Based on your financial expertise, provide your response or viewpoint on the given financial question or topic. The response format is open.

How do annual risks translate into long-term risks?
The short answer is the annualised volatility over twenty years should be pretty much the same as the annualised volatility over five years. For independent, identically distributed returns the volatility scales proportionally. So for any number of monthly returns T, setting the annualization factor m = 12 annualises the volatility.  It should be the same for all time scales.  However, note the discussion here: https://quant.stackexchange.com/a/7496/7178 Scaling volatility [like this] only is mathematically correct when the   underlying price model is driven by Geometric Brownian motion which   implies that prices are log normally distributed and returns are   normally distributed. Particularly the comment: "its a well known fact that volatility is overestimated when scaled over long periods of time without a change of model to estimate such "long-term" volatility." Now, a demonstration.  I have modelled 12,000 monthly returns with mean = 3% and standard deviation = 2, so the annualised volatility should be Sqrt(12) * 2 = 6.9282.   Calculating annualised volatility for return sequences of various lengths (3, 6, 12, 60 months etc.) reveals an inaccuracy for shorter sequences.  The five-year sequence average got closest to the theoretically expected figure (6.9282), and, as the commenter noted "volatility is [slightly] overestimated when scaled over long periods of time". Annualised volatility for varying return sequence lengths  Edit re. comment Reinvesting returns does not affect the volatility much.  For instance, comparing some data I have handy, the Dow Jones Industrial Average Capital Returns (CR) versus Net Returns (NR).  The return differences are somewhat smoothed, 0.1% each month, 0.25% every third month.  More erratic dividend reinvestment would increase the volatility.