Open source analytics and market risk library from OpenGamma

Fixed Income Analytics, Portfolio Construction Analytics, Transaction Cost Analytics, Counter Party Analytics, Asset Backed Analytics

Using Bidirectional Generative Adversarial Networks to estimate Value-at-Risk for Market Risk Management using TensorFlow.

Machine learning for financial risk management

Practical, hands-on risk modeling, risk assessment and verifications of risk models across major risk classes and understanding risk regulation as well. Implementing risk models in Python, R and Excel.

Measure market risk by CAViaR model

XQRiskCore is a governance-grade risk control engine for trading — with unified trade approval, structured audit logging, role-based access control, and multi-layer enforcement.

An Excel integration of OpenGamma Strata.

Java SDK providing access to the OpenGamma API

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

Testing Code abount quantitative finance algorithms

Excel bindings for OpenGamma's Strata library

Forecasting the performance of an asset and quantifying the uncertainty associated with such a forecast is a difficult task: one that is frequently made more difficult by a shortage of observed market data. This example illustrates one approach for creating a price forecast based on option price data.

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

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.

Repositório com o código-fonte do Derivativos e Risco de Mercado

Financial risks of bonds

Statistical techniques for Market Risk Modelling (VaR and ES)

These are two sets of Analysis done in this Project. Financial Risk Analysis to check the Credibility of companies and Market Risk Analysis for 10 different Indian Stocks.

Measuring Market Risk in R

I experiment in this project using different neural network approaches to predict gap risk on the price of financial assets.

Market risk analytics dashboard in Python and Streamlit that computes portfolio volatility, drawdowns, VaR/ES, rolling correlations, and stress tests (shocks + COVID‑style crisis window) for equity/ETF portfolios. ​

Repository represents python usability of measuring and managing risks (practice tasks and real cases)

Treasury products valuation and risk measurement using J programming -- under progress

This example shows how to analyze aspects of risk contagion in financial time series using mathematical tools such as graph theory and Markov chains.

LiquidityVaR

Python implementation of Risk Parity (ERC) optimization with tactical Trend-Following overlays. Empirical analysis based on Bhansali et al. (2024).

A MATLAB implementation of the Adrian-Crump-Moench (ACM) term premium model described in "Pricing the Term Structure with Linear Regressions", Federal Reserve Bank of New York Staff Reports, Staff Report No. 340, April 2013.

Quantitative risk engine: Historical Value at Risk (VaR), Expected Shortfall (CVaR), and VaR backtesting (Kupiec test) built in Python using multi-asset market data.

Implementation of a Historical Simulation Value at Risk (VaR) framework in Python, including Expected Shortfall, portfolio P&L estimation, backtesting analysis, and Basel Traffic Light classification.