# Brief overview of all financial models and quant strategies we use for our QuantLabs.net Premium Membership monthly service

(Last Updated On: November 24, 2011)

Brief overview of all financial models and quant strategies we use for our QuantLabs.net Premium Membership monthly service

This is a VERY brief description of the models and strategies used for the QuantLabs.net Premium service. Do note that these are technically and mathematically advanced so some terminology may be advanced for some users. I am hoping to post detailed articles on each with some YouTube videos (www.Youtube.com/quantlabs) to accompany these definitions. This will be in the future before the service goes into a monetization phase.

Note each the following models/strategies are applied to each market symbol screened daily by our real time application which broadly sweeps various global market regions for best performing equities. This is usually the top three of each region. For now we track the regions of Denmark, India, Australia, China, USA, Canada, UK, France, Switzerland, Brazil, Hong Kong, Norway, Sweden, and Germany. More may be added as demand requests it.

Equity Invariants

This does simple invariance assuming an independence and identically distribution (i.i.d). Two plots are generated. The first is a histogram to check various variables are independently distributed. A second scatter plot is generated to check the variables are independent under i.i.d. An ellipsoid circle will display to indicate local dispersion.

Compounded Return Estimation Interval

This projects a distribution of compounded returns from an estimation interval of the investment horizon. Distribution of prices at the investment horizon is then computed.

This uses classic pair trading techniques against 5 picked (from the same day) equity market symbols. You would hopefully see a profit in the short and long positions by using a mean reversion behaviour. Total payoff and total transaction costs are displayed as well based on certain capital amounts for each position.

Bayesian Jointly Uniform Prior Correlations Implies

This shows how a jointly uniform distribution on correlation implies marginal distribution of each correlation when it peaks around zero. This also plots both univariate marginals and bivariate marginals.

Shrinkage Estimator
This computes the multivariate shrinkage estimator of location and scatter plot data under normal assumptions of any market asset supplied to the model.

Statistics Summary Projection

This projects a summary of various statistics of arbitrary horizons.  This displays of each supplied market symbol:

single-period standardized statistics, central moments,

single-period non-central moments,

single-period standardized statistics,

multi-period cumulants

multi-period non-central moments

multi-period standardized statistics

Equity Projection Pricing

This projects the distribution of market invariants (i.e. compounded returns of a stock) of estimation interval of an investment horizon. The estimation is normally assumed which also computes the distribution of prices at various investment horizon points which is done analytically using Monte Carlo, delta, and duration approximation. Investment horizon points include 1 day, 1 week, 1 month, 1 year, and 2 years.

Equity Quantile Value at Risk (VaR)

This computes a quantile using VaR. The objective uses a Student T distribution and does simulations using the generated sample quantile. This does extreme value theory (EVT) approximation as well. A plot is generated displaying these three methods of exact, simulation, and EVT.

Distribution of Market Invariants

This will project the distribution of market invariants which include stock market compounded returns. This will then estimate the distribution of prices and performs a two step mean variance optimization.  The risk aversion parameter is set at 10. Mean variance inputs are provided analytically and numerically (using mean and covariance). First step of mean variance optimization uses quadratic mean variance optimization which determines one parameter frontier of a quasi optimal solution. The second step evaluates satisfaction for all allocations of the frontier and will pick the best. Two plots are created where one is the mean variance pricing frontier. A second plot displays the satisfaction as a function of standard deviation on the frontier.

Note that more models will be added as demands allows. Also, the focus of the service is on popular FX currency pairs, best performing daily equities, some fixed income assets, and futures.