Questions:

• We use AiLA-V001, which is a volatility index constructed from liquid options across the most liquid commodities, to measure the markets view on volatility
• Can we observe any relationships between the AiLA alpha product (AiLA S series) performance and the prevailing volatility conditions reflected by the AiLA-V001?
• Can we observe any relationships when periods of high volatility are removed from the equation?

Approach:

• Given the lack of data sufficiency with one single AiLA index, all AiLA products were pooled together for the analysis.
• The historical expected returns were investigated for AiLA products, conditional on different volatility circumstances.
• We use implied volatility and vol index interchangeably as vol index constitutes a basket of implied volatility.
• This analysis was done with high volatility periods like the 2020 COVID-19 crash being excluded.

• We split data into three bins based on the prevailing IV index values, i.e., levels of volatility.
• The filtered (removing periods of high volatility) and unfiltered cases shows a slight upward trend that suggest a weak indication of expected returns to be larger during periods with higher implied volatility.
• This indication disappears when normalizing the returns to the sample std.dev. of their corresponding bin.
• Interestingly, the filtered data shows an inverse relationship between normalized performance and implied volatility, which suggests that high volatility of returns coincide with periods of high implied volatility.
• Therefore, it would primarily be explained by the different size of the returns rather than the ability to predict their sign.
• There is an indication of a uniform product performance when traded on a risk adjusted basis.

• We split the data into three bins based on the prevailing change of IV index values, i.e., decreasing, flat or increasing IV.
• This indicates a different pattern for long-short and long-only AiLA products.
• The AiLA long-short products indicates a tendency to perform well in both a decreasing as well as increasing IV environment.
• It is interesting to point out that AiLA long-short products perform well in periods of high increases in IV, which may suggest that the long-short strategy takes advantage of the changing market conditions at a faster velocity.
• The AiLA long-only products indicate a strong tendency of performing best in a decreasing IV environment and to slightly underperform during increasing IV periods, which is also consistent in both the filtered and unfiltered data set.
• The long-only pattern appears intuitively consistent with the typical IV development during a market crash period, e.g., such as the first half of 2020.

• The performance pattern w.r.t increasing and decreasing IV is generally reflected also when broken down to an individual AiLA product level.
• Here, historical monthly returns are compared to increasing and decreasing IV periods. We can see that for 24 out of 30 AiLA products, monthly returns are higher in increasing IV periods than the monthly returns in decreasing IV periods.
• The plot for historical daily returns is inconclusive in determining the above, likely due to a daily period being too short to assess the effect of changing IV.
• It should, however, be noted that the limited data set and corresponding statistical errors generally makes it difficult to draw generalized conclusions.