Volatility is usually defined as the standard deviation of returns, but sometimes it is calculated as the standard deviation of cross-sectional return divided by the square root of time, where other times it is simply the standard deviation of returns for each time period.

I guess for equities that follow Brownian motion, the two don't really matter, since its variance equals time. But this property does not usually hold. For example, if you simulate bond price paths using transition matrices, assuming a fixed spread for bonds with the same credit rating, then the resulting price paths are clearly not Markovian: A bond that has been downgraded will have even a higher chance of being downgraded, thus low price bonds have stronger tendency to drift downward, and the opposite is true since upgraded bonds have even a smaller chance of being downgraded.

This creates an unusual situation. Suppose we simulate many price paths, there seems to be two ways to calculate the return volatility:
1. Find the standard deviation of the cumulative return at the end, the divide by the square root of time.
2. For each path, find the standard deviation of returns for all the time periods, and the take the average for all paths.

I have ran some simulations and the two does not match very well. The cross-sectional volatility is usually 50% to 200% larger than the time-wise volatility.

What would be a good way to illustrate volatility using these price paths?