Posted at 5:13 pm , on November 1, 2017
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.
Posted at 3:45 pm , on September 20, 2017
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%.
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Posted at 3:23 pm , on August 27, 2017
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!
Posted at 4:18 pm , on July 1, 2017
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.
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Posted at 3:51 pm , on June 1, 2017
The key issues for allocators and investors are the very low and mostly negative interest rates for core government bonds, toppish valuations in stock markets, permanent level of high risk-aversion by individual investors. How do we go from here?
Well, these are the questions that I am trying to solve in my current role for our client and for myself. In this respect, I was very pleased to be interviewed by Barbara Mack from Institutional Investor Journals to discuss my experience and my thoughts about the quantitative approaches to wealth management.
What I wanted to emphasize is the growing importance of quantitative tools and, in particular, robo-advisors, in the wealth management space.
The pdf with the transcript of my interview: Quantitative Approaches to Wealth Management
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