Artur Sepp Blog on Quantitative Investment Strategies

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    • Developing systematic smart beta strategies for crypto assets – QuantMinds Presentation

      Posted at 3:09 pm by artursepp, on February 23, 2022

      I am delighted to share the video from my QuantMinds presentation that I made in Barcelona in December 2021. Many thanks to QuantMinds organizers for allowing me to share this video. First, it was nice to attend the onsite conference in a while and to meet old friends and colleagues. I was positively surprised by how many people attended. Many thanks to organizers for making it happen during these uncertain times!

      I presented a framework for the design of sector-based smart beta indices and products for diversified investing to crypto assets. There are thee challenges to account for when designing a systematic strategy on crypto assets.

      First, the data quality is poor indeed. We need to tackle the enormous challenge to accommodate and filter data from multiple data providers. Unlike the traditional asset classes, the market data for public data (such as market cap and traded volumes) can be a source of alpha for systematic strategies.

      Second, the time history of data is very short. For example, most of protocol tokens for Decentralized Finance (DeFi) applications were listed during the second half of 2020, which means that we have to ascertain the design and risk-reward profile of a strategy using one year of data.

      Third, the liquidity of crypto assets may be insufficient when contrasted with traditional assets. Therefore, we need to carefully design strategies by screening and incorporating the liquidity into the process. One of the challenges is that most crypto exchanges (there are about 30 tier one exchanges) tend to over-estimate their traded volumes.

      To overcome these challenges, I constructed a bootstrapping simulation engine which allows to generate joint paths of price and fundamental data for the empirical distributions without breaking the correlation and auto-correlation structure of dependencies in the data.

       

       

       

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      Posted in Asset Allocation, Crypto, Decentralized Finance, Quantitative Strategies | 2 Comments
    • Toward an efficient hybrid method for pricing barrier options on assets with stochastic volatility – research paper

      Posted at 2:00 pm by artursepp, on February 23, 2022

      I am excited to share the latest paper with Prof. Alexander Lipton.

      https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4035813

      We find the semi-analytical solution to one of the unsolved problems in Quantitative Finance, which is to compute survival probabilities and barrier option values for two-dimensional correlated dynamics of stock returns and stochastic volatility of returns.

      An analytical solution to such a problem does not appear feasible because the valuation equation is asymmetric in the log-price variable when the correlation between returns and the volatility of returns is non-zero. In the case of zero correlation, an analytic closed-form solution is achievable involving a numerical integration in the Fourier space.

      In this article, we combine one-dimensional Monte Carlo simulations and the semi-analytical one-dimensional heat potential method (MHP) to design an efficient technique for pricing barrier options on assets with correlated stochastic volatility. Our approach to barrier options valuation utilizes two loops. First, we run the outer loop by generating volatility paths via the Monte Carlo method. Second, we condition the price dynamics on a given volatility path and apply the method of heat potentials to solve the conditional problem in closed-form in the inner loop. Next, we illustrate the accuracy and efficacy of our semi-analytical approach by comparing it with the two-dimensional Monte Carlo simulation and a hybrid method, which combines the finite-difference technique for the inner loop and the Monte Carlo simulation for the outer loop. Finally, we apply our method to compute state probabilities (Green function), survival probabilities, and the value of call options with barriers.

      As a byproduct of our analysis, we generalize Willard’s (1997) conditioning formula for valuation of path-independent options to path-dependent options. Additionally, we derive a novel expression for the joint probability density for the value of drifted Brownian motion and its running minimum or maximum in the case of time-dependent drift.

      Our approach provides better accuracy and is orders of magnitude faster than the existing methods. The methodology is general and can equally efficiently manage all known stochastic volatility models. Besides, relatively simple extensions (will be described elsewhere) can also handle rough volatility models. With minimal changes, one can use the method to price popular double-no-touch options and other similar instruments.

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      Posted in Quantitative Strategies, Volatility Modeling | 0 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
    • Trend-following strategies for tail-risk hedging and alpha generation

      Posted at 11:39 am by artursepp, on April 24, 2018

      Because of the adaptive nature of position sizing, trend-following strategies can generate the positive skewness of their returns, when infrequent large gains compensate overall for frequent small losses. Further, trend-followers can produce the positive convexity of their returns with respect to stock market indices, when large gains are realized during either very bearish or very bullish markets. The positive convexity along with the overall positive performance make trend-following strategies viable diversifiers and alpha generators for both long-only portfolios and alternatives investments.

      I provide a practical analysis of how the skewness and convexity profiles of trend-followers depend on the trend smoothing parameter differentiating between slow-paced and fast-paced trend-followers. I show how the returns measurement frequency affects the realized convexity of the trend-followers. Finally, I discuss an interesting connection between trend-following and stock momentum strategies and illustrate the benefits of allocation to trend-followers within alternatives portfolio.

      Interested readers can download the pdf of my paper Trend following strategies for tail-risk hedging and alpha generation or access the paper through SSRN web

      Key takeaway

      1. Risk-profile of quant strategies

      The skewness and the convexity of strategy returns with respect to the benchmark are the key metrics to assess the risk-profile of quant strategies. Strategies with the significant positive skewness and convexity are expected to generate large gains during market stress periods and, as a result, convex strategies can serve as robust diversifiers. Using benchmark Eurekahedge indices on major hedge fund strategies, I show the following.

        • While long volatility hedge funds produce the positive skewness, they do not produce the positive convexity.
        • Tail risk hedge funds can generate significant skewness and convexity, however at the expense of strongly negative overall performance.
        • Trend-following CTAs can produce significant positive convexity similar to the tail risk funds and yet trend-followers can produce positive overall performance delivering alpha over long horizons.
        • On the other spectrum, short volatility funds exibit significant negative convexity in tail events.

      Fig2HFconv

      HFSkew

      Continue reading →

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      Posted in Asset Allocation, Quantitative Strategies, Trend-following, Uncategorized | 1 Comment
    • 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.

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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%.

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      Posted in Asset Allocation, Quantitative Strategies, Uncategorized, Volatility Modeling, Volatility Trading | 4 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.

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      Posted in Quantitative Strategies, Uncategorized, Volatility Modeling, Volatility Trading | 0 Comments
    • Quantitative Approaches to Wealth Management: An Interview for Instututional Investor Journals

      Posted at 3:51 pm by artursepp, 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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      Posted in Asset Allocation, Quantitative Strategies, Uncategorized | 0 Comments
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    • Recent Posts

      • Developing systematic smart beta strategies for crypto assets – QuantMinds Presentation
      • Toward an efficient hybrid method for pricing barrier options on assets with stochastic volatility – research paper
      • Professional update: systematic solutions for crypto and digital assets at Sygnum Bank’s Asset Management
      • Paper on Automated Market Making for DeFi: arbitrage-fee exchange between on-chain and traditional markets
      • 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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