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Would love to see a thread in the commons with a link to the paper, I'm afraid this won't work too wel, it's a pricing SDE and the efficacy of calibrating parameters to historic or speculative drift/vol remains to be seen!
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Monte Carlo is a simulation technique; Geometric Brownian Motion (GBM) is one of the most commonly used stochastic models within it, but it is not the only one.
It also forms the foundation of the Black–Scholes model for options pricing, although it is not a perfect model. This framework is mainly focused on modeling a single asset return process rather than strategy dynamics or multi-asset portfolio construction.
QuantStats (Python library, GOOGL simulation example) uses historical return shuffling / bootstrap Monte Carlo.
And honestly, for trading strategies and portfolio analytics, this approach is often more realistic than assuming a pure GBM model, although it is still not perfect.
It also forms the foundation of the Black–Scholes model for options pricing, although it is not a perfect model. This framework is mainly focused on modeling a single asset return process rather than strategy dynamics or multi-asset portfolio construction.
QuantStats (Python library, GOOGL simulation example) uses historical return shuffling / bootstrap Monte Carlo.
And honestly, for trading strategies and portfolio analytics, this approach is often more realistic than assuming a pure GBM model, although it is still not perfect.
Reply