anthonyli01 Goto Github PK
Name: Anthony Li
Type: User
Bio: Aspiring Data Scientist and Quantitative Researcher. LinkedIn: https://www.linkedin.com/in/anthonyli01/
Location: London, UK
Anthony Li's Projects
advanced-simulation-methods
This project focuses on applying advanced simulation methods for derivatives pricing. It includes Monte-Carlo, Variance Reduction Techniques, Distribution Sampling Methods, Euler Schemes, and Milstein Schemes.
loan-default-prediction
University Project: building a random forest to predict loan defaults. This involves data processing, standardization, optimization, performance metrics, and model analysis.
ma-crossover-strategy
University Project: Building a simple moving average crossover trading strategy.
matlab-time-series
University Project: Implementing DCC, a multivariate conditional volatility model.
multi-factor-portfolios
University Project: constructing portfolios by blending different types of factor portfolios (low-beta, value, and momentum). We investigate different techniques to weight our portfolio and calculating a combined score.
neural-network-approach-to-implied-volatility-forecasting
Implied volatility is a key aspect when it comes to derivatives pricing. With the growing influence of machine learning in finance, I have investigated the use of LSTMs to forecast 1-day forward Implied Volatility.
portfolio-optimization
In Progress: We will investigate the common portfolio optimization methods and explore new ways to improve on this. We will start by building Minimum-Varance Portfolios, Maximum Sharpe Ratio portfolio, and building the efficient frontier. We will then investigate Kalman filters, and better ways to estimate the covariance matrix.
r-derivatives-pricing
University Project: simulation techniques to price derivatives. It will involve Monte-Carlo, variance-reduction techniques, and advanced simulation methods.
regression-model-for-car-prices
University Project: using linear regression models to predict secondary market car prices based on a series of features. We will apply variable selection techniques and optimisation in attempt to build the best predictive model.
statistical-arbitrage-pairs-trading-strategy
On-going project: I will be implementing a combination of pairs trading strategies in attempt to see which type performs best after backtesting. The main ideas involve cointegration, kalman filter, copulas, and machine learning approaches. Since it is a market-neutral strategy, we will analyse the performance on its alpha rather than sharpe ratio.
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