WHO WE ARE
Fucius Capital was founded in 2018 in London. Since then, the founder has successfully run a number of managed accounts. Following the success over the past three years of these managed accounts, we excitedly launched the Fucius Volatility Arbitrage Fund in October 2020. This flagship fund aims to generate exceptional & returns by investing and trading volatility futures on the CBOE Volatility Index (VIX).
Our performance history has shown our strong ability to generate alpha irrespective of market conditions. The success is accomplished by using a proprietary trading algorithm, pioneering the research and development of volatility risk premia, in conjunction with portfolio optimisation and machine learning.
We believe there is a strong case for investors to add non-correlated strategies to their portfolios. Our performance has demonstrated such an investment vehicle is a compelling alternative to more traditional hedge funds.
WHAT WE INVEST
In 1993, the Chicago Board of Options Exchange (CBOE) introduced the Volatility Index (VIX). The index provides a market consensus on future volatility of Standard & Poor 500 Index. In March 2004, the Chicago Futures Exchanges (CFE) launched its first ever traded futures on VIX.
In 2019, daily volume/contracts of VIX futures on CFE stood at 250,000 and open interest (contracts that are held by traders and investors in active positions) was 400,000, with a daily trading volume of around US$ 4.2 billion.
Volatility is negatively correlated with the equity. It displays different risk-return characteristics. This unique feature makes it a truly alternative asset to invest in and to diversify investment risk. The dynamics of volatility follow many stylised patterns.
- it is mean-reverting;
- it has a long memory property;
- it has a heavier right tail.
Quantitative Research: The investment process begins with disciplined quantitative research. We have developed a sophisticated quantitative model to rigorously study a number of stylised factors for volatility behaviour. The model is then tested using statistical analysis and financial tools to ensure its robustness.
Designing and Testing: The model is built upon extensive research and financial economic theory, thus creating a robust trading strategy. This strategy has numerous variations that are all back tested, thereby refining and improving performance and so creating further potential capabilities. The algorithm is encapsulated using advanced computer programmes. This enables an effective transactional decision when market opportunities in the VIX arise.
Trade Execution: Neither discretionary inputs nor subjective views about the market are used. Trades are automatically executed by a seamless connection between our automatic system and the broker. These trades can be rebalanced daily or even more, during the day, as and when trading opportunities arise.
Risk Management: Our investment practice adequately meets standard risk practices and parameters. As the trading model incorporates calendar spreads with different expirations to hedge our investment position. We produce daily profit/loss and other limits to monitor our investment risk. We review our daily and weekly VaR (value at risk) to ensure that even under exceptional market conditions, market liquidity is adequately and effectively managed.
He held a PhD in mathematics from Washington University in St. Louis (WUSTL) and in finance from Cass business school. At Cass business school, his research topic was to examining volatility dynamics. He did extensive research in volatility and published research papers in academic derivative journals. He has actively engaged in modelling volatility behaviours, characterising volatility risk premia, designing and testing volatility trading strategies for 8 years. In 2013, his innovative research topic on characterising volatility risk premia was awarded funding support from the recognized Cass fund (Dean’s fund and Seed fund for innovation).
He has an extensive knowledge in financial economics, derivative instruments, time-series analysis, stochastic calculus and risk management.
Previously, he worked at the equity front office desk of Citigroup to support equity traders. While working at Citigroup, he designed and developed a portfolio optimisation model to solve various investment objectives and constraints for fund managers.
Prior to joining Fucius, Yong has spent over 20 years in the software industry mainly focusing on data analysis and machine learning. His career started as a software developer in Microsoft, then joined a data mining company as mathematician responsible for modelling large telecom datasets. After that, he spent a couple years as senior data scientist in a consultancy company and focused on building the model to predict payment default for insolvency clients. He also collaborated with a finance professor in University College Dublin for horse racing modelling and trading via betfair and racingpost.
Yong holds a PhD in computer science from Trinity College in Dublin, Ireland, received a Master in artificial intelligence and pattern recognition from Shanghai Jiaotong university in China, and BS in Mathematics with Honors from Wuhan University in China.
His specialising area includes feature engineering, predictive modelling and company fundamental/financial analysis.
Patrick is the Founder and Managing Director of Odin Capital Management Ltd. Patrick has 20 years professional trading and investment experience in the hedge fund industry. Prior to founding Odin Capital Management in 2006 he was responsible for Global Macro and quantitative strategies in the hedge fund group of Credit Suisse in Zurich. Before that, he entered the industry as a hedge fund analyst at FERI Alternative Assets.
He holds a PhD in economics from the University of St. Gallen, a European Master of Business Sciences and a Master of International Business from the University of Vienna. He passed all three levels of the CFA program and both levels of the CAIA program as well as the GARP FRM examination. He is the author of several hedge fund specific publications in recognized scientific journals and specialised books.
AWARDS and RECOGNITION
“Parametric modeling of implied smile functions: a generalized SVI model”, Review of Derivatives Research, April 2013, Volume 16, Number 1: Page 53-77 doi:10.1007/s11147-012-9077-x,