一类随机保费下双险种风险模型的破产分布

研究了一类随机保费下带常利率的特殊双险种风险模型的破产问题,得到了该模型下破产概率和生存概率的递推表达式及所满足的积分方程。     

Forecasting with latent structure time series models: an application to nominal interest rates

In this paper we develop a latent structure extension of a commonly used structural time series model and use the model as a basis for forecasting. Each unobserved regime has its own unique slope and variances to describe the process generating the data, and at any given time period the model predicts a priori which regime best characterizes the data. This is accomplished by using a multinomial logit model in which the primary explanatory variable is a measure of how consistent each regime has been with recent observations. The model is especially well suited to forecasting series which are subject to frequent and/or major shocks. An application to nominal interest rates shows that the behaviour of the three‐month US Treasury bill rate is adequately explained by three regimes. The forecasting accuracy is superior to that produced by a traditional single‐regime model and a standard ARIMA model with a conditionally heteroscedastic error. Copyright © 1999 John Wiley & Sons, Ltd.     

Choosing the Number of Conditioning Events in Judgemental Forecasting

One method for judgemental forecasting involves the use of decomposition; i.e. estimating the conditional means of an unknown quantity of interest for a finite number of conditioning events, and weighting these estimated conditional means by the estimated marginal probabilities of the corresponding conditioning events. In this paper we investigate how the level of decomposition (i.e. the number of conditioning events) affects the precision of the resulting forecast. Previous analyses assume that key parameters (the informativeness of the decomposition, and the precision of estimation for the conditional means and the marginal probabilities) remain constant as the number of conditioning events increases. However, this assumption is unreasonable, and for some parameters mathematically impossible; the values of these parameters are likely to change significantly even for small numbers of conditioning events. Therefore, we introduce models for how these key parameters may depend on the level of decomposition. We then investigate the implications of these models for the precision of the resulting forecast. In particular, we identify cases in which decomposition is never desirable, always desirable, or desirable only near the optimal number of conditioning events. This second case was not observed previously. We focus throughout on the situation likely to be of greatest interest in practice; namely, the behaviour of decomposition for relatively small numbers of conditioning events.© 1997 John Wiley & Sons, Ltd.