This section shows that affine Gaussian DTSMs and TTSMs with constant variance are indistinguishable, but that an extended affine TTSM with flexible second moments captures
time-varying volatility and correlations of yields. Cieslak and Povala (2011) show that even a 4-factor A1(4) does not capture these stylized facts.
5.1 Model nomenclature
We consider term-structure models with K = 3 risk factors, where the matrix of portfolio weights in Equation (35) is obtained from principal component analysis applied to our sample of yields. Figure 6 shows the time series of risk factors.17 We estimate three classes of affine models. First, we estimate the standard 3-factor Gaussian DTSM using the canonical form in Joslin, Singleton, and Zhu(2011), which we label A (this corresponds to the A0(3) model in Dai and Singleton, 2000). This model imposes two important restrictions: (i) the computation of bond prices under the risk-neutral measure connects the mean and variance of the risk factors via the pricing equation, and (ii) the variances and correlations of the risk factors are constant Σt = Σ. Second, we estimate an affine version of our 3-factor TTSM with constant variance, which we label AT (affine TTSM) and an extended version with unrestricted EGARCH and DCC dynamics (Equations (40-42)), which we label AT V (affine TTSM with time-varying variance). In all cases, the short-rate equation is affine (i.e.,
θ → ∞). We also exclude macro variables in this section for comparability with existing results. We estimate non-affine versions of these models, with and without macro variables, in the following section.
1990 1995 2000 2005 2010 2015
−5 0 5 10
Time
PC
PC1 PC2 PC3
Figure 6: Pricing portfolios: Monthly data from January 1990 to December 2014.
17Inspection of the weights (not reported) reveal that these portfolios have the standard interpretation in terms of level, slope and curvature factors.
5.2 Fitting yields and bond risk premiums
Table 2 provides summary statistics on the pricing errors from models A, AT and AT V . These models are indistinguishable based on pricing errors. Since the pricing factors are the same across these models, the small differences between pricing errors imply that factor loadings are similar across models. Figure 17in the appendix compares the factor loadings, Bn,P. The differences are so small that we cannot distinguish them visually.
A AT AT V
RMSE ME RMSE ME RMSE ME
1yr 6.1 -1.5 6.1 -1.5 6.1 -1.5
3yr 2.5 -0.7 2.5 -0.7 2.5 -0.7
5yr 2.3 -0.4 2.3 -0.4 2.3 -0.4
10yr 3.0 -1.0 3.0 -1.0 3.0 -1.1
Table 2: Yield pricing errors from models A, AT and AT V are indistinguishable. Root-mean-squared error (RMSE) and mean error (ME) in annualized basis points.
Following Dai and Singleton (2002), we use Campbell-Shiller (CS) regressions (Campbell and Shiller, 1991) to next check whether each model captures variations in the bond risk premium. Figure7 shows OLS estimates of the coefficients from CS predictability regressions in our sample alongside estimates derived from three term-structure models. We repeat the exercise for monthly and quarterly returns. In both cases, the model-implied coefficients are remarkably close to each other.18
Summing up, the linear model, with constant variance, constructed based on an explicit no-arbitrage argument, is indistinguishable from a linear model with constant variance constructed within our new framework. This was expected. Moving between models A and AT , the only difference is the introduction of the Jensen term in the equation for An,X. Figure 2 shows that this term is very small. Then, moving between models AT and AT V , the pricing equations for yields are identical except for differences in parameter estimates.
But the estimates for δ0 and K are nearly identical across models (the factor structure in the cross-section of yields is measured very precisely).
18The A0(3) model does not match the OLS coefficients as closely as reported byDai and Singleton(2002) in a sample from 1970 to 1995. Noticeably, the expectation hypothesis is not rejected for the shortest maturity in our sample, which could be due to the FOMC’s increased transparency since the early 1990s.
(a) 1-month
Figure 7: Model-implied Campbell-Shiller coefficients are close to each other. Results from monthly and quarterly CS predictability regressions estimated with OLS and coefficients implied from different 3-factor models: A, AT and AT V . Monthly data, December 1990 to June 2008.
5.3 Volatilities and correlations of yields
Existing affine DTSMs face a well-known tension in fitting the cross-section and the (time-series) variance of yields. This section assesses whether the AT V model can also capture the stylized facts of the variances and correlations of yields. Figure 8 compares model-implied conditional volatilities with EGARCH volatility estimates for the 1-year and 5-year yields in Panels a and b, respectively.19 Our framework produces a close fit to the conditional volatility of yields. The fit from the AT V model is remarkably close throughout the sample and across yield maturities.
The results show that the volatilities of yield peak at times when the Federal Reserve is loosening its target rate in the midst of a recession. We also find significant variation across the term structure of yield volatility. To see this, Figure 8compares the model-implied volatility for the 1-month, 1-year and 10-year yields (Panels cand d). The volatility of the 1-year yield is generally higher than the volatility at both the shorter and longer maturities (i.e., the red line generally sits on top of the others), but this hump in the term structure of
volatility varies substantially over the sample.
Figure 9 reports the difference between the volatility of 1-year and 1-month yields, as implied by the AT V 3-factor model. This provides a direct measure of the term structure hump. The hump declines substantially in 1991, in 2001 and in 2007, which correspond to the peaks in the level of volatility in Figure 8.
19The EGARCH is used as a benchmark byKim and Singleton(2012) andJoslin(2014). For comparability between models, the yield innovations in the unrestricted EGARCH(1,1) are computed relative to the projection of current yields on the lagged principal components, as in the VAR(1).
Necessarily, variations in the humps are entirely driven by the time-varying volatility and correlation of the risk factors, since the factor loadings are constant. What is less evident, and still undocumented (to the best of our knowledge), is that the changes in the volatility term structure imply that the nature and explanatory power of the principal components of yields change over time. Figure10shows the explanatory power of the first two (conditional) components at each date. We stress that these components do not correspond to the unconditional (full-sample) principal components used to construct the risk factors Pt.20 The importance of the level factor varies between 85 and 95 percent over the sample.
By construction, the explanatory power of the other components must vary in the opposite direction. We find that the importance of the slope factor rises by as much as 10 percent in absolute terms (to 15 percent) in these episodes where the short rate is relatively more volatile, as in Figure9 (the hump shape in volatility is less pronounced).
Our model captures the following stylized facts about the yield volatility term structure.
First, the early stages of a recession are characterized by lower yields (higher bond prices) but higher volatility.21Second, we confirm that the volatility of yields exhibits a downward-sloping term structure but with a hump shape around the maturity of one or two years (Piazzesi, 2005). Third, the volatility of the short rate increases relative to other rates during episodes where the Federal Reserve loosens its policy rate (consistent with Cieslak and Povala, 2015).
Finally, the explanatory power of the first conditional principal component falls in these episodes, but the explanatory power of the second component rises. Note that the conditional volatility implied by our model is not spanned by contemporaneous yields, which is consistent with Collin-Dufresne, Goldstein, and Jones (2009), but derived from the history of shocks to the yield curve using the EGARCH-DCC filter.