I just wish people understand the product they are trading better before saying things like that. In the modern liquid vol market, an options model serves as a interpolation/comparison tool more than anything else. Black-Scholes framework, with some minor tweaks fits that purpose well enough. It's also a reasonable basic model for risk management, unless you are dealing with exotic derivatives of some sort.Unfortunately as we know Black Scholes assumes stock returns are normally distributed, which they are not.
With the recent busts of LJM and optionsellers.com, I have been thinking about the feasibility of selling options without risking a blowup. Looking at LJM's performance in 2013 when vol was low appears to indicate that there are times to sell and times to buy (or at least not sell). I have set out some principles which are up for discussion and modification:
1. Only sell options on SPX or SPY when decent premium can be earned without employing a lot of leverage (i.e. not selling deep OTM options that do not pay much premium and compensating with leverage)
a) At what VIX level should one not be selling options?
b) What are the best strikes to select? (a balance between having to adjust vs. not having to over-leverage)
2. Use some of the earned inflated option premium to buy some far OTM puts (units or tennies) to hedge against a fast moving market (i.e. marking conditions where one cannot adjust / roll options positions as the market moves toward the strikes)
Hopefully some members like sle will join in.![]()
...is farther away than your short leg.
I just wish people understand the product they are trading better before saying things like that. In the modern liquid vol market, an options model serves as a interpolation/comparison tool more than anything else. Black-Scholes framework, with some minor tweaks fits that purpose well enough. It's also a reasonable basic model for risk management, unless you are dealing with exotic derivatives of some sort.
Back to the topic, there is a question of "when is a good time to sell options?" and then there is a question of "how do I prevent a blowup?". The answer to the first question is that you have one or several ways of forecasting that realized volatility would underperform the implied one. The second question, however, should be reformulated as "how much of your expected gains are you willing to spend to protect yourself from a disaster?" and "what exactly is a disaster scenario?".
Agreed. However the point remains that the Black Scholes framework does not allow you to predict the future in any sense. The framework may indeed tell you that given assumptions in the market today regarding volatility the option is priced more or less "correctly".Black-Scholes framework, with some minor tweaks fits that purpose well enough. It's also a reasonable basic model for risk management, unless you are dealing with exotic derivatives of some sort.
What are these methods? One poster has suggested using the mean reverting nature of volatility to sell high and buy low. One problem with this approach is you have to wait for extremes or at least levels which, historically at least, look to be high or low. While implied volatility can always go higher however to irrational levels, at least there is a fairly reliable floor.I The answer to the first question is that you have one or several ways of forecasting that realized volatility would underperform the implied one.
Create a regression with a response variable log(RV_T1/IV_T0) add some explanatory variables. That should get you started down the rabbit hole.Any hints on how to go about creating a model that can forecast realized volatility?
Does this equate to "if realised volatility today is lower than forecast volatility at T+30 then sell the cash S&P" and vice versa? In back testing? On the grounds that higher realised volatility usually occurs in falling markets? And in answer to the question of the original poster, if this regression shows higher volatility forecast at day 30 than today then should the poster sell calls on the S&P on grounds that the index is likely to go down?Create a regression with a response variable log(RV_T1/IV_T0) add some explanatory variables. That should get you started down the rabbit hole.
Edit** I read "Implied / Realized volatility". For index and very liquid equities, implied vol is your best bet in forecasting realized vol. Try regressing SPX IV 1 month ~ SPX RV Lagged 1 month and you will see how good the relationship is.
In all fairness a regression is probably not the best tool to use for this, but it's a place to start. That is not what I was implying in my message. I'm to tired to explain. Interpret it as you wish, time for 3 hours of sleep goodnight.Does this equate to "if realised volatility today is lower than forecast volatility at T+30 then sell the cash S&P" and vice versa? In back testing? On the grounds that higher realised volatility usually occurs in falling markets? And in answer to the question of the original poster, if this regression shows higher volatility forecast at day 30 than today then should the poster sell calls on the S&P on grounds that the index is likely to go down?