MATLAB

**Value-at-Risk ** measures the amount of potential loss that could happen in a portfolio of investments over a given time period with a certain confidence interval. This example illustrates how to use Techila Distributed Computing Engine to speed up Value-at-Risk computations implemented with MATLAB.

**Calibrating models ** used in hedging strategies can be computationally intensive and subject to strict execution time requirements. In situations where models are calibrated for several underlying assets, the computations can be accelerated by using Techila Distributed Computing Engine.

R LANG

**Value-at-Risk ** measures the amount of potential loss that could happen in a portfolio of investments over a given time period with a certain confidence interval. This example illustrates how to use Techila Distributed Computing Engine to speed up Value-at-Risk computations implemented with R.

PYTHON

**Value-at-Risk ** measures the amount of potential loss that could happen in a portfolio of investments over a given time period with a certain confidence interval. This example illustrates how to use Techila Distributed Computing Engine to speed up Value-at-Risk computations implemented with Python.