Python implementation of pricing analytics and Monte Carlo simulations for stochastic volatility models including log-normal SV model, Heston

A vectorized implementation of py_vollib, that supports numpy arrays and pandas Series and DataFrames.

Machine learning for financial risk management

Python wrappers around QuantLib and Pandas to easily generate volatility surfaces

Implementation of option pricing models using Numba that performs better. This entire project has utilized as little libraries as possible, even though certain models have their own Machine Learning Model with assessment and performance.

MSc Finance dissertation project at Newcastle University. This project focused on forecasting the volatility of exchange rates involving the Great British Pound using EWMA, GARCH-type and Implied Volatility models.

Quantitative Finance Library & Option Trading Tool

项目主要构建了多混频Realized GARCH-MIDAS-X模型，结合社交媒体情绪和高频数据，与 不加社交媒体情绪指标的模型相比，加入后的模型显著提升了内地低碳市场波动预测的准确性。通过稳健 性检验，证明了研究结果的可靠性。该研究丰富了市场波动模型，并为低碳投资和宏观调控提供了参考。

computes Volatility Spillover between Cryptocurrency (BTC/USD) and S&P 500 index

Dashboard for return, volatility and correlation analysis for the NAFTRAC IPC. Mexican Stock Exchange (BMV).

A package that utilises QT and OpenGL graphics to visualise realtime 3D volatility surfaces and analytics.

Predicting asset prices' directional movements based on implied volatility of price action. This experiment was performed on SPX index fund with VIX as implied volatility reference.

Implied volatility surfaces from SPX option chains data (both calls and puts), interpolation for continuous querying, and GUI to visualize surfaces and calculate Black-Scholes prices and IVs

Predicting future VIX movements using forked repository from @maylathant. Investigation on VIX price action and predictive modeling in order to aid in risk management on SPX index fund.

A Monte Carlo simulation project for financial risk analysis of Crude Oil Stocks.

Estimate GARCH models using the BHHH algorithm