maths in book

README.md

@@ -80,9 +80,9 @@ We offer a `SampleCovariance` class as seen above.
 Factor risk models use the projection of the weight vector into a lower
 dimensional subspace, e.g. each asset is the linear combination of $k$ factors.
 
-```math
+$$
 r_i = \sum_{j=1}^k f_j \beta_{ji} + \epsilon_i
-```
+$$
 
 The factor time series are $f_1, \ldots, f_k$. The loadings are the coefficients
 $\beta_{ji}$.
@@ -94,15 +94,15 @@ factor space. The variance for a position $w$ is the sum of the variance of the
 systematic returns explained by the factors and the variance of the
 idiosyncratic returns.
 
-```math
+$$
 Var(r) = Var(\beta^T w) + Var(\epsilon w)
-```
+$$
 
 We assume the residual returns are uncorrelated and hence
 
-```math
+$$
 Var(r) = y^T \Sigma_f y + \sum_i w_i^2 Var(\epsilon_i)
-```
+$$
 
 where $\Sigma_f$ is the covariance matrix of the factors and $Var(\epsilon_i)$
 is the variance of the idiosyncratic returns.

book/_config.yml

@@ -13,7 +13,7 @@ parse:
 # needed for plotly
 sphinx:
   config:
-    mathjax_path: https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js
+    mathjax_path: https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML
     html_js_files:
     - https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js