5 ms·

I read that 3x leverage is bad because of volatility drag or something like that so 2x "is the sweet spot" SSO is 2x leveraged SP500 https://finance.yahoo.com

I read that 3x leverage is bad because of volatility drag or something like that so 2x "is the sweet spot" SSO is 2x leveraged SP500 https://finance.yahoo.com/quote/SSO https://finance.yahoo.com/quote/SSO Check out the chart since 2007 versus the SP500 index to today. I'm seeing 211% return for SSO, 113% for SPY. Just remember that we've pretty much been in a full on bear market ever since the last recession. Whether or not terrible things would have happened to SSO if we experienced a true recession/crash and whether or not you would actually recover/beat SPY, I have no clue and am not qualified to say. But, based on the cherry picked '07-'19, yes, SSO (2x SPY) beats SPY (1x SP500) 211% to 113%

smabie 7y ago
It really depends, see my blog post on the matter (part 1): https://smabie.github.io/posts/2019/10/04/vol.html https://smabie.github.io/posts/2019/10/04/vol.html In short, assuming a normal distribution, your max leverage ratio should be the future expected returns divided by the expected variance. I’m not at a computer right now so I can’t run the math on real S&P returns, but assuming 10% returns and 10% vol and a normal distribution, the highest leverage ratio you would ever be able to justify is 10/10^2 or 10x leverage. In my blog post I looked at the beginning of 2018 up to present. The ideal leverage ratio according to my equation (I’m sure it’s not novel, but haven’t been able to find anyone else talking about it), is 2.99x. Of course, the more skew or kurtosis the distribution has the less accurate this equation becomes. Right now I’m working on a hypothetical S&P ETF that adjusts leverage bases on forecasted volatility and returns. Almost impossible to get right, but my preliminary model has outperformed (not risk adjusted) any fixed leverage I’ve thrown at it.
- MuffinFlavored 7y ago
  You made a blog post that 10x leverage would be good given the past 12 months (where the market has mainly been up-up-up)? Doesn't that 100% ignore the effects of drag when the market goes down-down-down?
  - smabie 7y ago
    First of all, the market has not been exactly “up-up-up.” There has been massive volatility and drawdowns in the last year. Second, my analysis was from the beginning of 2018 to present. Using that data, the ideal leverage ratio is 2.99x. Third, the ret/var model put forth in the post assumes a log-normal distribution. The less log-normal the returns are, the less accurate it becomes. Excess kurtosis or skew will definitely affect the accuracy of the model. So the model would probably be fine if the market was up-up-up or down-down-down, I haven’t encountered any periods in which the non-normality of the distribution is so severe that it completely breaks the model.

smabie 7y ago

It really depends, see my blog post on the matter (part 1): https://smabie.github.io/posts/2019/10/04/vol.html https://smabie.github.io/posts/2019/10/04/vol.html In short, assuming a normal distribution, your max leverage ratio should be the future expected returns divided by the expected variance. I’m not at a computer right now so I can’t run the math on real S&P returns, but assuming 10% returns and 10% vol and a normal distribution, the highest leverage ratio you would ever be able to justify is 10/10^2 or 10x leverage. In my blog post I looked at the beginning of 2018 up to present. The ideal leverage ratio according to my equation (I’m sure it’s not novel, but haven’t been able to find anyone else talking about it), is 2.99x. Of course, the more skew or kurtosis the distribution has the less accurate this equation becomes. Right now I’m working on a hypothetical S&P ETF that adjusts leverage bases on forecasted volatility and returns. Almost impossible to get right, but my preliminary model has outperformed (not risk adjusted) any fixed leverage I’ve thrown at it.

MuffinFlavored 7y ago
You made a blog post that 10x leverage would be good given the past 12 months (where the market has mainly been up-up-up)? Doesn't that 100% ignore the effects of drag when the market goes down-down-down?
- smabie 7y ago
  First of all, the market has not been exactly “up-up-up.” There has been massive volatility and drawdowns in the last year. Second, my analysis was from the beginning of 2018 to present. Using that data, the ideal leverage ratio is 2.99x. Third, the ret/var model put forth in the post assumes a log-normal distribution. The less log-normal the returns are, the less accurate it becomes. Excess kurtosis or skew will definitely affect the accuracy of the model. So the model would probably be fine if the market was up-up-up or down-down-down, I haven’t encountered any periods in which the non-normality of the distribution is so severe that it completely breaks the model.