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Here are
151 public repositories
matching this topic...
Python-based portfolio / stock widget which sources data from Yahoo Finance and calculates different types of Value-at-Risk (VaR) metrics and many other (ex-post) risk/return characteristics both on an individual stock and portfolio-basis, stand-alone and vs. a benchmark of choice (constructed with wxPython)
Implementation of a variety of Value-at-Risk backtests
A collection of various computational methods to optimize a user's investment portfolio using Modern Portfolio Theory and optimizing various factors such as Returns, Sharpe Ratio and Risk.
👁 VaR
Value at Risk and Backtest Routines
👁 WQU-Projects
Projects are developed for implementing the knowledge gained in the courses studied at World Quant University and meeting the requirement of clearing the courses.
Lasso Quantile Regression
Portfolio level (un)conditional risk measure estimation for backtesting using Vine Copula and ARMA-GARCH models.
Undergraduate thesis, Seoul National University Dept. of Economics — "Modeling Volatility and Risk Spillover Between the Financial Markets of US and China Using GARCH Value-at-Risk Forecasting and Granger Causality."
Replication and extension of paper on Conditional Value at Risk (CoVaR) by Adrian and Brunnermeier.
Statistical tests for Value at Risk (VaR) Models.
Measure market risk by CAViaR model
Financial modelling, derivatives, investments
👁 value-at-risk
Shows how banks can modernize their risk management practices by back-testing, aggregating and scaling simulations by using a unified approach to data analytics with the Lakehouse.
Python code for rolling Value at Risk(VaR) of fiancial assets and some of economic time series, based on the procedure proposed by Hull & White(1998).
R Finance packages not listed in the Empirical Finance Task View
One-week side project to play around stochastic optimization (how to take *good* decisions under uncertainty)
👁 VaR-threshold-and-confidence-interval
This project studies the effects of the shape parameter estimator uncertainty at different threshold levels on the value-at-risk confidence interval for quantitative risk management (QRM) using the Generalized Pareto Distribution (GPD) from the Extreme Value Theory (EVT) approach.
Weekly exercises of the course of Stochastic Methods for Finance.
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