way assets returns are modelled.