Recently I have been working on applying machine learning for volatility forecasting and trading. I presented some of my findings at QuantMinds Conference 2018 which I wanted to share in this post.
My presentation is available at SSRN with the video of the talk in YouTube.
Exchange traded products with the short exposure to the implied volatility of the S&P 500 index have been proliferating prior to “Volatility Black Monday” on the 5th of February 2018. To investigate the crash of short volatility products, I will analyse the intraday risk of these products to steep intraday declines in the S&P 500 index. As a result, I will demonstrate that these products have been poorly designed from the beginning having too strong sensitivity to a margin call on a short notice. In fact, I estimate that the empirical probability of such a margin call has been high. To understand the performance of product with the short exposure to the VIX, I will make an interesting connection between the short volatility strategy and leveraged strategies in the S&P 500 index and investment grade bonds. Finally, I will discuss some ways to reduce the drawdown risk of short volatility products.
What is the most significant contributing factor to the performance of a quantitative fund: its signal generators or its risk allocators? Can we still succeed if we have good signal generators but poor risk management? How should we allocate to a portfolio of quantitative strategies?
I have developed a top-down and bottom-up model for portfolio allocation and risk-management of quantitative strategies. The interested readers can find the slides of my presentation here and can watch the webinar can be viewed on youtube.
During past years I have found the great value in using implied and realized volatilities for volatility trading and quantitative investment strategies. The ability to stay focused and to follow quantitative models for investment decisions is what sets you apart in these volatile markets and contributes to your performance. The implied volatility from option prices typically overestimates the magnitude of extreme events across all assets – see the figure above The volatility risk-premia can indeed be earned using a quantitative model.
Nevertheless, after many years of working on volatility models, I realize that there a lot of gaps and inconsistencies in existing models for measuring and trading volatility. Unsurprisingly, by designing a model that sets you apart from the existing ones, you can significantly improve the performance of your investment strategies.
In workshop presentation at Global Derivatives Conference 2016 I have discussed in depth the volatility risk premia. The beginning and largest part of the presentation is devoted to measuring and estimating historical volatilities. The historical volatility is the key to many of the quantitative strategies, so that the historical volatility an important starting point in all applications. Then I discuss delta-hedging, transaction costs, and macro-risk management. Finally, I discuss using volatility for systematic investment strategies.
In my last post I have discussed the growing popularity and demand for strategies investing in the volatility risk-premia. One of the recurring questions that arises when I discuss this topic is whether it makes sense to allocate to these strategies in a low volatility regime. In this post I will present evidence that the current level of the implied volatility serves as a weak predictor for the performance of a short volatility strategy. Instead, the two factors are significant to explain and predict the performance of the short volatility strategy: first, the realized volatility of the VIX and, second, the roll yield associated with the term structure of the VIX futures.
I present a few systematic strategies for investing into volatility risk-premia and illustrate their back-tested performance. I apply the four factor Fama-French-Carhart model to attribute monthly returns on volatility strategies to returns on the style factors. I show that all strategies have insignificant exposure to the style factors, while the exposure to the market factor becomes insignificant when strategies are equipped with statistical filtering and delta-hedging. I show that, by allocating 10% of portfolio funds to these strategies within equity and fixed-income benchmarked portfolios, investors can boost the alpha by 1% and increase the Sharpe ratio by 10%-20%.
Empirical studies have established that the log-normal stochastic volatility (SV) model is superior to its alternatives. Importantly, Christoffersen-Jacobs-Mimouni (2010) examine the empirical performance of Heston, log-normal and 3/2 stochastic volatility models using three sources of market data: the VIX index, the implied volatility for options on the S&P500 index, and the realized volatility of returns on the S&P500 index. They found that, for all three sources, the log-normal SV model outperforms its alternatives. Keep on Reading!
Q: Euan Sinclair
A: me 🙂
Q: What is your educational background?
A: My educational background is a bit unusual. I have a PhD in Probability and Statistics which I obtained after obtaining a bachelor in mathematical economics and three master degrees in statistics, industrial engineering and mathematical finance. As a result, I like to think I am truly diversified when it comes to work and experience. It was not my intention to get many degrees, I was driven by curiosity and desire to learn new skills.
Q: (given that I know the answer to that) how did you get from a phd in statistics to direct involvement in the markets? Did you ever intend to be an academic?
A: Since my undergraduate studies, I have been attracted to the capital markets, first as an observer, then as a researcher, and finally as a professional and an investor. I enjoy academic research as a way to postulate the hypothesis based on some assumptions and then apply empirics to test it. In statistics, we always differentiate between a population and a sample. So, we can create a theoretical model for the population and test it using a sample, but not the other way around. I am interested in both developing models and also in testing them empirically. With a pinch of salt, I like to think that the finance is a unique field. On one hand, it is very challenging to create a theoretical model because of the number of assumptions we need to make. On the other hand, the testing of theoretical ideas is also challenging because of the limited data samples. At the same time, I believe people still under appreciate the power of quantitative research for financial applications. As an example, I suggest to read this fascinating article : “Buffett’s Alpha”. Warren Buffett created his wealth not because of stock picking but because of sticking to a quantitative strategy. Personally, I didn’t think of becoming an academic, I was pursuing my studies to have a deeper understanding of the theoretical background and then work on developing quantitative models for financial applications.
What is volatility trading?
In this post I would like to discuss a practical approach to implement the delta-hedging for volatility trading strategies. While it is customary to assume a continuous-time hedging in most of the industrial applications and academic literature, the delta-hedging in practice is applied in the discrete time setting. As a result, to optimise the delta-hedging for the practical implementation, we need to consider the discrete time framework. That is why I would like to highlight some of my research and discuss my approach under the discrete time setting and the transaction costs to optimize the delta-hedging.