Ein Reservierungsverfahren für die Rechtsschutzversicherung nach Art der Lebensversicherung

In this paper we introduce a new approach to the calculation of claims reserves (known and IBNR cases) which is particularly adapted to the business model of legal expense insurance. An essential aspect here is the split into two model components: case numbers and average claim costs. In contrast to other reserving methods for case numbers and claims cash flows which are frequently used in practice without checking the validity for application we introduce a model in which the time until case settlement is described by a lifetime distribution according to the principles of life insurance. The split of model components also allows for a simple implementation of cost inflation effects which is required by German law. Finally, the approach proposed here can readily be transferred to the calculation of IBNR reserves.     

The characteristics of informed trading: Implications for asset pricing

This paper investigates the linkage of microstructure, accounting, and asset pricing. We determine the relationship between firm characteristics as captured by accounting and market data and a firm's probability of private information-based trade (PIN) as estimated from trade data. This allows us to determine what types of firms have high information risk. We then use these data to create an instrument for PIN, the PPIN, which we can estimate from firm-specific data. We show that PPINs have explanatory power for the cross-section of asset returns in long sample tests. We also investigate whether information risk vitiates the influence of other variables on asset returns. We develop a PPIN factor and show that it dominates the Amihud factor in asset returns. Our results provide strong support for information risk affecting asset returns in long sample tests.     

Is Information Risk a Determinant of Asset Returns?

We investigate the role of information–based trading in affecting asset returns. We show in a rational expectation example how private information affects equilibrium asset returns. Using a market microstructure model, we derive a measure of the probability of information–based trading, and we estimate this measure using data for individual NYSE–listed stocks for 1983 to 1998. We then incorporate our estimates into a Fama and French (1992) asset–pricing framework. Our main result is that information does affect asset prices. A difference of 10 percentage points in the probability of information–based trading between two stocks leads to a difference in their expected returns of 2.5 percent per year.     

A Trade-Based Analysis of Momentum

This article uses transactions data for all NYSE/AMEX stocksin the period 1983–2002 to study how investors trade inJegadeesh and Titman’s (1993) momentum portfolios. Amongsmall trades, there is an extremely sluggish reaction to thepast returns. For instance, an initial small-trade buying pressureexists for loser stocks, and it gradually converts into an intenseselling pressure over the following year. The results are consistentwith initial underreaction followed by delayed reaction amongsmall traders. Moreover, small-trade imbalances during the formationperiod significantly affect momentum returns, suggesting thatunderreaction among small traders contributes to the momentumeffect. Large traders, by contrast, show no evidence of underreaction,and large-trade imbalances have little impact on subsequentreturns. Overall, the results suggest that momentum could partlybe driven by the behavior of small traders.