We show how distributions can be reduced to low-dimensional scenario trees. Applied to intertemporal distributions, the scenarios and their probabilities become time-varying factors. From S&P 500 options, two or three time-varying scenarios suffice to forecast returns, implied variance or...

A random variable dominates another random variable with respect to the covariance order if the covariance of any two monotone increasing functions of this variable is smaller. We characterize completely the covariance order, give strong sufficient conditions for it, present a number of examples...

The evaluation of scientific research is crucial for both the academic community and society as a whole. Numerous bibliometric indices have been proposed for the ranking of research performance, mainly on an ad hoc basis. We now introduce the novel class of Scientific Research Measures (SRMs) to...

We investigate the distributions of e-drawdowns and e-drawups of the most liquid futures financial contracts of the world at time scales of 30 seconds. The e-drawdowns (resp. e-drawups) generalise the notion of runs of negative (resp. positive) returns so as to capture the risks to which...

We analyse the behaviour of a non-linear model of coupled stock and bond prices exhibiting periodically collapsing bubbles. By using the formalism of dynamical system theory, we explain what drives the bubbles and how foreshocks or aftershocks are generated. A dynamical phase space...

We develop a dynamic model of corporate investment and financing decisions in which corporate insiders have superior information about the firm's growth prospects. We show that firms with positive private information can credibly signal their type to outside investors using the timing of...

We provide a new method to derive the state price density per unit probability based on option prices and GARCH model. We derive the risk neutral distribution using the result in Breeden and Litzenberger (1978) and the historical density adapting the GARCH model of Barone-Adesi, Engle, and...

We develop a discrete-time stochastic volatility option pricing model, which exploits the information contained in high-frequency data. The Realized Volatility (RV) is used as a proxy of the unobservable log-returns volatility. We model its dynamics by a simple but effective (pseudo) long memory...

We model the dynamics of asset prices and associated derivatives by consideration of the dynamics of the conditional probability density process for the value of an asset at some specified time in the future. In the case where the asset is driven by Brownian motion, an associated "master...