At Chilin Capital, we use simulation for a range of purposes, from detailed backtests of individual investment strategies, to portfolio risk management and stress testing. We also make use of stylised toy models to explore the impact of, and interaction between, different statistical assumptions.
Chilin Capital proposes a methodology using historical data to quantify the return premia for major asset-class based factors. The implemented innovations intend to improve the accuracy of our long-term return forecasts, what we achieving by the following methodologies:
- Use new asset class return proxies to extend our analysis much further back than the daily return histories of most modern indices.
- Separate the most heterogeneous of the prior paper’s factors, Commodities, into six sector-based factors for which the long-term premia are individually estimated.
- Apply (what we believe to be) common sense adjustments to long-term histories — slightly overweighting recent returns and applying empirically-based shrinkage across the observed historical Sharpe ratios to generate our forward-looking estimates of each factor’s premium.