The number and cost of claims that will arise from each policy of an insurance company's portfolio are unknown. In fact, there is a high degree of uncertainty on how much will ultimately be the cost of claims, not only during the period of inception but also after the contract termination, since there might be future, not yet reported, losses associated with past claims. Therefore, in practice, insurance companies have to protect themselves against the possibility of this ultimate cost by creating an additional reserve known as the incurred but not reported (IBNR) reserve. This work introduces new non-parametric models to IBNR estimation based on kernel methods; namely, support vector regression and Gaussian process regression. These are used to learn certain types of nonlinear structures present in claims data using the residuals produced by a benchmark IBNR estimation model, Mack's chain ladder. The proposed models are then compared to Mack's model using real data examples. Our results show that the three new proposed models are competitive when compared to Mack's benchmark model: they may produce the closest predictions of IBNR and also more accurate estimates, given that the variance for the reserve estimation, obtained through the bootstrap technique, is usually smaller than the one given by Mack's model.

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Recent macro-finance papers have documented the importance of adding information from macro variables in order to improve out-of-sample forecasting performance of bond yields. This paper aims at investigating the reasons for this success. We use Diebold and Li's dynamic version of the Nelson and Siegel exponential approximation of the yield curve to estimate the factors that govern its dynamics. Factors and macro variables are modeled simultaneously in a VAR framework, which is then used to forecast the factors. Our main conclusions are (i) this framework is useful in forecasting slope and curvature factors, but not the level factor; and (ii) to get good results in forecasting the level factor, one needs a macro model which incorporates variables related to long-run trends and expectations.

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