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cook675
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Stock probability incorporating volatility

May 1st, 2021, 2:48 am

Hello, I'm trying to understand the best approach to model the probability of a stock move. My initial approach, however naive, was to create an X day rolling average of the stock prices over the trading history, then create a cumulative distribution function to determine what the probability was of a stock price being at or above a certain level X days from now

Ok, its im the ballpark probably somewhere. But recently I thought that, the probability must be somehow related to the current volatility estimate of the stock. If the stock's IV is very high right now, but for a large majority of the past it was very low, then the probability should be higher than what my rolling average analysis returns.

This lead me to discover the "Expected move" formula, which does seem to incorporate volatility. However, the classic expected move formula returns a range of prices for which the stock will be within 1 standard deviation over the time frame in question.

How could I rework this equation to answer my original question, or is there an alternate equation which incorporates volatility to get an estimate for the probability of a stock move?

Thanks for considering my questions. I am relatively new to this field but very eager to learn.
 
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Alan
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Re: Stock probability incorporating volatility

May 1st, 2021, 5:12 pm

You're thinking correctly that you should use options. But I wouldn't use IV's directly. Instead, from option prices, using the Breeden-Litzenberger formula, one can infer (up to some issues with the tails) the forward-looking Q-distribution. Then, the issue becomes: how to best transform from that to the P-distribution?, which is the distribution you seek. 

For the SPX, one answer is to use an exponential tilted distribution with tilt parameter [$]\gamma[$]. Historically, one can estimate, say from post WWI data, [$]\gamma \approx 3 \pm 0.8[$]. The interpretation is that [$]\gamma[$] is the coefficient of relative risk aversion (CRRA) for a 'representative investor'. 

For single-name stocks, the idea would be similar, but you use a stochastic discount factor transformation. 
There's more detail in Appendix A of my paper on Option-based equity risk premiums. An associated book is forthcoming. 
The single-name stock stuff needs fleshing out, but you could experiment with just the SPX case. 

Note that the linked paper uses [$]\kappa[$] for what I am now calling [$]\gamma[$]. (Have decided the latter is more conventional and adopted that for the book). Also, my best error estimate  for [$]\gamma[$] is currently the one in this post.
 
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Alan
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Re: Stock probability incorporating volatility

May 1st, 2021, 5:34 pm

Sorry, spotted a typo. That [$]\gamma[$] estimate is roughly what you find from post WWII data. If you go back further and take, say 1926 to date, then you will estimate a point [$]\gamma[$] that is somewhat lower. Generally, for any plausible data period, you'll estimate [$]\gamma \in (2,4)[$]. 
 
cook675
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Joined: April 18th, 2021, 7:27 pm

Re: Stock probability incorporating volatility

May 1st, 2021, 10:51 pm

Thanks Alan im going to look into this heavily and report back with questions. I was also starting on SPX and had planned to eventually move onto single tickers. Very helpful, much appreciated
 
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Alan
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Re: Stock probability incorporating volatility

May 2nd, 2021, 2:40 pm

You're welcome. 

There's something worth adding. As you'll see, in the linked paper, I  fit option prices to a Gaussian mixture model. This computationally intensive fit (as I show in my book) can be avoided (with a small loss of accuracy) for the equity risk premium estimates. For your problem, estimating the P-density, I'm not sure if you can skip that fit -- it's an interesting issue.