2018年-IMF国际货币组织全球_Accounting_for_Macrofinancial_Fluctuations_and_Turbulence_74页_1mb

Summary of IMF Working Paper: Accounting for Macrofinancial Fluctuations and Turbulence

Core Content

This paper explores the sources of macrofinancial fluctuations and turbulence in the world economy using an approximate linear dynamic stochastic general equilibrium (DSGE) model. The model incorporates structural shocks with asymmetric generalized autoregressive conditional heteroskedasticity (GARCH) effects, enabling the joint decomposition of macrofinancial variables' levels and volatilities into time-varying contributions from different types of shocks. The study focuses on the fifteen largest national economies, analyzing the role of risk premia shocks in driving output fluctuations and financial turbulence during episodes of financial condition swings.

Main Points

1. Model Overview

• The paper extends a DSGE model to account for macrofinancial fluctuations and turbulence.
• The model features:
  • Nominal and real rigidities
  • Extensive macrofinancial linkages
  • Diverse spillover transmission channels
• It includes asymmetric GARCH effects in structural shocks, offering a more nuanced understanding of volatility dynamics.

2. Key Findings

• Strong evidence of asymmetric ARCH effects is found, suggesting that the volatility of macrofinancial variables is not symmetric in response to shocks.
• Risk premia shocks are identified as a major contributor to cyclical output fluctuations and financial turbulence during abrupt swings in financial conditions.
• These shocks are particularly significant in economies affected by major financial crises, including:
  • The Global Financial Crisis
  • The Euro Area Sovereign Debt Crisis
  • The Taper Tantrum

3. Household Sector

• A continuum of households is considered, differentiated by:
  • Credit constraints
  • Saving behavior
• Households are categorized into:
  • Bank intermediated (Z = B): Access to domestic banks and property markets
  • Capital market intermediated (Z = A): Access to domestic and foreign capital markets
  • Credit constrained (Z = C): No access to banks or capital markets
• Households optimize intertemporal utility functions that incorporate:
  • Consumption and housing
  • Labor supply
  • Real property balances
  • Real portfolio balances
• The dynamic budget constraint is used to model how households allocate their wealth across different assets.

4. Bank Intermediated Households

• Households have capitalist spirit motives and diversification motives for holding real property balances.
• Preferences are modeled using constant elasticity of substitution (CES) subutility functions.
• The intertemporal optimality condition ensures that the expected present value of the gross real property return is equal to one.
• The intratemporal optimality condition equates the marginal rate of substitution between housing and consumption to the real rental price of housing.
• The risk-adjusted return optimality condition links the expected present values of bank deposits and real estate returns.

5. Capital Market Intermediated Households

• Households have capitalist spirit motives and diversification motives for holding real portfolio balances.
• The model includes short term bonds, long term bonds, and stocks as financial assets.
• Preferences over these assets are also modeled using CES subutility functions.
• The model incorporates duration risk premium shocks and equity risk premium shocks.
• The intertemporal optimality condition ensures that the expected present value of the gross real portfolio return is equal to one.

6. Empirical Framework

• The paper uses endogenous and exogenous variables to analyze macrofinancial fluctuations.
• Conditional means and conditional variances are estimated to capture the dynamic behavior of macrofinancial variables.
• Structural shocks are decomposed into their contributions to output levels and volatility.
• The GARCH model is used to estimate the asymmetric volatility effects.

7. Estimation and Inference

• Data transformations are applied to the empirical framework.
• The estimation procedure is outlined, and calibrated and estimated parameters are provided.
• Model specification tests and structural shock conditional variances are presented in tables and figures.
• Historical decompositions of output and financial conditions are analyzed, highlighting the role of shock contributions over time.

Key Information

• JEL Classification Numbers: C50; E30; E40; E52; E62; F40; G10; G21; G28
• Keywords: Macrofinancial analysis; World economy; Dynamic stochastic general equilibrium model; Autoregressive conditional heteroskedasticity
• Author: Francis Vitek
• Email: FVitek@imf.org
• Date: November 2018
• Department: Monetary and Capital Markets Department

Conclusion

The paper concludes that risk premia shocks play a disproportionate role in cyclical output fluctuations and financial turbulence, particularly during financial condition swings. It emphasizes the importance of incorporating asymmetric GARCH effects in DSGE models for a more accurate representation of macrofinancial dynamics. The study also highlights the need for further research into the interactions between macrofinancial variables and policy responses.

Figures and Tables

• Figure 1: Conditional variances of structural shocks
• Figure 2: Historical decompositions of output
• Figure 3: Historical decompositions of financial conditions
• Figure 4: Historical decompositions of output volatility
• Figure 5: Historical decompositions of financial conditions volatility
• Table 1: Model specification test results
• Table 2: Calibrated parameter values
• Table 3: Estimated parameter values

This model provides a comprehensive framework for understanding macrofinancial fluctuations and turbulence, offering insights into the role of risk premia shocks and asymmetric volatility in shaping economic outcomes.