Abstract:
In this research we describe how forward-looking information on the statistical properties of an asset
can be extracted directly from options market data and demonstrate how this can be practically applied
to portfolio management. Although the extraction of a forward-looking risk-neutral distribution is
well-established in the literature, the issue of estimating distributions in an illiquid market is not. We
use the deterministic SVI volatility model to estimate weekly risk-neutral distribution surfaces. The
issue of calibration with sparse and noisy data is considered at length and a simple but robust fitting
algorithm is proposed. We further attempt to extract real-world implied information by implementing
the recovery theorem introduced by Ross (2015). Recovery is an ill-posed problem that requires careful
consideration. We describe a regularisation methodology for extracting real-world implied distributions
and implement this method on a history of SVI volatility surfaces. We analyse the first four moments
from the implied risk-neutral and real-world implied distributions and use them as signals within a
simple tactical asset allocation framework, finding promising results.
