We create a time series model for annual returns of three asset classes: the USA Standard & Poor (S&P) stock index, the international stock index, and the USA Bank of America investment-grade corporate bond index. Using this, we made an online financial app simulating wealth process. This includes options for regular withdrawals and contributions. Four factors are: S&P volatility and earnings, corporate BAA rate, and long-short Treasury bond spread. Our valuation measure is an improvement of Shiller's cyclically adjusted price-earnings ratio. We use classic linear regression models, and make residuals white noise by dividing by annual volatility. We use multivariate kernel density estimation for residuals. We state and prove long-term stability results.
翻译:我们构建了三个资产类别年度收益率的时间序列模型:美国标准普尔(S&P)股票指数、国际股票指数以及美国银行投资级公司债券指数。基于此模型,我们开发了一款模拟财富过程的在线金融应用程序,该程序包含定期提取与定期投入功能。模型包含四个因子:标普波动率与收益、公司BAA利率以及长短期国债利差。我们的估值测度是对席勒周期性调整市盈率的改进。我们采用经典线性回归模型,并通过除以年度波动率使残差序列白噪声化。对残差采用多元核密度估计方法。我们提出并证明了长期稳定性结果。