Metadata-Version: 1.2
Name: var
Version: 0.0.5
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
License: UNKNOWN
Description: # Introduction
        
        "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 CeroviÄ‡ SmoloviÄ‡, 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).
        
        # Key Features
        
        Calculate, Backtest and Plot the
        
        - Value at Risk,
        - Conditional Value at Risk,
        - Conditional Drawdown at Risk, 
          
        with different methods, such that:
        - Historical
        - Parametric
        - Monte Carlo
        - Stressed Monte Carlo
        - Parametric GARCH 
          
        methods.
        
        # Examples
        For examples see [here]('https://github.com/ibaris/VaR')
        
        # Installation
        
        There are currently different methods to install `var`.
        
        ### Using pip
        
        The ` var ` package is provided on pip. You can install it with::
        
            pip install var
        
        ### Standard Python
        
        You can also download the source code package from this repository or from pip. Unpack the file you obtained into some directory (
        it can be a temporary directory) and then run::
        
            python setup.py install
        
        # Dependencies
        
        * Python: Python 3.7
        * Packages: numpy, pandas, arch, scipy, matplotlib, tqdm, seaborn, numba
        
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: End Users/Desktop
Classifier: Programming Language :: Python :: 3.7
Classifier: Operating System :: Microsoft
