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This project showcases an advanced GARCH implementation in Python, APARCH(1,1). It determines the parameters best defining a stock's returns variance, and then uses these in a Monte Carlo simulation to simulate future returns with asymmetric volatility clustering.

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Advanced GARCH

This project showcases an advanced GARCH implementation in Python, APARCH(1,1). It determines the parameters best defining a stock's returns variance, and then uses these in a Monte Carlo simulation to simulate future returns with asymmetric volatility clustering.

APARCH(p1,q1)-DCC(p2,q2)

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This project showcases an advanced GARCH implementation in Python, APARCH(1,1). It determines the parameters best defining a stock's returns variance, and then uses these in a Monte Carlo simulation to simulate future returns with asymmetric volatility clustering.

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