Tuesday, November 29, 2011

Implied Volatility Smile. Writing Options

Volatility Smile is the phenomenon of implied volatilities tend to increase the further away strikes go from the money. It's pretty similar to Long-Shot Bias seen in horse racing odds where bets with great win/loss size ratios offer increasingly negative expectations for the backing bets. So yes, far away from the money options tend to be over valued with respect to likely future realized volatility.

Regardless, some traders/punters/risk management don't realize that regardless of potential winning/losing size ratios, a positive EV (Expected Value) is what's needed to make money.

A pretty simple demonstration of this is everyone's fear of selling put options, as the draw downs can be quite sharp. It has a positive EV, and does make money OVER TIME, limiting volatility of its return is the job of the trader. We can see how a pure put-selling strategy would've done over the years off CBOE's Buy-Write Index.

BXM Index

Sunday, November 13, 2011

Supreme Court Vote Predictions via Algorithms

In John Grisham's The Appeal, mathematical/statistical algorithms were applied to forecast appellate court decisions. Albeit fiction, I had always found this concept interesting and had consulted several law school friends; who did not express as much faith in code, objective number crunching.

Well, it looks like somebody else has begun this research effort ahead of me; came across this article off Slashdot.

US Supreme Court Votes Can Be Predicted

...These researchers say their computational models, using methods developed to analyze complex social networks, are just as accurate in predicting a justice's decision as forecasts from legal experts.  

'We find that Supreme Court justices are significantly more predictable than one would expect from 'ideally independent' justices in 'ideal courts,' that is, free agents independently evaluating cases on their merits, free of ideology, the study said.

...In using a 'block' method, grouping justices and cases, the researchers' model correctly predicted 83 percent of votes, compared with 67.9 percent from legal experts in one study and 66.7 percent from a case-content-based algorithm.


It'll take time and more research publications for this concept to gain acceptance in legal industry; just as quantitative finance did with Wall St. Regardless, I genuinely believe this could offer existing legal teams a significant, objective edge against competing entities.