Flirting with Models: Benn Eifert

Link: https://blog.thinknewfound.com/podcast/s2e2-benn-eifert/

About Benn: Founder of QVR Advisors specializing in option-based strategies


Selected notes from his conversation with Corey Hoffstein, co-founder of quant management firm Newfound Research

Can you maybe explain the difference between what you would consider to be more of an option strategy versus what actual volatility investing is?

  • A common option strategy is call overwriting or put writing. They are both long equity exposures. That exposure is going to be the biggest risk factor. I would contrast that with volatility strategy which tries to isolate features of the distribution of returns, but not the direction of returns.

You mentioned that you guys focus somewhat heavily on relative value strategies in the volatility space. Can you explain what constitutes a relative value strategy? What’s a trade that you might put on?

  • Create value buying cheap exposures and selling expensive exposures at the same time, trying to hedge out the main directional market risks that would dominate a traditional asset allocation.
  • Identifying trades is really an important part of the process as they move around all the time over different cycle frequencies. Imagine, for example, long volatility in large-cap energy companies versus short volatility of smaller energy companies. That might be an opportunity at a point in time driven by a series of large transactions in the equity market. A large fund may have done a bunch of overwriting in their large-cap energy names, which suddenly made them very cheap. You really have to monitor, see the prices move and understand why there are dislocations and other relative value opportunities that might cycle over time.
  • Pension fund overwriting or cash-secured put selling are types of opportunities that might last for several years while those strategies are popular. You can imagine them becoming too popular over some number of years, then the pendulum swinging the other way. I wouldn’t say that there’s risk premia that you would expect in the space to just exist in perpetuity in a relative value sense.
  • There’s a large need to provide liquidity for end-users of options and distribute risk from where options are being heavily supplying to where they’re heavily demanded. These markets really developed on the back of end-user demand and their need to transfer risk. The key thing in relative value investing and in volatility, is that the marginal price setter for the probabilities and the market prices that prevail is not a volatility investor who is thinking about nuances in implied volatility. For a specialist volatility investor, many of the best opportunities really arise from either explicitly or implicitly providing liquidity to meet the needs of end-users, and to warehouse basis risk between what they’re buying and what they’re selling.

Analogs and Differences from traditional investing?

  • Selling vol and overwriting are expressions of carry styles
  • Rather than using traditional factor language to describe volatility trading he prefers a “Star Wars” analogy: Derivative users do things in big herds. And they typically have very large size relative to the absolute return community. Those flows are very sticky and implemented in similar ways with similar benchmarks, for example selling one-month index options. This creates congestion in one segment of the overall options market creating a ‘disturbance in the force’ — this creates really steep term structures, market makers get stuffed with short term options, they don’t have the risk limits to hold. And the relative value community’s job is really to distribute that risk much more broadly, throughout the ecosystem. A nice risk-reward profile is the payment to provide the liquidity to that market. Concentrated flows need someone on the other side to warehouse and distribute that basis risk.

How do you think about identifying trades in this space? How do you think about managing trades? How do you think about exiting trades? How does the book come together? It seems like a very overwhelming landscape to try to get your arms around.

  • Our investment process as a collection of bottoms-up strategy sleeves. So an individual strategy sleeve would really be a theme that’s driven by some particular type of dislocation or some particular type of underlying flow that end-users are generating. In a cross-sectional portfolio, opportunities are more fleeting, as opposed to being structural flows that are very consistent over long periods of time. It involves building out quite a lot of infrastructure, to identify those opportunities quickly.
  • In the example of a fund running a big overwrite sale on their long, large-cap, equity names portfolio, that would feed through quickly into the prices of options within that universe, and you’d see a significant reduction in those prices, relative to the prices of the small-cap energy names. You’d see it probably wasn’t driven by underlying realized volatility dynamics, it wasn’t that the spread compressed(because the names in the short baskets started becoming very volatile, and their prices started rising). You’d have various other ways of quantitatively triangulating that which would trigger an investigation into a type of the trade to add to the portfolio.
  • Where the dislocations are potentially more persistent, it might be more a question of measuring those dislocations. How do you track the ebb and flow over time? Is it a particularly attractive opportunity set? Do you want to have maximum risk on? Is it a less attractive opportunity set? Do you only want to have 30, or 40% of risk on? The identification of those type of opportunities is a starting point in the design of the strategies.
  • Again much is driven by what end-users of derivatives are doing in really big size and affecting markets. It’s not hard to see if you are an active market participant. You spend a lot of time talking to market makers and talking to the end-users of derivatives so you see it very quickly.

How do you think about the trade off between systematic versus discretionary and volatility investing?

  • I think in terms of a spectrum between, on the one hand, fully discretionary, and gut feel based investing all the way to the other end of the spectrum of fully automated back to front, systematic trading. Most volatility managers lie somewhere in between on that spectrum. It’s really hard to get that last mile to full automation. Since options are non-linear, you need to manage the very small risk of automation failures which also makes full automation elusive.

Are there any examples that come to mind where either an opportunity was systematically identified and you had a discretionary override? How about the opposite, where you thought there was an opportunity and the systems were not flagging it?

  • Back in the early days of Abenomics, in Japan, when the Nikkei was incredibly depressed, there was an interesting dynamic showing up in skew on Japanese equity indices. So skew is the relative price and an implied volatility sense of upside, call options versus downside put options. And in Japan, it actually started to go positive, which is very unusual. In other words, upside call options, were trading at a higher implied volatility than downside put options. A lot of folks in the volatility community got really excited about how silly it was, that an upside call option would trade at a higher implied probability than a downside put option, and really aggressively sold upside call options. But the key thing to remember back then was the Japanese equity market had just been incredibly depressed for a long time. There was a tremendous macro narrative building around big structural reforms and a great unconventional monetary policy. What followed was a very volatile rally! It was really a sucker’s trap to look at skew based on the historical data set because you were selling an upside crash scenario.
  • Another example was the model not appreciating how cheap the options on VIX were when the sizing in XIV became extreme and creating a very negatively convex profile in VIX due to the size of the rebalance. If you have a fund that requires a mechanical response that has to buy volatility when vol is up, it creates a problem if the size in the market became too large. It was just a market microstructure time bomb waiting to happen. The timing of that type of event happening was uncertain. But the sizes of those positions made it almost inevitable.

When you see a very steep VIX futures curve, in your opinion, is that an expression of the markets viewpoint? Or do you think that’s just an expression of a market imbalance?

  • Typically, it’s more related to risk premium than it is some kind of unbiased forecast of future volatility. If you look academic research or practitioner research there are some fundamentals to that term structure and some expectation element but often quite a lot of element of risk premium.

If you were doing due diligence, on a volatility strategy, describe red flags (besides leverage and are they selling tail insurance) and other concerns.

  • I would want to drill into sophisticated, top-down risk systems that stress all of the main risk factors in the portfolio to very extreme levels, and see that the risks were acceptable. There is no portfolio that makes money under all circumstances which is fine. But if there’s a major risk factor in the portfolio, you should be able to take it to a very extreme unprecedented level and see that the portfolio is not going to be getting liquidated at that level.
  • It should be contained in a level that’s acceptable to the end investor.
  • I’d want to understand the assumptions they’re making in those stress tests.
  • I would really want to see the actual positions and hear them explain what other parts of the market and what other market participants are doing to understand what the squeeze risk looks like.
  • I would want to see that they had at least contemplated thoughtfully and analytically how the strategy should be expected to perform going forward. And really a thought about how the market changed in the past 20 or 30 years versus right now. Markets in general change over time but volatility and options markets have changed dramatically.

Notes from EconTalk: Anja Shortland

Link: http://www.econtalk.org/anja-shortland-on-kidnap/

About Anja: Researcher and author of  Kidnap: Inside the Ransom Business


Economist Russ Roberts interviews Anja Shortland

Kidnapping for ransom as a business

The hint that kidnapping was in fact a business: 97% are resolved peacefully

How can the chance of a peaceful resolution be so high if all these things must go right:

  • Both sides must negotiate a price from a wide range
  • How to payment, typically unmarked cash, to the kidnapper?
  • Trust that the kidnapper will acknowledge payment
  • The kidnapper to trust they will not be arrested during the hand-off
  • The kidnapper must expect that the hostage will not be a witness

“The only reason for this kind of trade to go smoothly is what economists call the shadow of the future. So, people behave well this time ’round because it will help them in their business in future interactions.”

“This will only work if the kidnapper understands that he’s better off keeping the promises than breaking the promises. And that works because there must be a mechanism for information about good and bad behavior to be transmitted to future victims. So, if you have a kidnapping gang working in a city, then local gossip will probably ensure that people know whether or not they can trust the kidnappers. However, how does that work for transnational hostages? How does it work for the tourist that gets picked up in a bar late at night? How does that work for the aid-worker? How does that work for the expatriate?

Enter kidnap insurance

“There’s a very limited number of insurers, syndicates, underwrite kidnap-for-ransom, and they exchange information about trustworthy kidnappers and rogue kidnappers.”

  • Insurance actually ‘orders the market’, creating moral hazard in the process.
  • Corps buy ‘kidnap for ransom’ insurance with conditions:
    • Insured cannot know about it
    • Corporation provides security
  • In some areas, kidnapping occurs because corp didn’t know who to pay protection money to
  • Lloyd’s of London brokers a market of insurance companies willing to ensure special risks (like a basketball player’s knee)
    • The market settles into a civil equilibrium
    • Small supply. Crisis responders (often ex-special forces) retained by the insurer will have specific experience with a class of kidnapper
    • Insurers share info and more coordinated than the heterogenous kidnappers which keep prices down. However, when gov’t come in splashing the pot it changes the dynamics of the game as it raises the expectations of kidnappers b/c of public pressures and gov’t large resources and because unlike insurers they are in a one-off game (France hopes the next victim is Swiss)
  • Each kidnap market has local conventions
    • Example: Pirates want money dropped in canisters next to the ship so that kidnappers can stay high enough to avoid capture himself
    • Businesses that provide secure common ground for handoffs(almost like escrow!)
    • Trustworthy middlemen — again ‘shadow of the future’; reputations and long-running exchanges (reminds me of my open-outcry trading past. In the pits, your “word was your bond”)
    • While any one transaction can go wrong on average the market hovers around a going price.
    • If kidnappers make mistakes, then they are out of business.
      • “Sometimes you have very emotional kidnappers. Sometimes you have stupid kidnappers. But stupid kidnappers will reveal information. And ultimately it is in the insurer’s interest to eliminate stupid kidnappers–well, eliminate kidnappers where possible. But if you have stupid kidnappers who make mistakes, you can remove them from the market by dropping some hints to the police.”

On the game theory of negotiation

  • Manage kidnapper’s expectation of ransom size (hide the fact that the captive is insured)
  • “Squeezing the towel” process as the concessions offered to the kidnappers turn in to a slow drip
    • Eventually, the concessions are below the kidnappers’ cost to hold the victim. For example, the longer a hostage in custody the more expensive (via bribes) to keep it secret
  • Can’t reward kidnapper’s bad behavior or threats (“parenting lesson”)
  • Negotiators help the kidnappers see things through a more rational perspective. And, they educate them. And say, ‘Yes, we don’t want you to hurt Uncle Ted.’ And, ‘You’re not going to get anything out of hurting Uncle Ted.’ And they just help the kidnappers see how that strategy is not going to be helpful.

Notes from Alpha Exchange: Harley Bassman

Link: https://www.youtube.com/watch?v=X8wioRF0434&t=26s

About Harley: There is but one “Convexity Maven” in the world, a moniker that belongs uniquely to Harley Bassman. A 35-year career in financial markets has left Harley steeped in all things relating to the price of and characteristics of optionality.


Dean Curnutt of Macro Risk Advisors interviews “Convexity Maven” Harley Bassman

  • Is there too much short convexity out there?
    • Not in listed option markets where there’s a clearinghouse and vol is explicit traded and monitored
    • Risk is in the implicit convexity similar to portfolio insurance
  • Bassman on volatility surfaces
    • Term structure reflect flows; SPX has option sellers near term and insurance company buying in the longer term
    • Skew in bond markets has flipped since GFC. Pre-GFC puts were richer than calls as large asset managers hedged their bond exposures buying puts. Since GFC, the market recognizes that low interest rates are more coincident with financial stress which has re-priced the upside higher.
    • Forwards will typically price in line with long term options
    • Structured note issuance has vol-suppressing influence on surfaces
      • Europe has more structured note issuance b/c older more income-demanding demographic (looks more like covered calls)
      • Auto-callables in Asia suppresses downside vol (until roughly 10-15% knockout levels)
  • Bassman on a low interest rate worldWith central banks setting policy rates negative, the market is setting pricing across the curve very low.
    • Germany is -.20% out to 10 years yet have nominal positive growth and breakeven inflation is priced at 90 bps, so an extremely negative real interest rate out 10 years.

    Demographic motivated argument for secular stagnation

    • Negative short term rates are not unprecedented and typically accompany short-term market stress. Insurance premium to secure assets
    • Longer term negative rates are a symptom of market expectations for slower growth due to demographic headwinds.
      • In US boomers are getting older. Japan is further ahead and Europe behind Japan.
      • Declining labor force participation is biggest concern since growth = total hours worked x productivity
      • Labor force participation and yields are correlated over long periods
      • The trend of each decade is bluntly explained by demographics but it’s slow moving and difficult to trade
      • Immigration necessary to balance the ratio of workers to retirees. Immigration very important.
    • Trump is a symptom of low wage growth
      • Bassman believes QE1 was necessary to save economic system but later rounds of stimulus should have been fiscal not monetary. Monetary has caused asset inflation without wage growth. Inflation therefore was uneven and regressive leading to Trump and dissatisfied public
    • MMT
      • It’s coming. 2029 boomers will be fully retired and Republicans will not want to cut spending so there will be no check on Democrats
      • Japan a good example that MMT can work in the short term if you borrow in your own currency. The issue is that MMT will not be restrained even if inflation starts to emerge so is likely bad idea in grand scheme
      • The fallout can take decades but it’s not sustainable to print money at a faster rate than the economy grows

    Trade idea

    • Since bond vol term structure is flat, buy long dated (10 year) vol to hedge against longer term seismic shift while levering coupons on CEFs, MLPs, REITs and/or sell puts in 1 to 3 years bond options since demographics will limit rate upside to 3-4%. Can lever the near dated trades while owning the vol protection. This is a version of long time spread since near-dated levering or outright option selling is all short vol.
    • Outright tail protection too expensive and path dependent to be relied upon