Recent academic work has developed a method to determine, in real time, if a
given stock is exhibiting a price bubble. Currently there is speculation in the
financial press concerning the existence of a price bubble in the aftermath of
the recent IPO of LinkedIn. We analyze stock price tick data from the short
lifetime of this stock through May 24, 2011, and we find that LinkedIn has a
price bubble.
In this paper we study progressive ?ltration expansions with random times. We
show how semimartingale decompositions in the expanded ?ltration can be
obtained using a natural link between progressive and initial expansions. The
link is, on an intuitive level, that the two coincide after the random time. We
make this idea precise and use it to establish known and new results in the
case of expansion with a single random time. The methods are then extended to
the multiple time case, without any restrictions on the ordering of the
individual times.
We study strict local martingales via h-transforms, a method which first
appeared in Delbaen-Schachermayer. We show that strict local martingales arise
whenever there is a consistent family of change of measures where the two
measures are not equivalent to one another. Several old and new strict local
martingales are identified. We treat examples of diffusions with various
boundary behavior, size-bias sampling of diffusion paths, and non-colliding
diffusions. A multidimensional generalization to conformal strict local
martingales is achieved through Kelvin transform.