Metadata-Version: 2.1
Name: var
Version: 2023.6.2
Summary: Different Methods to Estimate the Value-at-Risk of a portfolio.
Home-page: https://github.com/ibaris/VaR
Author: Ismail Baris
Author-email: i.baris@outlook.de
Maintainer: Ismail Baris
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: End Users/Desktop
Classifier: Intended Audience :: Financial and Insurance Industry
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Operating System :: Microsoft
Description-Content-Type: text/markdown
License-File: LICENSE

"The search for appropriate risk measuring methodologies has been followed by increased financial uncertainty worldwide. Financial
turmoil and the increased volatility of financial markets have induced the design and development of more sophisticated tools for
measuring and forecasting risk. The most well known risk measure is value at risk (VaR), which is defined as the maximum loss over
a targeted horizon for a given level of confidence. In other words, it is an estimation of the tails of the empirical distribution
of financial losses. It can be used in all types of financial risk
measurement" ([Julija Cerović Smolovic, 2017](https://doi.org/10.1080/1331677X.2017.1305773)).

In addition to Value at Risk, the package includes Conditional Value at Risk (Expected Shortfall or CVaR) and Conditional Drawdown
at Risk (CDaR).
