Many automobile insurance companies offer the possibility to monitor driving habits and distance driven by means of telematics devices installed in the vehicles. This provides a novel source of data that can be analysed to calculate personalised tariffs.
Commercially available factor models provide good predictions of short-horizon (e.g. one day or one week) portfolio volatility, based on estimated portfolio factor loadings and responsive estimates of factor volatility.
The one-month return reversal in equity prices was first documented by Jedadeesh (1990), who found that there was a highly significant negative serial correlation in the monthly return series of stocks. This is in contrast to the positive serial correlation of the annual stock returns.
Recent research finds that prominent asset pricing models have mixed success in evaluating the cross-section of anomalies, which highlights proliferation of anomalies and zoo of factors. In this paper, I investigate that how is the relative pricing performance of these models to explain anomalies, when comparing their misspecification errors– the Hansen–Jagannathan (HJ) distance measure.
As an alternative to traditional loans, young people could issue securities that pay dividends that depend on their future financial success in life. This type of a personal IPO is especially desirable for young people, who for example may need money for a college education, because it allows them to shift the risk of repayment to investors who bet on their future success, unlike in a traditional loan setting.
We consider the optimal execution of a book of options when market impact is a driver of the option price. We aim at minimizing the mean-variance risk criterion for a given market impact function. First, we develop a framework to justify the choice of our market impact function.
In 2009, the Seventh Circuit ruled in U.S. v. Apex Oil that certain types of injunctions requiring firms to clean up previously released toxic chemicals were not dischargeable in bankruptcy.
Parameter-dependent Markov chains with exponentially small transition rates arise in modeling complex systems in physics, chemistry, and biology. Such processes often manifest metastability, and the spectral properties of the generators largely govern their long-term dynamics.
Nonstandard analysis, a powerful machinery derived from mathematical logic, has had many applications in probability theory as well as stochastic processes. Nonstandard analysis allows construction of a single object - a hyperfinite probability space - which satisfies all the first order logical properties of a finite probability space, but which can be simultaneously viewed as a measure-theoretical probability space via the Loeb construction.
This paper proposes an asset pricing model with endogenous allocation of constrained learning capacity, that provides an explanation for abnormal returns before the scheduled release of information about firms, such as quarterly earnings announcements.
This talk will provide an overview of recent results in high-dimensional robust estimation. The key question is the following: given a dataset, some fraction of which consists of arbitrary outliers, what can be learned about the non-outlying points?
