Annualizing Volatility When Returns Are Autocorrelated

# Standard deviation of annual returns formulas return all different values


# Standard deviation of annual returns formulas return all different values












I am trying to build a monte-carlo simulator for predicting the possible future values of a portfolio. I have daily historical prices for several assets but I don't know how to correctly estimate annual standard deviation of the returns for each specific asset.

Looking on the web there are three different ways to compute it:

- Calculate std on the daily returns and multiply by sqrt(252)

- Calculate std on monthly returns and multiply by sqrt(12)

- Calculate std on annual returns

But each of them return different results with the last one usually being much larger with respect to the first two. For instance if I apply those strategies to the historical daily gold prices I will have the following annual deviations:

- 20.89%

- 21.16%

- 28.91%

I think the most appropriate one is to directly compute std on annual returns but this will lead to a high deviations which is different to the one I usually see on the web for specific assets.

## Answer by Newquant (score 1)

https://quant.stackexchange.com/a/73747

It looks like you've discovered positive autocorrelation between returns.

When returns are independent (and identically distributed), one is able to annualise volatility using the sqrt(T) rule. However, when there is autocorrelation (subsequent returns over equal time slice, dt, having a correlation != 0) one cannot annualise with the sqrt(T) rule anymore, and must resort to an uglier looking annualisation factor:

For variance V(r_ht):

$V(r_{ht}) = σ^2 * (h + 2ρ/(1-ρ)^2 * ((h-1)(1-ρ) - ρ(1-p)^{h-1}))$

So the volatility annualisation is:

$σ * \sqrt{(h + 2ρ/(1-ρ)^2 * ((h-1)(1-ρ) - ρ(1-p)^{h-1}))}$

Where ρ is the correlation between subsequent returns at your chosen time slice, dt; and where h is the number of periods in a year for your chosen time slice, 1/dt.

For daily data with a volatility of 1.31%, the sqrt(T) rule would dictate that the annualised volatility is 1.31% * sqrt(252) = 20.89%.

If returns had a daily autocorrelation of 0.2, then the annualised volatility would be 1.31% * sqrt(377.3) = 0.2544.

In your case it seems your return autocorrelation is 0.3192. Which suggests a positive trend affect.

You can confirm this using numerical methods. I found it written in Carole Alexander's book 'Practical Financial Econometrics', which is the second book in her 'Market Risk Analysis' series.