Category: Uncategorized

• Tail risk of systematic investment strategies and risk-premia alpha

  Posted at 2:55 pm by artursepp, on April 9, 2019

  

  Everyone knows that the risk profile of systematic strategies can change considerably when equity markets turn down and volatilities spike. For an example, a smooth profile of a short volatility delta-hedged strategy in normal regimes becomes highly volatile and correlated to equity markets in stressed regimes.

  Is there a way to systematically measure the tail risk of investment products including hedge funds and alternative risk premia strategies? Further, how do we measure the risk-premia compensation after attribution for tail risks? Finally, would we discover patterns in cross-sectional analysis of different hedge fund strategies?

  I have been working through years on a quantitative framework to analyse the above raised questions and recently I wrote two articles on the topic:

  1. The regime-conditional regression model is introduced in The Hedge Fund Journal (online paper or PDF on SSRN).
  2. A short review of the methodology and results is presented for QuantMinds

  I would like to highlight the key results of the methodology so that interested readers can further follow-up with the original sources.

  Regime conditional index betas

  In the top Figure, I show the regime conditional betas for a selection of hedge fund style from HFR indices data using the S&P 500 index as the equity benchmark.

  We can classify the strategies into defensive and risk-seeking based on their return profile in bear market regimes:

  1. Defensive strategies (long volatility, short bias, trend-following CTAs) have negative equity betas in bear regime so that these strategies serve as diversifiers of the equity downside risk.
  2. Risk-seeking strategies (short volatility, risk-parity) have positive and significant equity betas in bear regime. Equity betas of most of risk-seeking strategies are relatively small in normal and bull periods but equity betas increase significantly in bear regimes. I term these strategies as Risk-seeking risk-premia strategies.
  3. I term strategies with insignificant betas in normal bear regimes as Diversifying strategies. Examples include equity market neutral and discretionary macro strategies because, even though these strategies have positive betas to the downside, the beta profile does not change significantly between normal and bear regimes. As a result, the marginal increase in beta exposure between normal and bear periods is insignificant.

  Risk-premia alpha vs marginal bear beta

  I define the risk-premia alpha as the intercept of the regime-conditional regression model for strategy returns regressed by returns on the benchmark index. To show a strong relationship between the risk-premia alpha and marginal bear beta (the marginal bear betas are computed as the difference between betas in normal and bear regimes), I apply the cross-sectional analysis of risk premia for the following sample of hedge fund indices and alternative risk premia (ARP) products, using quarterly returns from 2000 to 2018 against the S&P 500 total return index:

  1. HF: Hedge fund indices from major index providers including HFR, SG, BarclayHedge, Eurekahedge with the total of 73 composite hedge fund indices excluding CTA indices;
  2. CTA: 7 CTA indices from the above providers and 15 CTA funds specialized on the trend-following;
  3. Vol: 28 CBOE benchmark indices for option and volatility based strategies;
  4. ARP: ARP indices using HFR Bank Systematic Risk-premia Indices with a total of 38 indices.

  In figure below, I plot risk-premia alphas against marginal bear betas grouped by strategy styles. For defensive strategies, their marginal bear betas are negative; for risk-seeking strategies, the marginal bear betas are positive and statistically significant.

  

  We see the following interesting conclusions.

  1. For volatility strategies, the cross-sectional regression has the strongest explanatory power of 90%. Because a rational investor should require a higher compensation to take the equity tail risk, we observe such a clear linear relationship between the marginal tail risk and the risk-premia alpha. Defensive volatility strategies that buy downside protection have negative marginal betas at the expense of negative risk-premia alpha.
  2. For alternative risk premia products, the dispersion is higher (most of these indices originate from 2007), yet we still observe the pattern between the defensive short and risk-seeking risk-premia strategies with negative and positive risk-premia alpha, respectively.
  3. For hedge fund indices, the dispersion of their marginal bear beta is smaller. As a result, most hedge funds serve as diversifiers of the equity risk in normal and bear periods; typical hedge fund strategies are not designed to diversify the equity tail risk.
  4. All CTA funds and indices have negative bear betas with insignificant risk-premia alpha. Even though their risk-premia alpha is negative and somewhat proportional to marginal bear beta is proportional, the risk-premia alpha is not statistically significant. In this sense, CTAs represent defensive active strategies. The contributors to slightly negative risk-premia alpha may include transaction costs and management fees.

  Endnotes

  All figures are produced using seaborn data visualization package in Python.

  References

  Sepp A., Dezeraud L., (2019), “Trend-Following CTAs vs Alternative Risk-Premia: Crisis beta vs risk-premia alpha”, The Hedge Fund Journal, Issue 138, page 20-31, https://thehedgefundjournal.com/trend-following-ctas-vs-alternative-risk-premia/, https://ssrn.com/abstract=3368932

  Sepp, A. The convexity profile of systematic strategies and diversification benefits of trend-following strategies, QuantMinds, April 2019

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  Posted in Asset Allocation, Quantitative Strategies, Trend-following, Uncategorized, Volatility Modeling | 1 Comment

• Trend-Following CTAs vs Alternative Risk-Premia (ARP) products: crisis beta vs risk-premia alpha

  Posted at 3:00 pm by artursepp, on February 5, 2019

  

  Year 2018 was eye-opener for investors in alternative risk-premia products. A lot of these products have been sold as market-neutral but they did not live up to expectations… I think, the reason is simple: most of ARP products have been driven by marketing with nice looking back-tested results obtained by over-fitted models. I made a presentation on this topic back in early November 2018. Yet, traditional alternatives had a bad year too.

  We published an article in the Hedge Fund Journal to explain the difference between traditional trend-following CTAs and Alternative Risk Premia. Here I will post the introduction and the key insight from our model to define the risk-premia alpha.

   

  Introduction

  The turbulence of 2018 made it a difficult year for most systematic investment products. To the surprise of several investors, many of these quant products had been sold as market neutral. In particular, the new breed of alternative risk-premia (ARP) products – that had flooded the market a few years prior to 2018 – performed exceptionally badly. For example, the composite HFR Bank Systematic Risk-premia Multi-Asset Index lost -18%, in comparison with a loss of -4% on the S&P 500 total return index. However, traditional alternative asset classes also underperformed, with the flagship HFRX Global Hedge Fund Index losing -7% and the SG Trend Index losing -8%.

  In the face of such losses, both investors and managers are asking how and why so many quant strategies underperformed? Still more importantly, what are the implications for the diversification of traditional equity-bond portfolios and alternative investments? In particular, since trend-following CTAs belong to a handful of tried-and-tested diversifiers, why did trend-followers not diversify in 2018?

  To address such questions, we first intend to look at how trend-following programs are expected to perform when crises last for extended periods of at least two months, because trend-followers need to adjust to profit from sustained crises in equity markets. Second, we shall focus on the way in which the risk profile of ARP products, hedge funds, and trend-following CTAs can change in bear and bull market regimes because of their potential exposures to tail-risk. We analyse the risk-premia alpha in these products by taking into account regime-conditional risk.

  For this analysis, we are proposing a new quantitative model to explain the risk of investment strategies by accounting for extreme market conditions and for their exposure to tail risk, such as selling volatility and credit protection. We apply this model to the cross-sectional risk attribution of about 200 composite indices of hedge funds and ARP products. We show that there is a strong linear relationship between risk-premia alpha and the tail risk of systematic ARP strategies. We can demonstrate that our model explains nearly 90% of the risk-premia for volatility strategies and about 35% of the risk-premia for hedge fund and ARP products. In this way, most ARP and hedge fund type products can be seen as risk-seeking strategies. Importantly, our model predicts that ARP products offer smaller risk-premia compensation compared to hedge funds.

  We are able to illustrate that, interestingly, trend-following CTAs are exceptions since they belong to defensive strategies with negative market betas in bear regimes, yet risk-premia alphas for CTAs are insignificant. CTAs cannot be seen either as ARP products with positive risk-premia alpha from exposures to tail risk, or as defensive products with negative risk-premia designed to reduce tail risk, such as long volatility strategies. Instead, trend-following CTAs should be viewed as an actively managed defensive strategy with the goal to deliver protective negative market betas in strongly downside markets along with risk-seeking positive market betas in strongly upside markets. Overall, after adjusting for the downside and upside betas, the risk-premia alpha of CTAs is insignificant. Yet, because of the negative protective betas in bear markets, trend-followers well deserve their place as diversifiers in alternative portfolios to improve risk-adjusted performance and capture risk-premia alpha on a portfolio level, as we will show in the last section.

  Finally, since our risk-attribution model assumes conditional equity betas in specific market regimes, we are able to illustrate the misunderstanding behind strategies claiming to be “zero-correlated” and “market-neutral”. Given a specific market regime, most typically in the bear regime, many risk-premia strategies tend to produce a strong exposure to equity markets because of their hidden tail exposures. For example, a strategy selling delta-hedged put options would have a small market beta during normal regime; yet the strategy would exhibit a significant market beta during crisis periods because of its negative gamma and vega exposures. When we analyse systematic strategies unconditional to market regimes, the performance may appear to be smooth and uncorrelated because of the aggregation across different regimes.

  We will conclude the introductory section and our article by answering the above questions in the following way. Firstly, ARP strategies are expected to perform well during normal regimes. However, since the excess performance of these strategies is derived from a hidden tail risk, these strategies are expected to underperform during turbulent markets, as in 2018. To earn risk-adjusted alpha from these products, investors need to look at long time horizons that include both bull and bear markets. Second, while the performance of trend-following CTAs is not derived from risk-premia alpha as compensation for hidden tail risks, the performance of trend-followers is conditional on trends lasting for sustained periods. Since trends reversed rapidly multiple times during 2018, trend-followers underperformed. As a result, in what proved to be an extraordinary year, both ARP products and trend-followers underperformed, but for different reasons.

  Going forward, investors and allocators need to understand how different strategies are expected to perform during bear and normal markets and how to diversify their portfolios accordingly. Our results provide a valuable aid in quantifying the hidden tail behaviour of systematic strategies as well as suggesting an approach for the risk attribution and diversification of alternative portfolios.

  Risk-premia Alpha

  Risk-premia alpha measures the excess return on a strategy after adjusting for conditional beta exposures. According to the regime conditional CAPM, a strategy should produce higher risk-premia alpha if it assumes higher equity risk in a bear market measured by marginal bear market betas.

  The figure in the top illustrates different risk profiles of hedge fund and ARP products. We apply the regime conditional model to a large universe of indices grouped into three categories:

  1. Hedge fund indices from major index providers including HFR, SG, BarclayHedge, Eurekahedge with the total of 73 composite hedge fund indices excluding CTA indices;
  2. 7 CTA indices from the above providers; and
  3. ARP indices using HFR Bank Systematic Risk-premia Indices with a total of 38 indices.

  According to our model we see a clrear differentiation among risk-seeking strategies, defensive strategies and trend-following CTAs.

  Risk-seeking strategies: the marginal bear beta is positive (increased risk in bear regime) compensated by positive risk-premia alpha. Most hedge fund and ARP products are risk-seeking strategies with tail risk. We observe almost a linear relationship between risk-premia alpha and marginal bear betas for the cross-section of hedge funds and ARP indices. ARP products deliver less risk-premia alpha for the same level of tail risk compared to hedge funds.

  Defensive strategies: the marginal bear beta is negative (reduced risk in bear regime) compensated by negative risk-premia alpha. Defensive strategies diversify equity risk in bear regimes but deliver negative risk-premia alpha.

  Trend-following CTAs: produce negative marginal bear market betas and hence strongly diversify equity risk in bear market regimes. Because their risk-premia alpha is flat, trend-followers can be considered as an anomaly, or as a differentiation from ARP and traditional hedge fund products.

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  Posted in Uncategorized | 1 Comment

• 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.

  

  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

• Why Python for quantitative trading?

  Posted at 12:41 pm by artursepp, on October 24, 2018

  

  “Today a new language is overtaking French as the most popular language taught in primary school. Its name is Python… 6 out of 10 parents want their kids to learn Python”, Joel Clark.

  Well, when I attended school, I learnt BASIC… But I must confess, I do share the excitement taking over the Python language.

  I have recently taken part in a webinar organised by Risk.net and Fincad where we discussed the advantages and challenges in using Python for developing quantitative trading applications. The panel included experts from various corners of the industry including myself and:

  1. Joel Clark, contributing editor, Risk.net (Moderator)
  2. Gary Collier, CTO, Man Group Alpha Technology
  3. Per Eriksson, senior executive, enterprise risk and valuation solutions, FINCAD
  4. Ronnie Shah, head of US quantitative research and quantitative investment solutions, Deutsche Bank

  The webinar was a success with over 500 participants. Since Python is on everyone’s mind, I wanted to highlight some interesting questions and thoughts from our discussion. The audio of the webinar is available here

   

  Why Python has become an increasingly popular programming language in financial markets?

  One of the major advantages of using Python is the ease to interconnect different systems with data feeds and databases, to process data, and to output results into user and trading applications.

  My first experience with Python came in 2012, when Bank of America Merrill Lynch, where I worked as a front office quant strategist, introduced the Quartz system developed in Python. The Quartz was supposed to be the bank-wide solution to share data and trading risks. The reason is that the insufficient centralization and aggregation of positions and risks across all trading books (traditionally differentiated by geographies and asset classes) was one of the key weaknesses shared by large investment banks during and in the aftermath of the 2008 financial crisis.  As a result, the Quartz and Python-based analytics were thought as a bridge to connect different parts of analytics, data centres, and development teams. A daunting task for any large organization employing hundreds of developers and users!

  Moving fast forward, Python has been widely applied by major financial institutions for developing tools to connect different parts of analytics and to increase collaboration within a firm. Over time, people have also started to do more core development in Python in addition to using Python as a glue language.

  New developments using the Python language have been leveraged thanks to a rich Python ecosystem with huge number of libraries for data analytics and visualization. For an example, Man AHL illustrated how they benefited by moving both research and production code to Python.

  Summarising their paper and our panel, Python has become increasingly popular because:

  1. Python enhances the communication between different teams.
  2. Python provides an advanced ecosystem with packages for numerical and statistical analysis, data handling and visualization.
  3. Python is easy to learn and it is flexible to apply, and it’s actually fun to program using the Python language. As a person with many years of doing quantitative modelling in C++ and Matlab, I fully support this view.

   

  How Python works among other languages for data analysis?

  Since data analytics is currently one of the key drivers across all industries including the finance and investment management, choosing the right ecosystem for development may have a crucial impact on the business development and success.

  Presently, the three development tools are widely applied for the data analytics.

  1. Python along with pandas for tabular data structures and multiple packages for data analysis (statsmodels for statistical analysis, matplotlib for data visualization, scikit-learn for machine learning, etc). The advantage is that Python provides a free and open-source solution with plentiful resources for data fetching, processing, and visualization. Python can be easily deployed on either a PC or a server to make scalable firm-wide solutions.
  2. Traditionally, Matlab has been widely applied in academic and research labs but it comes with a heavy cost for commercial firms. Matlab has numerous packages for data processing, analysis, and visualization, however each package is available at a separate price. Personally, I have used Matlab a lot along with its capabilities for the object-oriented programming. While I value some capabilities of Matlab, the major drawback of Matlab, apart from its licensing cost, is that the deployment of Matlab-based analytics is problematic and comes with separate fees. Matlab applications can be compiled and deployed on a server but the deployment process looks complex and not well documented and it may be costly if external consultancy is needed. In my opinion, the insufficient portability and scalability are major obstacles for developing firm-wide solutions using Matlab.
  3. R along with its multiple packages for statistical data analytics. While R is free and it has many packages to do various statistical analyses, the deployment of R across firm-wide platform may not be as efficient. In my opinion, the R language is suitable only for the development of stand-alone tools for statistical analyses. In fact, Jupiter Lab enables to apply R functionality within the Python ecosystem.

   

  How long would it take to convert Matlab production code to Python?

  Given the advantages of Python over Matlab, most firms would now employ Python to start any new development from scratch. How is about converting the legacy code and systems?

  Gary Collier gave one example of AHL converting a fairly complicated trading system for single stock equities to Python within 8-9 months.

  In fact, my friend Saeed Amen has just written a short overview paper on moving from Matlab to Python. The transition is feasible… While there will be short-term costs, the long-term benefit is to have a firm-wide solution developed in one multi-purpose language that everyone can understand and contribute to.

   

  Python everywhere?

  To conclude, the top figure shows the share of questions about various programming languages asked each month at Stack Overflow, which is the largest online community for developers. We clearly see the growing trend for Python against all other major programming languages. Perhaps soon enough the Python will overtake all other languages taught not only in primary school but e,plyed everywhere else…

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  Posted in Python, Uncategorized | 1 Comment

• 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.

  

  

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  Posted in Asset Allocation, Quantitative Strategies, Trend-following, Uncategorized | 0 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 5^th 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

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