The paper uses the efficient hedging methodology in order to optimally price and hedge equity-linked life insurance contracts whose payoff depends on the performance of several risky assets. In particular, we consider a policy which pays the maximum of the values of n risky assets at some maturity date T, provided that the policyholder survives to T. Such contracts incorporate financial risk, which stems from the uncertainty about future prices of the underlying financial assets, and insurance risk, which arises from the policyholder's mortality. We show how efficient hedging can be used to minimize expected losses from imperfect hedging under a particular risk preference of the hedger. We also prove a probabilistic result, which allows one to calculate analytic pricing formulas for equity-linked payoffs with n risky assets. To illustrate its use, explicit formulas are given for optimal prices and expected hedging losses for payoffs with two risky assets. Numerical examples highlighting the implications of efficient hedging for the management of financial and insurance risks of equity-linked life insurance policies are also provided.

Model risk is a constant danger for financial economists using interest-rate forecasts for the purposes of monetary policy analysis, portfolio allocations, or risk-management decisions. Use of multiple models does not necessarily solve the problem as it greatly increases the work required and still leaves the question "which model forecast should one use?" Simply put, structural shifts or regime changes (not to mention possible model misspecifications) make it difficult for any single model to capture all trends in the data and to dominate all alternative approaches. To address this issue, we examine various techniques for combining or averaging alternative models in the context of forecasting the Canadian term structure of interest rates using both yield and macroeconomic data. Following Bolder and Liu (2007), we study alternative implementations of four empirical term structure models: this includes the Diebold and Li (2003) approach and three associated generalizations. The analysis is performed using more than 400 months of data ranging from January 1973 to July 2007. We examine a number of model-averaging schemes in both frequentist and Bayesian settings, both following the literature in this field (such as de Pooter, Ravazzolo and van Dijk (2007)) in addition to introducing some new combination approaches. The forecasts from individual models and combination schemes are evaluated in a number of ways; preliminary results show that model averaging generally assists in mitigating model risk, and that simple combination schemes tend to outperform their more complex counterparts. Such findings carry significant implications for central-banking analysis: a unified approach towards accounting for model uncertainty can lead to improved forecasts and, consequently, better decisions.

An objective function is a key component of a strategic portfolio management model used to determine the optimal allocations of assets and, possibly, their associated liabilities over some investment horizon. The author discusses investment philosophies and perspectives for the management of foreign reserves, and investigates how to translate the three common policy objectives for reserves (liquidity, safety, and return) into objective functions for strategic reserves management. Stochastic programming is identified as an advantageous modelling framework to capture the objectives of foreign reserves management, and a strategic reserves management model is illustrated that trades off expected net returns with costs and liquidity issues related to a potential liquidation of a portion of the portfolio. JEL classification: G11 Bank classification: Foreign reserves management RésuméUne fonction objectif est une composante essentielle d'un modèle stratégique de gestion de portefeuille visant à déterminer la répartition optimale des actifs et, éventuellement, des passifs connexes sur un horizon de placement donné. L'auteure se penche sur diverses philosophies de placement et des démarches adaptées à la gestion des réserves de change, et étudie la façon de traduire les trois objectifs poursuivis traditionnellement dans la gestion stratégique des réserves de change (soit la liquidité, la sûreté et le rendement) en des fonctions objectifs facilitant celle-ci. Elle établit que la programmation stochastique est un cadre de modélisation intéressant pour saisir ces objectifs et, pour illustrer son propos, elle présente un modèle stratégique de gestion des réserves qui permet d'exercer un arbitrage entre le rendement net attendu et les coûts et problèmes de liquidité associés à la liquidation potentielle d'une partie du portefeuille.