{"repo":"ibaris/VaR","free":true,"listed":false,"github":"https://github.com/ibaris/VaR","clone":"git clone https://github.com/ibaris/VaR.git","description":"Value at Risk and Backtest Routines","language":"Python","stars":33,"topics":["quantitative-finance","value-at-risk"],"license":null,"category":"trading","readme_excerpt":"Value-at-Risk Introduction • Key Features • Examples • Installation • Dependencies • Introduction The \"VaR\" package is a comprehensive Python tool for financial risk assessment, specializing in Value at Risk (VaR) and its extensions. It enables robust financial risk forecasting by incorporating methods like historical, parametric, Monte Carlo, and Parametric GARCH. It also focuses on the Probability Equivalent Level of VaR and Expected Shortfall (PELVE). The package also features backtesting capabilities, distribution fitting, and detailed plotting options for clear visualization. Designed for ease of use, it includes practical examples and is easily installable through pip. With dependencies like numpy and pandas, it's tailored for those seeking advanced risk measurement tools in finance. Key Features Calculate, Backtest and Plot the - Value at Risk, - Conditional Value at Risk (Expected Shortfall), - Conditional Drawdown at Risk, - Probability Equivalent Level of VaR and Expected Shortfall - with different methods, such that: - Historical - Parametric - Monte Carlo - Parametric GARCH methods. Examples In this example we will show all the key features of the var package. At first we will import all necessary packages. To quickly test and demonstrate the functions, the package includes a function named load data , which by default includes the daily returns of stocks TSLA , AAPL and NFLX . The only important thing in the data preparation is that the columns contain the indivi","default_branch":null,"files":null,"tree":[],"storefront":"/r/ibaris","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/ibaris/VaR/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}