Parameter Estimation Methods of Required Rate of Return

ArXiv ID: 2305.19708 “View on arXiv”

Authors: Unknown

Abstract

In this study, we introduce new estimation methods for the required rate of returns on equity and liabilities of private and public companies using the stochastic dividend discount model (DDM). To estimate the required rate of return on equity, we use the maximum likelihood method, the Bayesian method, and the Kalman filtering. We also provide a method that evaluates the market values of liabilities. We apply the model to a set of firms from the S&P 500 index using historical dividend and price data over a 32–year period. Overall, the suggested methods can be used to estimate the required rate of returns.

Keywords: Dividend Discount Model, Required Rate of Return, Kalman Filter, Maximum Likelihood Estimation, Bayesian Method

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 5.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced stochastic models like Markov-Switching VAR, Kalman filtering, and Bayesian estimation, which indicates high mathematical complexity. The empirical application uses 32 years of S&P 500 data to test the methods, showing solid backtesting and data implementation.

  flowchart TD
    A["Research Goal: Estimate Required Rate of Return"] --> B["Input: Historical Data"]
    B --> C["Stochastic Dividend Discount Model DDM"]
    C --> D["Estimation Methods"]
    D --> E["Computational Processes"]
    E --> F["Key Findings Outcomes"]
    subgraph D ["Estimation Methods"]
        D1["Maximum Likelihood"]
        D2["Bayesian Method"]
        D3["Kalman Filtering"]
        D4["Liability Valuation"]
    end
    subgraph E ["Computational Processes"]
        E1["Estimate R_e Equity"]
        E2["Estimate R_d Debt"]
        E3["32-Year Application"]
    end
    subgraph F ["Key Findings Outcomes"]
        F1["Validated Methods"]
        F2["Applicable to Private Public Firms"]
        F3["S&P 500 Results"]
    end