My Finance

Here we describe the final steps we make to complete the simulator, first as a collection of Python files, later an online web app.

1. Make available classic (frequentist, with fixed point estimates for the covariance matrix of regression residuals and for regression coefficients) and Bayesian (what we called Bayesian-lite, with fixed point estimates for the covariance matrix of regression residuals but objective Bayes inference for only regression coefficients) options.
2. Make two versions of the model for stocks: simple (with only volatility as factor for international developed and emerging stocks, but also BAA corporate bond duration and the new valuation measure based on dividends for domestic stocks) and complete (with BAA corporate bond duration for all three stock asset classes, and the new valuation measure for both domestic and developed, but not emerging stock markets).
3. Make two versions of the model for bonds: simple (with Treasury long-term bond returns modeled as a deterministic function of this and last end-of-year rates) and complete (with noise components there, based on historical returns of 7-year zero-coupon Treasury bonds from 1962).
4. All eight versions of the model must have 10000 simulations. Moreover, all coefficients and covariance matrices, including objective Bayes inference, encoded directly in the Python code, not inferred via reading external data file or separate fitting of regressions. The latter takes some time, and while this does not seem to be so much, it does take extra 3-4 seconds on my local computer, which translates into 1-1.5 seconds on the web app (which is generally 3 times faster than my local computer). The difference between say 1.5 and 3 seconds is huge for the casual user.
5. There must be a separate Python file main.py which combines simulations from each of 8 chosen models and giving this graphical output, as well as a document about the exact equations and verification governing the chosen model.