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.

Application to finance

Value at Risk and Backtest Routines

R code for CAViaR model

Lasso Quantile Regression

Projects are developed for implementing the knowledge gained in the courses studied at World Quant University and meeting the requirement of clearing the courses.

Portfolio level (un)conditional risk measure estimation for backtesting using Vine Copula and ARMA-GARCH models.

Replication and extension of paper on Conditional Value at Risk (CoVaR) by Adrian and Brunnermeier.

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."

Statistical tests for Value at Risk (VaR) Models.

Measure market risk by CAViaR model

Financial modelling, derivatives, investments

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)

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.

Manuel Touyaa's porfotlio of Python projects/assignments for Finance Market Risk.

Model and replications scripts for the 2020 IMF Working Paper "Foreign Exchange Interventions Rules for Central Banks: A Risk-Based Framework"

The repo contains the main topics carried out in my master's thesis on operational risk. In particular, it is described how to implement the so called Loss Distribution Approach (LDA), which is considered the state-of-the-art method to compute capital charge among large banks.

Proposed solutions to selected exercises in the book "Value-at-Risk: Theory and Practice" (2nd edition) by Glyn A. Holton.

Calculation of Value at Risk using Generalized normal distribution, EGARCH and GARCH + EVT

Comprehensive financial risk analysis toolkit with Altman Z-Score bankruptcy prediction, Value at Risk (VaR) calculations, and historical crisis stress testing. Supports US & European markets with automated company classification.

R package providing functions for computing Expected shortfall (ES) and Value at risk (VaR)

Comparisons of financial metrics (e.g. VaR vs CVaR/ES, simple vs log returns, etc.).

A package for evaluating tail probabilities and partial moments for random vectors in multivariate generalized hyperbolic random vectors.

Financial risks of bonds