Release Notes
Change log for communicating new features, fixes, and revisions of Excel Lab.
Last updated
Change log for communicating new features, fixes, and revisions of Excel Lab.
Last updated
Happy holidays! Santa has been busy, new API connection this holiday season.
New licensing server API connection.
New licensing API connection.
Preparations for licensing server upgrade support for unlock code feature.
Fixed a gremlin that is fussy on double-precision machine errors.
now checks and accounts for double-precision differences in the upper and lower triangular corrected matrix, i.e. symmetry is verified.
Refinements to several existing functions and new runtime framework.
New runtime (9.13).
Added desmoothing returns functionality to Excel Lab.
Improvements to authentication process.
Small improvements to calculator logic and verification.
It has been an exciting week working with early adopters, we have had very constructive feedback.
Wrapper fix for VERIFYLICENSE.
It has been an exciting week working with early adopters, we have had very constructive feedback.
Security enhancements in offline license manager.
Ready for prime time!
Improved licensing verification.
Revised matrix orientation and dimensional fixes for iso-curve functions.
Feature-creep is getting intense!
COVMATRIX an much easier and direct way to compute covariance matrix, with the ability to treat missing data as well.
CORRMATRIX you can't have the covariance matrix without some correlation coefficients.
added specification for covariance matrix as an optional parameter.
improved handling for machine epsilon.
now uses Newton's method by minimizing the Frobenius distance.
using a first-order autoregressive model.
Introducing scenario modification functionality to Excel Lab based on our innovations in .
solve for the implied scenario estimates across economic variables to reconcile with your target probabilities.
Default marshalling between Microsoft Excel and Excel Lab's engine for missing values. This will impact and where users will have to explicitly specify missing values in their workbook's cells as in the formula bar.
sample from a dataset with replacement with equal or custom probability weights across sample periods. Draw from a univariate or multivariate sample with the option to apply a set of portfolio weights to the multivariate sample.
now also returns general information in cell in addition to a UI dialog.
new optional input arguments, giving you more control.
the threshold parameter is now based from an inliers perspective.
minor refinements to code elegance and speed.
Happy new year
concatenate disjointed vectors (or matrices) quickly and intuitively within Excel.
added an optional free parameter to model fatter tails for calculating scenario probabilities.
solve for multiple optimal portfolios on the efficient frontier.
solve for multiple optimal portfolios to evaluate relative risk.
Sometimes, we have to regress in order to progress
maximum drawdown in Excel - forget your VBA code upkeep!
forget nested MMULT and SUMPRODUCT formulas! Vector math simplified!
added argument to allow the specification for the predictor values for the partial sample regression model. This allows the analyst to specify any set of predictor values to evaluate a response for the dependent variable forecast based. If not specified, as previously implicitly assumed, the most recent observations of the dependent variables are used with the model parameters for forecasting.
Hello world! Lots of coffee and rock music in the background at . The Excel Lab quant library is being tested and worked on extensively by the team. We are getting things ready to bring this exciting tool to your desk.
calculate equilibrium returns
run a reverse optimization
maximum-likelihood return estimates
annualize risk to account for compounding and log-normality
exponential moving average risk model
estimate expected risk from historical data
compute the Mahalanobis distance statistic
maximum-likelihood risk estimates
estimate risk based on statistical outliers
estimate risk based on statistical inliers
multi-goal optimization (mean-variance tracking error)
mean-variance optimization
mean-tracking error optimization
solve for an iso-return efficient frontier (risk vs. tracking error)
run multi-factor OLS and stepwise regression analysis
estimate probability of loss continuously and conventionally
calculate the omega ratio
run partial-sample regressions, a new novel approach to regression analysis
calculate the Sortino ratio
estimate tail ratio from empirical data
estimate value at risk measures continuously and conventionally
check matrix for positive semi-definite (PSD) properties / solve to nearest PSD values
simulate multi-variate normal random values using Monte-Carlo
calculate scenario probabilities implied from empirical data