Remember the volcanic eruption in New Zealand, the devastating bushfires in Australia, California and the Amazon? Yes, well this all happened in the past 12 months. Financial markets haven’t had a smooth ride either, three out of the top 20 largest one day moves of the S&P500 index over the past 100 years happened in 2020.
Statistically if markets were a normal distribution (which they are certainly not) one would expect 0.3% or 1 in every 333 trading days to have a 3 standard deviation move, 2020 already had 17 such days, that means the price action of the year to date should have happened over a time span of almost 23 years…. No wonder it feels like we have aged so much
One could argue that volatility compresses time.
Your reaction time during low volatility periods will have much less of an impact in a low volatility environment than in an environment where markets move more in a few hours than what they annualise. In other words, when you make a bad decision or a mistake in a low volatility period, it will be less painful to your portfolio.
What is market volatility and how can one measure it?
There are a few ways to measure market volatility.
- Standard deviation from the mean (Historical volatility), this is the most common measure of volatility and used by most statistical models in financial markets
- Intra-day volatility or True Range, this is the previous day close to current day high and low. Traders often use this measure to determine price targets or stop losses.
- Implied volatility – this is used by option traders to price Call and Put options
- Volatility indices like the VIX. The VIX comprises of an average of implied volatilities at various price levels.
Looking at the annualised historical volatility over the past 90 years one can clearly see that volatility spikes during market corrections and crashes
(Let’s focus on the high volatility periods for now although the same can be done for low volatility periods)
The year to date has been on average 2.5 times more volatile than the previous hundred years, that would mean the average daily return should be split over 2.5 days or alternatively divided by 2.5 to reduce volatility back to the mean.
Now what would happen should one reduce volatility back to the mean?
(Current volatility / Historical average volatility x daily market movement = Volatility adjusted market movement)
Continuously adjusting to an ever-changing market environment can mean the difference between average and market beating returns, especially in environments such as the above where the volatility is extremely high relative to its history.
Certain principles will always hold in financial markets, but the execution of these principles should be adjusted to the prevailing risk and market environment. This is just one example of what we at Methodical watch closely to allow us to adapt to changing market conditions.