Attaining stochastic optimal control over debt ratios in U.S. markets
We propose a refined dynamic programming model based on a hidden Markov chain formulation and a nonlinear filtering technique to calculate the optimal debt ratio for public and private sectors for different scenarios. We then conduct the empirical analysis of the U.S. markets in real estate and equi...
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| Veröffentlicht in: | Review of quantitative finance and accounting 2023-10, Vol.61 (3), p.967-993 |
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| container_title | Review of quantitative finance and accounting |
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| creator | Liu, Wei-han |
| description | We propose a refined dynamic programming model based on a hidden Markov chain formulation and a nonlinear filtering technique to calculate the optimal debt ratio for public and private sectors for different scenarios. We then conduct the empirical analysis of the U.S. markets in real estate and equities during 1991.Q1 and 2020.Q1, comparing them with the theoretical results. It indicates that U.S. households and governments spent more than they can afford. While households reduced their debt ratio during times of economic distress, the public sector hiked its debt ratio to stimulate the economy. The policy effect took a long time to accumulate, and the outcome was lower than expected to revitalize the economy in time. |
| doi_str_mv | 10.1007/s11156-023-01173-0 |
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| source | Springer Journals; Business Source Complete |
| subjects | Accounting/Auditing Consumption Corporate Finance Debt Debt management Dynamic programming Econometrics Economic crisis Economics and Finance Finance GDP Government spending Gross Domestic Product Households International finance Markets Markov analysis Operations Research/Decision Theory Optimization techniques Original Research Partial differential equations Psychological distress Public sector Ratios Stochastic models |
| title | Attaining stochastic optimal control over debt ratios in U.S. markets |
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