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MCarloRisk3D


MCarloRisk3D
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Description

MCarloRisk3D: MCarloRisk with 3D viewing options for better understanding of the estimated probability surface.

Stock price risk analyzer app for the common man. Now with optional Black Swan events and tunable forward volatility.

Estimates future price distribution using random walk theory.


Background discussion: E. Fama article on early random walk studies from the 1960's:

 http://www.ifa.com/Media/Images/PDF%20files/FamaRandomWalk.pdf



New model calibration tutorial:

 http://diffent.com/tuning1.pdf



Example use case & training guide for studying "AAPL to $320" can be found at:

 http://diffent.com/AAPL320arialP.pdf



The app uses prior data from the stock in question for volatility estimates. 

User can control how far back in time to use historical data to capture only the current "epoch" of a company or of the market as a whole if desired.


Built-in backtesting, verification, and model tuning tools.



-- Details --



This app models daily stock returns as a stable stochastic process and estimates a future price distribution by Monte Carlo re-sampling from an "empirical distribution" of a user-specified subset of prior (known) daily returns. 

Be sure to press the Run Monte button on the Monte Carlo tab after changing settings or downloading a new data set. 

This app downloads historical data from Yahoo Finance as base data to resample. Prices are converted to daily returns [P(t)/P(t-1)] before resampling. The user can choose how far back to resample. By estimating a probability distribution of future prices at the user-specified investment horizon in this manner, we can give risk-of-loss estimates in thumb-rule fashion. 

Reports out estimated price and %loss estimates at the commonly used levels of 1st percentile and 5th percentile (1% and 5% risk). Also reports out median (50th percentile) price estimates at the given number of days forward. Calculations may be performed on Yahoo daily Closing or Adjusted daily Closing price data. An artificial shock filter is provided, which can be used to reject the resampling of prior returns that are artificially large (due to splits or other artificial re-valuations that do not affect the underlying value of the asset).

The stochastic model may be tuned or calibrated only by adjusting the maximum number of days backwards to sample or adjusting the black swan parameters.

Model Validation features:



On the Monte Carlo tab, you can withhold any number of recent days from the model and then plot the results of the stochastic risk forecast as lower-bound envelopes at 1% and %5 estimated probability (risk) levels. 



Validate tab:



This allows you to perform an exhaustive validation on your model by withholding several points, computing the model, comparing the forward prediction of the model versus the actual reserved data, and repeating this in increasing time sequence for all withheld points.



A vertical "Cursor Beam" is provided that you can drag across the new plots in the Monte Carlo tab and the Validate tab to show the plotted values from several curves at once, with the values color-coded to the curves.



Show the full price probability plot linked to the days-forward setting of the Monte Carlo graph. This is a slice thru the probability surface generated by the Monte Carlo procedure.


The app provider makes no claims as to the suitability of this app for any purpose whatsoever, and the user should consult an investment advisor before making investment decisions.

What's New in Version 2.2

Add "skew adjust" setting on Opt popup window. Skew adjust > 0 tells the app to put a scaling factor on the positive historical returns before constructing the monte carlo paths, and a scale-reducing factor on the negative returns. Skew adjust < 0 does the opposite. This provides another tuning factor when you are attempting to calibrate a model to withheld data, or if you want to add additional expectations to your model (e.g. if you expect that the future will be better than the past, you can set skew adjust accordingly).

For example, if we set skew adjust to 0.1, the positive historical returns will be multiplied by 1.1 and the negative historical returns will be multiplied by 0.9 before the monte carlo random walk paths are constructed.

When we say "skew adjust," we are merely providing a "handle" to approximately adjust the skewness of historical returns. As the reader may know, skewness is a cubic quantity (see Skewness wiki article below), and so a linear scaling factor will not yield a linear change in the formal "skewness" value. Also, skewness is formally calculated around the mean of the data. We are assuming a mean 0 (no change day-to-day, return = 0%) as the centerpoint of our skew adjust factor.

However, as the skew adjust factor is propagated through the equations, the skewness effect is approximately linear if we keep skew adjust near zero (+ or -).

More info on skew:

https://en.wikipedia.org/wiki/Skewness

Screenshots

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