论文标题
在商业估值模型中，卡尔曼过滤估值风险的降低
Reduction of valuation risk by Kalman filtering in business valuation models
论文作者
论文摘要
提出了一种递归的自由现金流模型（FCFF），以确定公司在有效市场中的公司价值，在该市场中，新市场和特定于公司的信息以添加剂白噪声为模型。 FCFF模型的随机方程明确求解，以获得平均公司价值和估值风险。可以指出的是，可以通过在递归FCFF模型中实施常规的两步卡尔曼过滤器来大大降低估值风险，从而提高其预测能力。通过测量残差检测，卡尔曼滤波器的系统错误是由风险中间变化以及加权平均资本成本（WACC）引起的。通过在常规的Kalman滤波算法中包含一个附加的调整步骤，可以表明可以通过递归调整WACC来消除系统错误。通过Monte Carlo模拟测试了三步自适应Kalman滤波器的性能，该模拟证明了针对系统错误的可靠性和鲁棒性。还可以证明，可以将常规和自适应的卡尔曼过滤算法实施到其他估值模型中，例如经济增值模型（EVA）和自由现金流到Equity模型（FCFE）。
A recursive free cash flow model (FCFF) is proposed to determine the corporate value of a company in an efficient market in which new market and company-specific information is modelled by additive white noise. The stochastic equations of the FCFF model are solved explicitly to obtain the average corporate value and valuation risk. It is pointed out that valuation risk can be reduced significantly by implementing a conventional two-step Kalman filter in the recursive FCFF model, thus improving its predictive power. Systematic errors of the Kalman filter, caused by intermediate changes in risk and hence in the weighted average cost of capital (WACC), are detected by measuring the residuals. By including an additional adjustment step in the conventional Kalman filtering algorithm, it is shown that systematic errors can be eliminated by recursively adjusting the WACC. The performance of the three-step adaptive Kalman filter is tested by Monte Carlo simulation which demonstrates the reliability and robustness against systematic errors. It is also proved that the conventional and adaptive Kalman filtering algorithms can be implemented into other valuation models such as the economic value added model (EVA) and free cash flow to equity model (FCFE).
