Artur Sepp Blog on Quantitative Investment Strategies

Systematic Trading Strategies – Volatility, Trend-following, Risk-Premia – Asset Allocation and Wealth Management
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  • Category: Volatility Trading

    • My talk on Machine Learning in Finance: why Alternative Risk Premia (ARP) products failed

      Posted at 2:56 pm by artursepp, on November 27, 2018

      I have recently attended and presented at Swissquote Conference on Machine Learning in Finance. With over 250 participants, the event was a great success to hear from the industry leaders and to see the recent developments in the field.

      The conference featured very interesting talks ranging from an application of natural language processing (NLP) for industry classifications to a systematic trading in structured products using deep learning. For the interested, the slides and videos are available on the conference page.

      I would like to share and introduce my talk presented at the conference on applications of machine learning for quantitative strategies (the video of my talk available here).

      In my talk, I address the limitations of applying machine learning (ML) methods for quantitative trading given limited sample sizes of financial data. I illustrate the concept of probably approximately correct (PAC) learning that serves as a foundation to the complexity analysis of machine learning.

      In particular, the PAC learning establishes model-free bounds on the sample size to estimate a parametric function from the sample data for a specified level of approximation and estimation error. I recommend very nice textbooks An Elementary Introduction to Statistical Learning Theory and The Nature Of Statistical Learning Theory to study more about the PAC learning.

      I also present an example of using supervised learning for the selection of volatility models for systematic trading from my earlier presentation.

      Finally, I touch on the important topic of the risk-profile of quantitative investment strategies and, in particular, Alternative Risk Premia (ARP) products. For the past few years, since about 2015, the sell-side have been marketing a plethora of ARP products as “cheap” substitutes for hedge fund strategies. However, ARP products fared miserably throughout year 2018 despite the fact that most of these products were marketed as market-neutral. I wanted to share my view why ARP products failed…

      The typical creation process of ARP products is as follows. First, a research team runs multiple back-tests of “academic” risk factors (value, carry, momentum, etc) across many markets until a specific parametrization of their strategy produces a satisfactory Sharpe ratio (around 1.0 or so). Once the necessary performance target is achieved in the back-test, the research team along with a marketing team would write a research paper with economic justification of the strategy. Then the marketing team would pitch the strategy to institutional clients. If the marketing team is successful, they would raise money for the strategy. Finally, the successful strategy (out of dozens of attempted) would reach to the execution team who would implement the strategy in a trading system and execute on behalf of clients.

      The creation of ARP products serve as a prime example why we need to understand the limitations of statistical learning given limited sample sizes of financial data. Also, there is the incentive to fit a rich model to the limited sample to optimize the in-sample performance. For an example, using PAC learning, to estimate a model with 10 parameters at an approximation error within 10% we need to apply 2,500 daily observations!

      It is no coincidence that ARP product suffered a major blow once market conditions changed. As we speak, post October 2018, quants are facing a crisis of confidence.

      In the hindsight, year 2018 brought to the failure the two very popular strategies:

      1) The short volatility ETNs: the figure at the top of the post illustrates how would a naive 5-parameter regression fit the in-sample data of past two years with the accuracy of 98%, but the fitted model fails miserably in February 2018 (I posted a detailed statistical analysis of the crash).

      2) The alternative risk-premia products: the figure below shows the risk-profile of Bank Systematic Risk Premia Multi-Asset Index compiled by the Hedge Fund Research.

      In the figure below, as the predictor, I use the quarterly returns on the S&P 500 index which I condition into the three regimes: bear (16% of the sample), normal (68%), and bull (16%). Then I consider the quarterly returns on the HFR index conditional on these regimes and illustrate the corresponding regression of returns on the HFR index predicted by returns on the S&P 500 index.

      It is clear that the HFR index sells 3 puts to buy 5 calls to obtain the leveraged exposure to the S&P 500 index. Well, over the past decade these models learned to leverage the upside at the cost of selling the downside.

      BankRiskPremia.png

      The key message from my talk is that, we may be able to avoid the traps of applying machine and statistical learning methods for systematic trading strategies by understanding the theoretical grounds of the ML methods and the potential limitations of using only limited sample sizes for the estimation of these models.

       

      Disclaimer

      All statements in this presentation are the author personal views. The information and opinions contained herein have been compiled or arrived at in good faith based upon information obtained from sources believed to be reliable. However, such information has not been independently verified and no guarantee, representation or warranty, express or implied, is made as to its accuracy, completeness or correctness. Investments in Alternative Investment Strategies are suitable only for sophisticated investors who fully understand and are willing to assume the risks involved. Alternative Investments by their nature involve a substantial degree of risk and performance may be volatile.

       

       

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      Posted in Quantitative Strategies, Uncategorized, Volatility Modeling, Volatility Trading | 2 Comments
    • Machine Learning for Volatility Trading

      Posted at 6:33 am by artursepp, on May 29, 2018

      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.

      Continue reading →

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      Posted in Asset Allocation, Quantitative Strategies, Uncategorized, Volatility Modeling, Volatility Trading | 2 Comments
    • Lessons from the crash of short volatility ETPs

      Posted at 6:50 am by artursepp, on February 15, 2018

      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.

      Key takeaways

      • Exchange traded products (ETPs) for investing in volatility may not be appropriate for retail investors because, to deliver the lasting performance in the long-term, these products need risk controls and dynamic rebalancing to avoid steep drawdowns and to optimise the carry costs from the VIX futures curve.
      • The convexity of VIX changes and the sensitivity of changes in the VIX futures to changes in the S&P 500 index is extremely high in regimes with low and moderate levels of the implied volatility. As a result, a margin call on short volatility ETPs is more likely to occur in periods with low to medium volatility rather than in periods with high volatility.
      • Without proper risk-control on the notional exposure, ETPs with the short VIX exposure are too sensitive to the intraday margin calls on a very short notice. Empirically, in the regimes with medium volatility, an intraday decline of 7% in the S&P 500 index is expected lead to 80-100% spike in the VIX futures and, as a result, to margin calls for short volatility ETPs.
      • Short volatility ETNs provide with a leveraged beta exposure to the performance of the S&P 500 index, there is no alpha in these strategies. This leveraged exposure can be replicated using either S&P 500 index with leverage of 4.2 to 1 or with investment grade bonds with leverage of 9.6 to 1. All these strategies perform similarly well in a bull market accompanied by a small realized volatility and significant roll yields, yet these leveraged strategies are subject to a margin call on daily basis.

      Continue reading →

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      Posted in Quantitative Strategies, Uncategorized, Volatility Modeling, Volatility Trading | 7 Comments
    • Diversifying Cyclicality Risk of Quantitative Investment Strategies: presentation slides and webinar Q&A

      Posted at 5:21 pm by artursepp, on December 1, 2017

      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.

      Keep on Reading!

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      Posted in Asset Allocation, Quantitative Strategies, Trend-following, Uncategorized, Volatility Modeling, Volatility Trading | 1 Comment
    • Volatility Modelling and Trading: Workshop presentation

      Posted at 5:13 pm by artursepp, 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.

       

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      Posted in Quantitative Strategies, Uncategorized, Volatility Modeling, Volatility Trading | 0 Comments
    • Allocation to systematic volatility strategies using VIX futures, S&P 500 index puts, and delta-hedged long-short strategies

      Posted at 3:45 pm by artursepp, 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%.

      Keep on Reading!

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      Posted in Asset Allocation, Quantitative Strategies, Uncategorized, Volatility Modeling, Volatility Trading | 4 Comments
    • Why the volatility is log-normal and how to apply the log-normal stochastic volatility model in practice

      Posted at 3:23 pm by artursepp, 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!

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      Posted in Uncategorized, Volatility Modeling, Volatility Trading | 0 Comments
    • Volatility Modeling and Trading: Q&A with Euan Sinclair

      Posted at 4:18 pm by artursepp, 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.

      Keep on Reading!

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      Posted in Quantitative Strategies, Uncategorized, Volatility Modeling, Volatility Trading | 0 Comments
    • How to optimize volatility trading and delta-hedging strategies under the discrete hedging with transaction costs

      Posted at 3:37 pm by artursepp, on May 1, 2017

      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.

      Keep on Reading!

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      Posted in Quantitative Strategies, Uncategorized, Volatility Modeling, Volatility Trading | 1 Comment
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    • Recent Posts

      • Tail risk of systematic investment strategies and risk-premia alpha
      • Trend-Following CTAs vs Alternative Risk-Premia (ARP) products: crisis beta vs risk-premia alpha
      • My talk on Machine Learning in Finance: why Alternative Risk Premia (ARP) products failed
      • Why Python for quantitative trading?
      • Machine Learning for Volatility Trading
      • Trend-following strategies for tail-risk hedging and alpha generation
      • Lessons from the crash of short volatility ETPs
      • Diversifying Cyclicality Risk of Quantitative Investment Strategies: presentation slides and webinar Q&A
      • Volatility Modelling and Trading: Workshop presentation
      • Allocation to systematic volatility strategies using VIX futures, S&P 500 index puts, and delta-hedged long-short strategies
      • Why the volatility is log-normal and how to apply the log-normal stochastic volatility model in practice
      • Volatility Modeling and Trading: Q&A with Euan Sinclair
      • Quantitative Approaches to Wealth Management: An Interview for Instututional Investor Journals
      • How to optimize volatility trading and delta-hedging strategies under the discrete hedging with transaction costs
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