Volatility targeting strategy in S&P500

This project used volatility targeting strategy and was constructed and processed on Python to design an optimal strategy that maximized the rate of return of the portfolios. According to Romain Perchet [2015], volatility targeting serves as a strategy that stabilizes between a risky asset and risk-free asset to keep the volatility at a constant level. The goal of which was achieved by forecasting the future return rate of S&P500 stock market based on its return rate in the past, using two approaches: GARCH model and standard deviation, namely, Equally Weighted Averages, with the volatility target of 10%. In the end, Sharpe Ratio, which was the average return rate of risk-free asset per unit of volatility, was used to calculate volatility, which proved that GARCH model was a better approach in volatility targeting strategy than standard deviation, due to volatility clustering.