This section describes the benchmark structural VAR results to show that Flight to Safety shocks inflict a long and lasting impact on the business cycle variables.
The results from some investment-related variables compare well with the observed lack of investment growth in periods following the global financial crisis when economic pessimism or Flight to Safety was at higher levels. The section also shows how effective are various identification strategies in picking up innovations in Flight to Safety. It discusses the impact this identification has on beliefs about economic channels that guide investor behaviour during business cycles.
Let us first look at the identified structural shocks from benchmark VAR, which are plotted in Figure8 on page 51, where the shaded areas represent the peak to trough dates of NBER recessions. The identified shocks correspond well with recessionary periods. The scale and size of shocks in the 1990-91 recession is smaller than in the other two recessions of 2000-01 and 2007-09. Most inter-estingly, only the Flight to Safety shock displays the most significant jump of its entire history during the 2007-09 recession. This brings us back to the original purpose of identifying data series and events related to the unprecedented global financial crisis more than they do so with any other post-war recession. The initial assessment portrays FTS shock as a likely candidate.
We can further assess the usefulness of the vector autoregressions in the benchmark configuration of the model by looking at identified structural shocks that are quite orthogonal (see Figure 9 on page 52) and uncorrelated. Figure 9 shows the probability distribution of these shocks, the regression line of one shock on another, and the correlation coefficients between the identified shocks. The regression line and scatter plots display a lack of meaningful relationships between the shocks. Correlation coefficients for most of them are not significant. There is some linkage in identified TFP and FTS shocks as their correlation coefficient has a small negative slope, which shows that positive FTS shocks appear to be leading to adverse TFP shocks. We further explore this feature in the following section through the results of impulse responses to FTS shocks.
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Figure 8: Identified structural shocks
Notes: Standardised median structural shocks from 1983:Q1 to 2019:Q3 and their 68% confidence bands. Shocks are identified through the benchmark 5-variable VAR, using Sign and Zero restrictions discussed in Strategy 1. The 5 variables in that model are: TFP, Price of risk (Bond minus Equity price), Real rates, Surplus Ratio and Investments. The shaded areas represent peak to trough period of NBER recessions.
Respective shocks are: TFP, FTS, Monetary Policy, Demand and Residual.
Figure 9: Orthogonality of structural shocks
Notes: Regression (line) of one structural shock on another, and Probability distribution of structural shocks identified in benchmark 5 variable VAR using Identification strategy 1. r is Pearson’s correlation coefficient. p is Two-tailed p-value of the correlation coefficient. The 5 variables in the benchmark model are: TFP, Price of risk (Bond minus Equity price), Real rates, Surplus Ratio and Investments. Respective shocks are:
TFP, FTS, Monetary Policy, Demand and Residual.
5 . 1 E c o n o m i c c o n t r ac t i o n s f ro m F l i g h t t o S a f e t y 53
5.1 Economic contractions from Flight to Safety
In this section, the results from identification strategy 1 (as discussed in Table 3 on page41) for the 5 variable VAR with variables: Total factor productivity, Price of risk, Real rate, Surplus ratio, and Investments are discussed. Investments are a key variable of interest, and in later studies, it is replaced with other variables such as Output, Consumption, Hours, and CPI.
Figure 10 on page 54 plots the impulse responses from identified Flight to Safety shocks on all variables of the benchmark 5-variable VAR with Investments as the key variable that is unrestricted and considered agnostic in the identification strategy. Additionally, the figure also reports ‘Hours’ when replacing the variable
‘Investment’ in the benchmark model. Similarly, in Figure 11on page 56results of other variables of interests as they replace the fifth variable of the benchmark VAR are reported. The impulse response results for the first 4 variables in these alternate models are similar to those of the benchmark model where ‘Investment’
is the variable of interest. So, preserving space and time, they are only reported once, i.e., for the benchmark model.
The results (in Figure 10) show that the pessimism and risk aversion resulting from Flight to Safety shocks has a long term impact on the business cycle. The Bond minus Equity price (Price of risk) series jumps upon impact from FTS shocks. As described earlier, an unexpected increase in the price of a safe asset (10-year T-bond) vis-á-vis the price of a risky asset (S&P 500) symbolizes an increase in investors’ preference for safety over risky investments, or Flight to Safety. So as expected from sign and zero restrictions set out in Identification strategy 1, we witness an immediate jump in this series of 4-5%. An impact of this size cannot be fully generated from an increase in the safe asset price alone.
The yields on safe asset (bonds) are meager, and a 4-5% increase in prices (or a drop in yields of this magnitude) cannot be obtained without violating the Zero lower bound on bond yields. Therefore a steeper fall in S&P 500 index on impact from FTS shock is the primary driver of this sudden jump in the Price of risk series. Once the FTS shock has hit, it takes near 28 quarters for the series to return to its pre-shock levels. The identified FTS shock thus demonstrates a long term impact on the investors’ preference for safer assets.
Identification strategy 1 (discussed in table Table 3on page 41) restricts the response of Real rate to FTS shocks to be positive and Surplus ratio to be negative
Figure 10: Impulse responses of Benchmark VAR to FTS shocks
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Horizon 0.5
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