Computation of Multifactor Endogeneous Mortgage Rates

多因素内生抵押贷款利率的计算

• 批准号: 0703849
• 负责人: Giray Okten
• 金额: $ 10.24万
• 依托单位: Florida State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-08-01 至 2009-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0703849&HistoricalAwards=false
• 关键词: Computation; Multifactor; Endogeneous; Mortgage; Rates

The research objective is to develop a multifactor endogenous mortgage rate model. To obtain accurate predictions of the mortgage rate, a model needs to consider many state factors such as multiple factor interest rates, real estate prices, unemployment rates, etc. The conventional approach to solve such a multifactor model suffers from the curse of dimensionality and is, therefore, computationally intractable. This research will develop a computationally tractable algorithm by a fundamental shift in the formulation of the problem: instead of computing the mortgage rate that corresponds to a multidimensional state factor, the algorithm computes the set of state factors that correspond to a given mortgage rate. This approach eliminates the curse of dimensionality: the complexity of the algorithm is independent of the number of factors used to model the mortgage rate. The research will develop regression based Monte Carlo methods and a new randomized quasi-Monte Carlo sequence to implement the algorithm.The accurate modeling of the mortgage rates, and thus accurate modeling of the prepayment behavior, improves the stability of the financial markets, reduces risk for mortgage investors, and, consequently, lowers mortgage rates for homeowners. At present, the common approach to model mortgage rates by the financial industry is based on a very simple heuristic: the 10-year Treasury yield plus an exogenous constant. Recent empirical evidence suggests that this heuristic is seriously flawed in certain economic situations. This research will develop a computationally efficient algorithm to solve an accurate mortgage rate model.

研究目标是开发多因素内源性抵押贷款率模型。为了获得对抵押贷款利率的准确预测，一个模型需要考虑许多国家因素，例如多重因素利率，房地产价格，失业率等。解决这种多因素模型的常规方法遭受了维数的诅咒，因此在计算上是可纠缠的。这项研究将通过问题制定的基本转移来开发一种可以计算障碍的算法：而不是计算与多维状态因子相对应的抵押贷款率，而是计算与给定抵押贷款率相对应的一组状态因素。这种方法消除了维度的诅咒：算法的复杂性与用于建模抵押贷款率的因素的数量无关。该研究将开发基于回归的蒙特卡洛方法和新的随机准蒙特卡洛序列，以实施算法。抵押贷款利率的准确建模，从而准确地建模预付款行为，可改善金融市场的稳定性，降低抵押贷款投资者的风险，从而降低负责人的抵押费率。目前，金融业对抵押贷款利率进行建模的通用方法是基于一个非常简单的启发式方法：10年的财政收益率加上外源性常数。最近的经验证据表明，在某些经济情况下，这种启发式措施存在严重缺陷。这项研究将开发一种计算有效的算法，以解决准确的抵押贷款率模型。