I tried analyzing cycling accidents around New York City, but it was difficult to draw meaningful conclusions from the data. For example, men die more frequently than women while biking, according to a report on Bicyclist Fatalities and Serious Injuries in New York City.
However, interpreting this data is tricky. For example, do more men die because more men bike or is it because they engage in riskier behavior? I had a different issue with analyzing accident locations (even though the data has geogrpahical location, longitude and latitude).
I've recently been tasked with working more with more fixed-income derivatives, so I'm writing this more as a refresher for myself, but I've always guaged how well I know something based on how well I'm able to teach others.
Recall that the Black-Scholes price of a European-style call option is
\[ \begin{equation} \begin{aligned} C & = S\Phi(d_1)-Ke^{-rT}\Phi(d_2),\\ d_1 & = \frac{1}{\sigma\sqrt{T}}[log(S/K)+[r+(1/2)\sigma^2]T],\\ d_2 & = d_1-\sigma\sqrt{T}, \end{aligned} \end{equation} \]
where $\Phi(x)$ is the cumulative distribution function (CDF) of the standard normal distribution:
One of the most bizarre David vs. Goliath scenarios in modern finance, by far, belongs to the retail investors from Reddit's r/WallStreetBets versus Hedge Fund space. The story became such a fad that it garnered the attention of Treasury Secretary Janet Yellen and the SEC. With the democratization of finance, everyone can invest in markets once thought unobtainable to the common man.
r/WallStreetBets vs. Wall Street Hedge Funds The mania revolves around the most shorted stocks, shorted by hedge funds that hoped to make a killing when those stocks collapse.
Lying with statistis - Case 1324
How we respond to infectious dieases is probably more important than the diease itself.