Feel free to use my code.

If you're interested in my GitHub, then here's the link.

### R Packages

An improved and simplified package for MF-DFA analysis, including significance testing, surrogate algorithms (IAAFT, IAAWT) and simulation of multifractal series.

Spectral Projected Gradient method with Inexact Restoration: An algorithm for large-scale nonlinear optimization problems with nonconvex constraints. We apply the algorithm here to solve the "nearest option-implied correlation matrix problem".

Method for fitting the option implied volatility surface via the Stochastic Volatility Inspired (SVI) equation, computing the risk-neutral density distribution (RND), and risk-neutral moments (skewness, kurtosis, etc.). Details can be found in my working paper.

### Python

Iterated Amplitude Adjusted Wavelet Transform for Python. We're using it in this paper to transform the heavy-tailed distribution of daily returns to a normal distribution while keeping the structure of volatility clustering.

Method for fitting the option implied volatility surface via the Stochastic Volatility Inspired (SVI) equation. Details can be found in my working paper.