{"id":5265,"date":"2012-05-30T05:53:11","date_gmt":"2012-05-30T09:53:11","guid":{"rendered":"http:\/\/www.jasonapollovoss.local\/?p=5265"},"modified":"2018-09-21T02:04:21","modified_gmt":"2018-09-21T06:04:21","slug":"holes-in-some-of-finances-critical-assumptions-a-dialogue-with-massif-partners-kevin-harney-part-two","status":"publish","type":"post","link":"https:\/\/jasonapollovoss.com\/web\/2012\/05\/30\/holes-in-some-of-finances-critical-assumptions-a-dialogue-with-massif-partners-kevin-harney-part-two\/","title":{"rendered":"Holes in Some of Finance\u2019s Critical Assumptions: A Dialogue with Massif Partners\u2019 Kevin Harney (Part Two)"},"content":{"rendered":"

In part one of \u201cHoles in Some of Finance\u2019s Critical Assumptions,\u201d<\/a> Kevin Harney of the hedge fund Massif Partners discussed some of the market distortions arising from critical finance assumptions like Brownian motion<\/a> and normal distributions used in options pricing. What are some of the effects of these choices? What can be done differently? Here is what Harney said.<\/span><\/p>\n

Jason Voss:<\/strong> If I may interrupt you there for a moment. . . . You just said something, in passing, that is, to me, rather profound, and I want to make sure that I have this right. Are you saying that much of the trading in derivatives is not based on a real, objective price, but rather on prices created by agreed upon models? In other words, the real agreement is in valuation methodology, not an agreement as to value? So if new models were developed that were widely accepted, they might spit out suggested values that differed substantively from the previous ones? Did I get that right?<\/span><\/p>\n

Kevin Harney:<\/strong> That\u2019s rather a strong interpretation of what I said. If you lose money consistently you\u2019ve got to explain why and do so pretty quickly. It\u2019s efficient and self-correcting. And derivatives are zero-sum instruments\u00a0\u2014 someone wins and someone loses (which is not to say that they don\u2019t create value through risk-transfer).<\/span><\/p>\n

Advances in models tend to be shared quite quickly\u00a0\u2014 people move around between organizations and a lot of the intellectual property effectively ends up in the public domain (though I will note a number of successful hedge funds that are much more vigorous in protecting their intellectual property). Thus the core models for \u201cvanilla\u201d derivatives tend to be widely known and used. If someone starts to make money systematically because of a better model, then that tends to become known quite quickly. And since prices are effectively publicly available, you can see if you are diverging from the \u201cmarket\u201d price and address why (it may be that you are correct, but you have to know why). There are lots of smart people involved who have a lot of money at stake. That tends to lead to efficient (correct) pricing. And you\u2019ve seen a large growth in the number of market participants\u00a0\u2014 particularly hedge funds always looking for opportunities. So in some sense, pricing for liquid derivatives must be correct, if only because there\u2019s a market price and lots of educated participants and a natural tendency for arbitrage opportunities to dissipate\u00a0\u2014 like in any liquid market. If you are systematically underpricing, you\u2019ll be picked off. And conversely, you won\u2019t make any money if you are systematically overpricing. And so, in answer to the question of whether those prices are model-driven or market-driven, if a better model appeared that allowed for a systematic profit (relative to prior models), then you can be assured it will be used and, for some period, it will allow the holder to earn a profit on that modeling investment, before it makes its way into the mainstream, and the market price adjusts accordingly. You can see many examples of this over the years\u00a0\u2014 from option pricing when the initial\u00a0Black-Scholes<\/a> model hit the mainstream in the 70s through to the SABR model<\/a>, which became a de facto standard in interest-rate derivative pricing about six years ago.<\/span><\/p>\n

But as liquidity falls off, and the model valuation becomes more sensitive to large moves in underlying variables, then you may have much more disparate pricing and consequently opportunities to take advantage of model mispricing. For example, if you are pricing a very out-of-the-money equity option, then you will get much wider disparities in model prices, depending on how the probability of a large move in the underlying equity price has been modeled (the \u201ctail-event\u201d). And someone with a better model (or even a more accurate assessment) of equity return distributions can make money from those whose models don\u2019t accurately reflect those potential returns, at least until everyone else is doing the same thing.<\/span><\/p>\n

Another \u201cvanilla\u201d model (at least based on this liquidity definition) is MBS pricing<\/a>. And MBS pricing is highly contingent on prepayment models (i.e., mortgage holders refinance when interest rates fall enough to make it worth their while). As you can imagine, an inordinate amount of intellectual energy is devoted to modeling that behavior. Anyone with a better model of prepayments (particularly it it\u2019s tailored to a specific set of mortgages or a geographic region) would be well-placed to make a substantial amount of money.<\/span><\/p>\n

But the lack of market pricing for exotic derivatives \u2014 which I\u2019ve used to define \u201cexotic\u201d \u2014 provides a much more substantial opportunity for things to go wrong. A great opportunity is to find a flaw in a model that doesn\u2019t take into account some market nuance \u2014 for example, the fact that volatility might be expected to spike around certain dates because of the release of certain government data. Now, obviously, as with the earlier arguments, through time that opportunity inevitably disappears. But, because of the complexity of the instrument (and its underlying model), there are ample opportunities to exploit real-world factors that cannot be incorporated into a valuation model. Of course traders are meant to supplant the model and appropriately price the risk in the model itself, but it\u2019s a battle of wits.<\/span><\/p>\n

And, with a nascent market, the core model may just have been wrong to start with. And you charge a large premium for that risk. But it may not have been large enough.<\/span><\/p>\n

An example that\u2019s of specific interest to me is longevity (being a specific risk for pension plan sponsors). The market for longevity swaps in Europe is growing and is something that we\u2019ll see in the United States shortly (it\u2019s a bigger risk for European pension plans because they tend to provide inflation-linked benefits upon retirement unlike in the US and thus the longevity risk is amplified). This is a highly-illiquid market with few providers and very large premiums. And thus pricing is highly dependent on modeling mortality for a group of pension beneficiaries (or a proxy that may be based on a national population). On the one hand, you will find people predicting that growing obesity will have a very large impact on mortality, and on the other, those who predict that advances in medicine will have a large opposite impact. It\u2019s unlikely they\u2019re both going to be right. Or maybe they will be and it will net out in the end. This is one of the most extreme examples \u2014 it will take decades to determine if the models were accurate. But that\u2019s not going to stop a market from developing.<\/span><\/p>\n

Let me give one final example \u2014 blackjack. Casinos were happy to have their original payout scheme and then they realized people were counting cards\u00a0\u2014 i.e., those players had a better model than the casinos who were mispricing the payout. Now, in this case, the casinos \u201cbanned\u201d the new model rather than accepting it and modifying the payout downwards. But the point is the same. And it would be the same if a roulette player discovered that a flaw in a particular table meant that 0 comes up 10% of the time. I am exaggerating of course\u00a0\u2014 casinos are very, very, good at making sure this is not the case. But the principle is the same. If the distribution is found to be wrong, then either the price changes to reflect the new distribution or someone is going to make money with that information\u00a0\u2014 until the opportunity goes away.<\/span><\/p>\n

The above leads me to my two fundamental laws of finance: (1) the price of an asset is how much you can sell it for when you need to sell it; and (2) caveat emptor. It would be nice to have those embedded in any valuation models.<\/span><\/p>\n

Without giving away the secrets of the shop, do you have any suggestions about proper starting points?<\/span><\/p>\n

Caveat emptor. There is too much asymmetry of information and expertise in the exotic derivatives business for anyone other than the most sophisticated buyers. We only use very<\/span> vanilla derivatives for a very specific purpose.<\/span><\/p>\n

It has been my experience that most people in finance have never deconstructed the calculation of variance and standard deviation. If they did they would discover that it is a weighted averaging technique that unnecessarily aggregates information into a summary number that obscures the preponderance of upside volatility relative to downside volatility.<\/span><\/p>\n

What\u2019s your opinion of using semi-standard deviation or semi-variance<\/a> to improve the accuracy of derivative pricing models due to a better dissection of volatility?<\/span><\/p>\n

To be honest, I don\u2019t know enough about moving away from a symmetric definition of volatility and how it would be incorporated. But clearly most people with a long position in an asset are only interested in downside volatility. And the economic research is clear that people are much more sensitive to losses than gains.<\/span><\/p>\n

What are some of the holes in finance that you have spent time researching that we haven\u2019t covered so far?<\/span><\/p>\n

I\u2019ve spent the last six years working with corporate pension plans\u00a0\u2014 and their key issues are asset allocation and immunizing their liability. Asset allocation has been dominated by mean-variance optimization for 50 years now, but once you step back from it and look at the underlying assumptions, it\u2019s so clear that it\u2019s absolutely inappropriate for use in the real world. Here again, we have a lovely, simple idea which was taken to an extreme. The key flaw with this is the normality assumption about joint distributions of asset returns\u00a0\u2014 and this falls down so badly in reality. The quick way of explaining this is that diversification doesn\u2019t work just when you need it, and thus asset risk is always<\/em> higher than predicted. And, at its core, MVO is a one-period model. Which begs the question, what period is being used? And it\u2019s a very sensitive model\u00a0\u2014 continuous admittedly\u00a0\u2014 but not in a nice way. A model with 30 assets is going to require around 500 inputs . . . all of which have to come from somewhere. It\u2019s too easy to hide behind the fa\u00e7ade of mathematical sophistication. But I suppose one could call it a vanilla model at this point. . . . It just happens to be blatantly wrong.<\/span><\/p>\n

There\u2019s no doubt that many people are looking for an alternative. And while there isn\u2019t an established replacement on the horizon, there are better ways of doing it that are available.<\/span><\/p>\n

Pension plans are very interesting from a modeling perspective, and they actually contain a number of features that should be treated like options. There\u2019s one in particular that is ignored, or rather assumed away, because it\u2019s very difficult to model, but it has a very significant impact on estimating risk and reward in a pension plan\u2019s asset allocation, with the uniformly unfortunate consequence that risk is overestimated\u00a0\u2014 which can be just as deleterious as underestimating risk.<\/span><\/p>\n

Any model must lose information \u2014 either because the reality is too complex or the calculation is impossible to complete in a time that makes it useful. The balance, as Einstein said, it making it as simple as possible, but no simpler.<\/span><\/p>\n

Kevin, thank you for sharing your expertise with us about some of the critical holes in financial assumptions.<\/span><\/p>\n

 <\/p>\n

Originally published on CFA Institute\u2019s \u00a0Enterprising Investor<\/a>.<\/em><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

In part one of \u201cHoles in Some of Finance\u2019s Critical Assumptions,\u201d Kevin Harney of the hedge fund Massif Partners discussed some of the market distortions arising from critical finance assumptions like Brownian motion and normal distributions used in options pricing. What are some of the effects of these choices? What can be done differently? Here […]<\/p>\n","protected":false},"author":2,"featured_media":5266,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"categories":[3],"tags":[117,137,91],"class_list":["post-5265","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-the-blog","tag-hedge-funds","tag-options","tag-primer"],"_links":{"self":[{"href":"https:\/\/jasonapollovoss.com\/web\/wp-json\/wp\/v2\/posts\/5265","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/jasonapollovoss.com\/web\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/jasonapollovoss.com\/web\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/jasonapollovoss.com\/web\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/jasonapollovoss.com\/web\/wp-json\/wp\/v2\/comments?post=5265"}],"version-history":[{"count":0,"href":"https:\/\/jasonapollovoss.com\/web\/wp-json\/wp\/v2\/posts\/5265\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/jasonapollovoss.com\/web\/wp-json\/wp\/v2\/media\/5266"}],"wp:attachment":[{"href":"https:\/\/jasonapollovoss.com\/web\/wp-json\/wp\/v2\/media?parent=5265"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/jasonapollovoss.com\/web\/wp-json\/wp\/v2\/categories?post=5265"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/jasonapollovoss.com\/web\/wp-json\/wp\/v2\/tags?post=5265"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}