Code Samples

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.

Numerical optimization has a central role in many fields of applied mathematics ranging from quantitative finance to control theory. In situations where the loss function is computationally intensive and contains a routine that can be parallelized, the computations can be accelerated by using Techila Distributed Computing Engine.

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.

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.