Xinqian Li's repositories
Bitcoin-Futures-price-forecasting-and-volatility-surface
Implementing Bitcoin futures' strike prices and time-to-maturity to construct a volatility surface for potential profit opportunities. Utilizing time series and the GARCH model for volatility forecasting and Long Short-Term Memory (LSTM) for bitcoin futures' price forecasting in Python.
Comparison-in-confidence-interval-methods
We conducted simulation study to estimate the probability of success, denoted as p, from a Binomial distribution. Our primary focus is to assess the performance of various confidence interval procedures.
Financial-Risk-Analysis
This project is to provide a flavor of “real-life” experience of a quantitative financial analyst with the risk management concepts in financial risk analysis. The project involves activities of data preparation, financial modeling and analysis, documentation and reporting.
Stock-classification-with-K-means-and-KNN
Using market capitalization and price per earnings growth as metrics run clustering using K-means algorithm on companies traded in Russell 2000 index with 9 clusters. Comparing the cluster positioning with that of the Morningstar rating for those companies and observe how these boundaries are matching with expectations.
Volatility-Forecasting-for-Dynamic-Portfolio-Optimization
Conducted data extraction from Bloomberg Terminal and created a benchmark (Bridgewater holdings) of 700+ assets and a testing portfolio. Achieved a 1.416 Sharpe ratio (0.4 over Bridgewater's portfolio), and a volatility of 0.093 (0.02 lower than the S&P500).