I agree volatility of the stock price is represented by standard deviation.IMHO, volatility is the std dev of returns, but risk is something different. Risk is the std dev of the residuals of your “model.” When you have no model, then volatility and risk end up being the same thing since the residuals become the returns around the mean (or 0 if there is no expected drift).
The above difference between vol and risk is based on the assumption that we can model the returns of an instrument. This shouldn’t be a stretch as we’ve been relying on models such as CAPM and the factor models from Fama-French, etc. for a long time. Let’s say that you’re using a single factor model (it’s easier to visualize), perhaps using the momentum factor. So you load up on positive momentum stocks and short negative momentum ones. In our idealized example, the positive momentum stocks may have high volatility due to the fact that their prices go up a lot and the negative momentum stocks may have high volatility because their price depreciates a lot. But, in this highly unrealistic and for illustrative purposes only scenario, the “risk” experienced by your model would probably be much lower than the volatility in the associated stocks. Your model would have nice (positive) returns since it went long the appreciating stocks and short the depreciating ones. There would be a distribution to the returns generated by your model over some time period (think daily returns over a 20 year span). And, these returns generated by your model would not match exactly the returns “expected” by your model; in some time periods, your model would expect higher returns than it produced and vice versa. The difference between the returns the model generated and expected is the residuals. The std dev of the distribution of these residuals gives us an idea of the “riskiness” of the model.
Of course, everything I’ve said above is easier said than done and is based on some assumptions that we know don’t really hold in practice. For one, it assumes that we can actually create an unbiased model of the underlying process; I’m not sure that I’ve actually ever seen this. Certainly it would mean that our model is free from any data snooping, look ahead and any other biases; no curve fittingAnother issue is that we’re using std dev, which really only works well on normal distributions and we know that market returns (even log returns) are not normal. But, IMO, despite practical implementation challenges, the above mental model is useful when thinking about the difference between volatility and risk.
When I day trade, risk and volatility are essentially the same.
But as a long term investor, my #1 risk is the probability of the company going out of business. Of course there are also other risk, like I may not get all of my money back...
It is often very complicated, different from just the volatility of the stock price?
Anyway, it is beyond my pay grade, my capability, to define risk.
Another issue is that we’re using std dev, which really only works well on normal distributions and we know that market returns (even log returns) are not normal. But, IMO, despite practical implementation challenges, the above mental model is useful when thinking about the difference between volatility and risk.