论文标题
保险估值：两步的广义回归方法
Insurance valuation: A two-step generalised regression approach
论文作者
论文摘要
当前的保险公平估值方法通常遵循两步的方法，将二次对冲与对剩余责任的风险措施相结合，以获得资本成本利润率。在这种方法中，监管风险措施代表的偏好不反映在对冲过程中。我们基于广义回归论证的替代两步套期保值程序来解决这个问题，这导致对风险措施（例如危险价值或期望值）中立的投资组合。首先，旨在复制责任的交易资产组合由当地二次对冲决定。其次，使用替代目标函数对剩余责任进行对冲。然后将风险利润定义为对冲剩余责任所需的资本成本。在第二步中使用了分数回归，每年的偿付能力约束自然满足；此外，投资组合是满足此类限制的所有对冲投资组合中的风险最小化。我们提出了一种基于向后迭代方案的保险责任估值和对冲的神经网络算法。该算法相当笼统且易于适用，因为它仅需要模拟风险驱动因素的途径。
Current approaches to fair valuation in insurance often follow a two-step approach, combining quadratic hedging with application of a risk measure on the residual liability, to obtain a cost-of-capital margin. In such approaches, the preferences represented by the regulatory risk measure are not reflected in the hedging process. We address this issue by an alternative two-step hedging procedure, based on generalised regression arguments, which leads to portfolios that are neutral with respect to a risk measure, such as Value-at-Risk or the expectile. First, a portfolio of traded assets aimed at replicating the liability is determined by local quadratic hedging. Second, the residual liability is hedged using an alternative objective function. The risk margin is then defined as the cost of the capital required to hedge the residual liability. In the case quantile regression is used in the second step, yearly solvency constraints are naturally satisfied; furthermore, the portfolio is a risk minimiser among all hedging portfolios that satisfy such constraints. We present a neural network algorithm for the valuation and hedging of insurance liabilities based on a backward iterations scheme. The algorithm is fairly general and easily applicable, as it only requires simulated paths of risk drivers.
