Interestingly, if one does portfolio optimization (always a dangerous tool, but still) to find out which mix of (backfilled) XIV or SPY gives the best backtested Sharpe ratio over the dates 3/26/2004 to 9/1/2015 the answer is: 100% XIV, 0% SPY (due to, apparently, the higher Sharpe ratio for XIV and the high correlation with SPY). Of course, in a real portfolio one would want to rebalance with cash or cash-like holdings otherwise the drawdowns would be insane.

Actually I meant to say 'more comprehensive numbers'. To get maximally representative numbers it would probably be necessary to control so that the start and end dates have similar VIX/contango.

Some more representative numbers for XIV are below.

If one uses the daily backfill data since 2004 [1], XIV has gone up by 10.9x since March 26, 2004 (when VIX future trading was initiated) to Sept 2, 2015, with the worst drawdown of 93% in the 2007 to 2008 period. The S&P during that period gained 2.17x with a maximum drawdown of 55%. The annualized Sharpe ratio for S&P 500 during this period is 0.53 (including dividends), whereas for the backfilled data XIV has a Sharpe ratio of 0.83. (In both Sharpe calculations I assumed the RFR was zero, since the RFR has been pretty low during that period).

The daily correlation of XIV with SPY appears to be about 0.94, from its initiation in 2010 to the current date. Volatility is mean reverting and the current date has volatility above the historical mean of 19.5 during the 2004 to 2015 observation window. So the current time may not be a representative observation time.

XIV appears to have gone up in general during this time because contango is the usual situation for VIX futures.

Why Shorting Volatility Now May Be A Very Favorable Trade [View article]

@djgabel: It really depends on the investment time frame. Obviously for short-term trading the backwardation is super dangerous. Everyone agrees on that, and it was mentioned in the original article.

But Martin mentions that this is a long-term trade/investment. Usually for long-term stock market investing one prefers holding periods of at least a couple years. In that case, Martin's strategy has a 100% win rate.

Would someone have closed the position due to psychological reasons in 2008? I suppose it depends on the person. But even if so, that is not a flaw in the strategy. It is a flaw in the person who makes the trade.

King Doesn't Need A Homerun To Keep Its Throne [View article]

Can any Windows 10 users report whether Candy Crush Saga is actually preinstalled on Windows 10? I saw that on the news but wanted to check the validity of that claim. Thanks!

As an illustration of (1), suppose we have some highly speculative security that has 50% chance to be worth say $2 to $6 (with a uniform distribution), but might have another 50% chance to be worth exactly U, where U is unknown to us. Classic value investing would suggest paying no more than $2 for this situation, as part of a diversified portfolio, because U might be 0.

However, we could also look at this from a UU perspective, and ask game theoretic questions such as:

(1) How easy is this situation for me to evaluate relative to others? (2) Is anyone I respect more of a domain expert on this situation than I, and which side are they on? Contrarily, does no one know much about this situation? (3) Does this situation interface with non-public companies or events that are not ordinary secondary market activities; if so, can I do any side-car investments with the relevant people? (4) Can I use my personal complementary skills to influence the business outcome of this situation? (5) Are counter-parties positioned in such a way that they are likely taking advantage of you, or vice versa? (6) Does this situation have one-off peculiarities such that computerized odds-making is more difficult here? Can we take advantage of errors in computerized models such as Black-Scholes? (7) Have I reduced any chances of myself being over-confident, by assuming appropriately large confidence intervals? (8) Over most sequences of events, which side has the more fragile thesis, e.g. it incorporates more conditionals or sequencing constraints?

These kinds of analyses could suggest the expected value for the unknown U could be, say, $0.5, or $1, or $2, as opposed to the worst case of $0.

Essentially the UU approach is to analyze the behavioral errors and game theoretic incentives of all the players, and select for situations that are impossible to quantify without an analysis that iterates through and explores these incentives. Thus one could hope to collect premiums off of what others might deem un-analyzable and un-investable.

I think these UU situations fundamentally do have a quantifiable probability distribution given enough game theory analysis, behavioral priors, and domain expertise. The key though is that without these priors, the probabilities are non-quantifiable.

I finally read Investing in the Unknown and Unknowable and thought it was decent. A few thoughts:

(1) These UU activities need not be taken as only speculations, as Ben Graham would define them. Often even though the intrinsic value is very difficult to calculate, one can establish some probabilistic lower bound (or when shorting, upper bound) that offers a statistical edge for mean reversion bets while in aggregate offering safety of principal and a reasonable rate of return.

(2) I guess the complementary skills part was how Andrew Carnegie made a lot of money. He knew the steel and railroad industries very well, and had inside information, placing him firmly in box B ("Easy for You, Hard for Others"), with the leverage of complementary skills in the industry. I think that is the optimal box, but if it is not available, box F is sometimes reasonable ("Unknownable to You, Unknowable to Others"). (Although, the box F example of Buffett's California reinsurance shows that it should only be taken if the terms are highly favorable.) I think in public markets investing, typically only activist investors have complementary skills, by their ability to pressure/threaten/replace management. I guess this means that private markets on average offer more opportunity.

(3) Retail investors are tend to be very bad at odds-making due to falling victim to behavioral biases. If one is on the same side as many retail speculators, and it is a UU situation, then I think this would be a bad sign, because it is likely that counter-parties are more sophisticated. Retail speculation on the long side in U.S. upstream oil companies is a recent example that I have noticed.

Let me describe this for the non-math inclined. It is a method for finding a minimum, like the bottom of a hill. If you were smart you would figure out which direction is steepest and go that way. But if you are dumb, you take the "stochastic gradient descent" approach: get drunk, put on a blindfold, and repeatedly spin around in a circle followed by taking ten paces forward if your feet tell you that "forward is downhill."

Interestingly, if you stagger drunkenly around like this long enough, eventually you will find the bottom of any nearby hill/cliff/ravine/etc (or in the case of some of these speculative volatility funds, $0). Since the expected result of every such staggering is locally downhill, eventually, gravity wins. But in the interim there are many uphill movements made, since after all, your feet might tell you some direction is initially downhill, but when you proceed in that direction, it involves climbing a ladder.

So you get long-term trending down but interim very spiky plots like this for SGD: http://bit.ly/1S7sAgw

This same SGD approach is also used by a lot of big tech / Big Data companies like Google and Facebook for training neural networks to recognize photos (e.g. cats). The computer just does a lot of blindfolded staggerings very quickly to "train" the neural network to recognize the photo.

My best friend in childhood had a nice two story house that was bought for $750 from the owner after being condemned I believe for code violations, loaded on a truck, shipped out the countryside, and repaired. I always thought that was pretty savvy.

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If one uses the daily backfill data since 2004 [1], XIV has gone up by 10.9x since March 26, 2004 (when VIX future trading was initiated) to Sept 2, 2015, with the worst drawdown of 93% in the 2007 to 2008 period. The S&P during that period gained 2.17x with a maximum drawdown of 55%. The annualized Sharpe ratio for S&P 500 during this period is 0.53 (including dividends), whereas for the backfilled data XIV has a Sharpe ratio of 0.83. (In both Sharpe calculations I assumed the RFR was zero, since the RFR has been pretty low during that period).

The daily correlation of XIV with SPY appears to be about 0.94, from its initiation in 2010 to the current date. Volatility is mean reverting and the current date has volatility above the historical mean of 19.5 during the 2004 to 2015 observation window. So the current time may not be a representative observation time.

XIV appears to have gone up in general during this time because contango is the usual situation for VIX futures.

[1]. http://bit.ly/zZ4gZ2

## Why Shorting Volatility Now May Be A Very Favorable Trade [View article]

But Martin mentions that this is a long-term trade/investment. Usually for long-term stock market investing one prefers holding periods of at least a couple years. In that case, Martin's strategy has a 100% win rate.

Would someone have closed the position due to psychological reasons in 2008? I suppose it depends on the person. But even if so, that is not a flaw in the strategy. It is a flaw in the person who makes the trade.

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http://bit.ly/1DEyfns

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## Kill Short-Term Pressures [View instapost]

However, we could also look at this from a UU perspective, and ask game theoretic questions such as:

(1) How easy is this situation for me to evaluate relative to others?

(2) Is anyone I respect more of a domain expert on this situation than I, and which side are they on? Contrarily, does no one know much about this situation?

(3) Does this situation interface with non-public companies or events that are not ordinary secondary market activities; if so, can I do any side-car investments with the relevant people?

(4) Can I use my personal complementary skills to influence the business outcome of this situation?

(5) Are counter-parties positioned in such a way that they are likely taking advantage of you, or vice versa?

(6) Does this situation have one-off peculiarities such that computerized odds-making is more difficult here? Can we take advantage of errors in computerized models such as Black-Scholes?

(7) Have I reduced any chances of myself being over-confident, by assuming appropriately large confidence intervals?

(8) Over most sequences of events, which side has the more fragile thesis, e.g. it incorporates more conditionals or sequencing constraints?

These kinds of analyses could suggest the expected value for the unknown U could be, say, $0.5, or $1, or $2, as opposed to the worst case of $0.

Essentially the UU approach is to analyze the behavioral errors and game theoretic incentives of all the players, and select for situations that are impossible to quantify without an analysis that iterates through and explores these incentives. Thus one could hope to collect premiums off of what others might deem un-analyzable and un-investable.

I think these UU situations fundamentally do have a quantifiable probability distribution given enough game theory analysis, behavioral priors, and domain expertise. The key though is that without these priors, the probabilities are non-quantifiable.

## Kill Short-Term Pressures [View instapost]

(1) These UU activities need not be taken as only speculations, as Ben Graham would define them. Often even though the intrinsic value is very difficult to calculate, one can establish some probabilistic lower bound (or when shorting, upper bound) that offers a statistical edge for mean reversion bets while in aggregate offering safety of principal and a reasonable rate of return.

(2) I guess the complementary skills part was how Andrew Carnegie made a lot of money. He knew the steel and railroad industries very well, and had inside information, placing him firmly in box B ("Easy for You, Hard for Others"), with the leverage of complementary skills in the industry. I think that is the optimal box, but if it is not available, box F is sometimes reasonable ("Unknownable to You, Unknowable to Others"). (Although, the box F example of Buffett's California reinsurance shows that it should only be taken if the terms are highly favorable.) I think in public markets investing, typically only activist investors have complementary skills, by their ability to pressure/threaten/replace management. I guess this means that private markets on average offer more opportunity.

(3) Retail investors are tend to be very bad at odds-making due to falling victim to behavioral biases. If one is on the same side as many retail speculators, and it is a UU situation, then I think this would be a bad sign, because it is likely that counter-parties are more sophisticated. Retail speculation on the long side in U.S. upstream oil companies is a recent example that I have noticed.

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Let me describe this for the non-math inclined. It is a method for finding a minimum, like the bottom of a hill. If you were smart you would figure out which direction is steepest and go that way. But if you are dumb, you take the "stochastic gradient descent" approach: get drunk, put on a blindfold, and repeatedly spin around in a circle followed by taking ten paces forward if your feet tell you that "forward is downhill."

Interestingly, if you stagger drunkenly around like this long enough, eventually you will find the bottom of any nearby hill/cliff/ravine/etc (or in the case of some of these speculative volatility funds, $0). Since the expected result of every such staggering is locally downhill, eventually, gravity wins. But in the interim there are many uphill movements made, since after all, your feet might tell you some direction is initially downhill, but when you proceed in that direction, it involves climbing a ladder.

So you get long-term trending down but interim very spiky plots like this for SGD:

http://bit.ly/1S7sAgw

This same SGD approach is also used by a lot of big tech / Big Data companies like Google and Facebook for training neural networks to recognize photos (e.g. cats). The computer just does a lot of blindfolded staggerings very quickly to "train" the neural network to recognize the photo.

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But why not just close out the short put by buying it back? How does the IRR of buying back the put compare with creating a spread?

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