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Conservatorship, quantitative easing, and mortgage spreads: a new multi-equation score-driven model of policy actions

  • Szabolcs Blazsek EMAIL logo , Virag Blazsek and Adam Kobor
Published/Copyright: March 31, 2022
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Studies in Nonlinear Dynamics & Econometrics
From the journal Studies in Nonlinear Dynamics & Econometrics Volume 27 Issue 2

Abstract

In this paper, the effects of United States (US) policy actions on mortgage-backed security and mortgage loan spreads are measured, by using data before, during, and after the US subprime mortgage crisis. We study the effects of the following policy actions: (i) the placement of Fannie Mae and Freddie Mac into US Government conservatorship; (ii) the US Federal Reserve quantitative easing (QE) programs. We provide the following contributions to the literature: (i) for a robust measurement of policy effects, a new multi-equation score-driven t-QVARMA (quasi-vector autoregressive moving average) model is used. (ii) In addition to the measurement of the effects of QE, the effects of government conservatorship are also measured in this paper. (iii) Furthermore, the data period of the relevant literature is extended to the period of June 1998 to March 2020.

Keywords: dynamic conditional score (DCS); generalized autoregressive score (GAS); government conservatorship; government-sponsored enterprises (GSEs); mortgage-backed securities (MBSs); quantitative easing (QE) programs
JEL Classification: C32; C52; E52; E58; G21; G28
Corresponding author: Szabolcs Blazsek, School of Business, Universidad Francisco Marroquín, Guatemala City 01010, Guatemala, E-mail:

Acknowledgments

The authors greatly appreciate the helpful comments of Matthew Copley. All remaining errors are our own. No potential conflict of interest is reported by the authors. Data and computer codes are available from the authors upon request. Szabolcs Blazsek acknowledges funding from Universidad Francisco Marroquin. The material in this article does not reflect the positions of any of the institutions to which the authors are affiliated.

  1. Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: None declared.

  3. Conflict of interest statement: The authors declare no conflicts of interest regarding this article.

Appendix
Figure A1: Time series components μ t † + μ t ° ${\mu }_{t}^{{\dagger}}+{\mu }_{t}{}^{\circ}$ , μ t * ${\mu }_{t}^{\ast }$ , and v t for (M1) Fannie Mae MBS spread, (M2) Freddie Mac MBS spread, and (M3) Ginnie Mae MBS spread. Notes: Φ* and Ψ* are diagonal matrices. The unconditional mean of y t is measured by μ t † + μ ° t ${\mu }_{t}^{{\dagger}}+{\mu {}^{\circ}}_{t}$ . The estimates are for the specification of Table 3, panel A.
Figure A1:

Time series components μ t † + μ t ° , μ t * , and v t for (M1) Fannie Mae MBS spread, (M2) Freddie Mac MBS spread, and (M3) Ginnie Mae MBS spread. Notes: Φ* and Ψ* are diagonal matrices. The unconditional mean of y t is measured by μ t † + μ ° t . The estimates are for the specification of Table 3, panel A.

Figure A2: Time series components μ t °, μ t * ${\mu }_{t}^{\ast }$ , and v t for (L1) Freddie Mac lending spread, (L2) fixed lending spread, and (L3) jumbo lending spread. Notes: Φ* and Ψ* are diagonal matrices; μ t † = 0 3 × 1 ${\mu }_{t}^{{\dagger}}={0}_{3\times 1}$ . The unconditional mean of y t is measured by μ° t . The estimates are for the specification of Table 4, panel A.
Figure A2:

Time series components μ t °, μ t * , and v t for (L1) Freddie Mac lending spread, (L2) fixed lending spread, and (L3) jumbo lending spread. Notes: Φ* and Ψ* are diagonal matrices; μ t † = 0 3 × 1 . The unconditional mean of y t is measured by μ° t . The estimates are for the specification of Table 4, panel A.

Figure A3: Robustness of the scaled score function to extreme values for (M1) Fannie Mae MBS spread, (M2) Freddie Mac MBS spread, and (M3) Ginnie Mae MBS spread. Notes: v3,t = 0 is assumed for this figure. The estimates are for Table 3, panel B.
Figure A3:

Robustness of the scaled score function to extreme values for (M1) Fannie Mae MBS spread, (M2) Freddie Mac MBS spread, and (M3) Ginnie Mae MBS spread. Notes: v3,t = 0 is assumed for this figure. The estimates are for Table 3, panel B.

Figure A4: Robustness of the scaled score function to extreme values for (L1) Freddie Mac lending spread, (L2) fixed lending spread, and (L3) jumbo lending spread. Notes: v3,t = 0 is assumed for this figure. The estimates are for Table 4, panel B.
Figure A4:

Robustness of the scaled score function to extreme values for (L1) Freddie Mac lending spread, (L2) fixed lending spread, and (L3) jumbo lending spread. Notes: v3,t = 0 is assumed for this figure. The estimates are for Table 4, panel B.

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Received: 2021-07-03
Revised: 2021-10-24
Accepted: 2022-01-24
Published Online: 2022-03-31

© 2022 Walter de Gruyter GmbH, Berlin/Boston

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  2. Research Articles
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https://doi.org/10.1515/snde-2021-0066
Keywords for this article
dynamic conditional score (DCS); generalized autoregressive score (GAS); government conservatorship; government-sponsored enterprises (GSEs); mortgage-backed securities (MBSs); quantitative easing (QE) programs

Articles in the same Issue

  1. Frontmatter
  2. Research Articles
  3. Asymmetry in stochastic volatility models with threshold and time-dependent correlation
  4. Financial crisis spread, economic growth and unemployment: a mathematical model
  5. Article
  6. Augmenting the Realized-GARCH: the role of signed-jumps, attenuation-biases and long-memory effects
  7. Research Articles
  8. Unrestricted, restricted, and regularized models for forecasting multivariate volatility
  9. Controlling chaos in New Keynesian macroeconomics
  10. Conservatorship, quantitative easing, and mortgage spreads: a new multi-equation score-driven model of policy actions
  11. Expected, unexpected, good and bad aggregate uncertainty
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