A tail-focused asset path simulation technique

An important feature of financial markets is that they are highly unpredictable. But despite market randomness, finance professionals spend considerable amounts of time and effort designing the best possible frameworks for reality. These frameworks, which help model and quantify the investment strategy, come in all shapes and sizes and are used in the investment decision process. The effectiveness of decisions taken in the light of these representations of reality will depend on how well they have been constructed.

Nowadays most finance professionals use Monte Carlo simulation methods for generating assets paths. Others recycle the range of observed returns to run Historical Simulations. We would like to discuss the pros and cons of those two simulation approaches and present a third one, Filtered Historical Simulations (FHS) that overcomes some of the biggest drawbacks of Monte Carlo and Historical Simulations.


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