The Journal of Finance and Data Science

Dataset#2 consists of the time series data for 129 worldwide financial products (Table 4), which include: - 9 major cryptocurrencies - 84 major forex - 19 major commodities - 17 worldwide financial indices The raw data contains the daily time series of the daily OPEN, HIGH, LOW, CLOSE and VOLUME. These raw time series data are provided by AvaTrade.com – one of the world major cryptocurrency trading platforms and Forex.com – one of the world major Forex MT4 trading platforms. To facilitate chaotic neural network training and system testing, we have generated time series data of 15 major financial technical indicators for these 129 financial products. Fellow researchers can base on these financial time series to test for the chaotic neural networks for time series financial prediction under different categories and products.

This repository hosts the Python package developed from the research presented in the paper "Topological Tail Dependence: Evidence from Forecasting Realized Volatility" and repositoy by Hugo Gobato Souto. The package is designed to implement the methodologies and techniques described in the paper, focusing on the application of topological data analysis to understand tail dependence in financial markets. The core of this package lies in its ability to model and forecast realized volatility in financial markets through the lens of topological data analysis. It presents a novel approach to understanding the complex relationships in financial data, especially during periods of high volatility or market stress.

French par-yield curves from October 22, 1987, through April 10, 2018.

French forward rate curves from October 22, 1987, through April 10, 2018.

French zero-coupon yield curves from October 22, 1987, through April 10, 2018.