International Journal of Theoretical and Applied Finance

We set up a model to study the voluntary disclosure of information by insiders of publicly traded companies. We consider a trading framework as in [14] with many assets and one insider per asset. There is one discretionary liquidity trader who can allocate his trades across the different assets and many noise traders who trade with equal intensity in all assets. Before trade begins, insiders can disclose information in order to attract the discretionary liquidity trades. We show that if the level of noise trading is above a certain threshold, then there is an equilibrium where all insiders do not disclose any information. Below this threshold, equilibria are such that some information is always revealed by insiders. We also find that the greater the number of assets, the smaller the intensity of noise trading must be in order to induce insiders to disclose some information, and we find that insiders reveal all their information when the intensity of noise trading approaches zero.

We present the SPA framework, a novel approach to the modeling of the dynamics of portfolio default losses. In this framework, models are specified by a two-layer process. The first layer models the dynamics of portfolio loss distributions in the absence of information about default times. This background process can be explicitly calibrated to the full grid of marginal loss distributions as implied by initial CDO tranche values indexed on maturity, as well as to the prices of suitable options. We give sufficient conditions for consistent dynamics. The second layer models the loss process itself as a Markov process conditioned on the path taken by the background process. The choice of loss process is non-unique. We present a number of choices, and discuss their advantages and disadvantages. Several concrete model examples are given, and valuation in the new framework is described in detail. Among the specific securities for which algorithms are presented are CDO tranche options and leveraged super-senior tranches.

In this work we measure the evolution of the long-range dependence phenomenon of returns and volatilities of nominal British exchange rates (British pound against US dollar) futures contracts negotiated on the Chicago Mercantile Exchange from 1986 to 2004. The measurement employs the R/S classic analysis, Detrended Fluctuation Analysis and Generalized Hurst exponents, upon a 1008-observation window, which moves along the data. We obtain as a result, the effects of the 1992 European financial crisis on the measurements of the long-range dependency phenomenon. After the crisis the returns of this futures contract showed no signs of the long-range memory, which existed before the crisis. The volatility presented moderate long-range memory the whole time. We also test for long-memory in European currencies inside the European Monetary System and find evidence of moderate long memory, which suggests that being inside the EMS increases predictability.

In this paper, we show that an individual optimal credit rating exists for firms and empirically test whether firms strive to achieve their optimal rating. For this purpose, we consider the structural model by Leland [12], which balances the benefits of debt in the form of the tax-deductibility of interest payments against bankruptcy costs in order to obtain the optimal rating. Testable implications for both firms which have implemented their optimal rating and firms with non-optimal ratings are deduced. An empirical test with 420 firms contained in the S&P 500 Index indicates that all factors which theoretically drive optimal ratings also affect the observed rating in the predicted way. In line with our theory, observed ratings can be considerably better explained if, in addition to the traditional factors such as leverage and firm size, a proxy for bankruptcy costs and the default probability related to the optimal rating is considered. These findings suggest that U.S. firms contained in the S&P 500 Index strive to achieve their optimal credit ratings.