Finance and Economics Discussion Series: 2013-63 Screen Reader version ^{♣}

PRELIMINARY DRAFT

Abstract:

This paper develops a method to approximate arbitrage-free bond yields within a term structure model in which the short rate follows a Gaussian process censored at zero (a "shadow-rate model" as proposed by Black (1995)). The censoring ensures that model-implied yields are
constrained to be positive, but it also introduces non-linearity that renders standard bond pricing formulas inapplicable. In particular, yields are not linear functions of the underlying state vector as they are in affine term structure models (see Piazzesi, 2010). Existing approaches
towards computing yields in shadow-rate models suffer from high computational burden or low accuracy. In contrast, I show that the technique proposed in this paper is sufficiently fast for single-step estimation of a three-factor shadow-rate term structure model, and sufficiently accurate to
evaluate yields to within approximately half a basis point.

1 Introduction

In late-2008, short-term nominal interest rates in the U.S. fell to their effective "zero lower bound" (see Bernanke et al., 2004 ). Since standard Gaussian term structure models do not rule out the possibility of negative model-implied yields, they provide a poor approximation to the behavior of nominal yields when the lower bound is binding (Kim and Singleton, 2012; Christensen and Rudebusch, 2013; Bauer and Rudebusch, 2013). Kim and Singleton (2012) find that shadow-rate models in the spirit of Black (1995) successfully capture yield-curve properties observed near the zero lower bound. However, arbitrage-free multi-factor versions of these models tend to be computationally intractable (Christensen and Rudebusch, 2013). Gorovoi and Linetsky (2004) show that bond prices in a one-factor shadow-rate model can be computed analytically by an eigenfunction expansion, but their approach does not generalize to multiple dimensions. Kim and Singleton (2012) and Ichiue and Ueno (2007) successfully estimate shadow-rate models with up to two factors, but they compute bond prices using discretization schemes that are subject to the curse of dimensionality. Christensen and Rudebusch (2013) use a yield formula proposed by Krippner (2012) to estimate shadow-rate Nelson-Siegel models with up to three factors, but 's derivation deviates from the usual no-arbitrage approach. Bauer and Rudebusch (2013) evaluate bond prices by Monte Carlo simulation for given model parameters from an unconstrained Gaussian term structure model, but they do not estimate a shadow-rate version of the model due to the computational burden.

This paper develops and applies a new technique for fast and accurate approximation of arbitrage-free zero-coupon bond yields in multi-factor Gaussian shadow-rate models of the term structure of interest rates. The computational complexity of the method does not increase with the number of yield curve factors, and, empirically, it produces yields that are accurate to within about half a basis point. The method is sufficiently fast to estimate a flexible, arbitrage-free, three-factor term structure model in which the shadow rate follows a Gaussian process. For illustration purposes, I estimate such a model by quasi-maximum likelihood on a sample of U.S. Treasury yields, using the unscented Kalman filter to account for the non-linear mapping between factors and yields.

2 Model

Consider first the standard, continuous-time N-factor Gaussian term structure model. In particular, let be -dimensional standard Brownian motion on a complete probability space with canonical filtration . Assume there is a pricing measure on that is equivalent to , and denote by Brownian motion under as derived from Girsanov's Theorem (Karatzas and Shreve, 1991). Suppose latent factors (or states) representing uncertainty underlying term-structure securities follow the multivariate Ornstein-Uhlenbeck process

were . Let the short rate be

Then by definition, the arbitrage-free time price of a zero-coupon bond maturing at time is given by

with associated zero-coupon bond yield

Bond prices (and hence yields) can equivalently be defined in terms of forward rates:

where denotes the instantaneous time forward rate effective at time .

Since is a Gaussian process (Karatzas and Shreve, 1991), it follows from (2) that the short rate takes on strictly negative values with strictly positive probability. To modify the model in a way that accounts for the zero lower bound on nominal yields, Black (1995) proposes to think of as a *shadow* short rate (and, analogously, of ,
and as shadow bond price, shadow yield, and shadow
instantaneous forward rate, respectively) and define the *observed* short rate as the shadow rate censored at zero:

With the observed short rate in place of the shadow rate , the observed bond price , yield , and instantaneous forward rate are then defined as in (3)-(5).

The theoretical argument for a lower bound *at zero* on the nominal short rate (and hence on nominal yields) is based on arbitrage between bonds and currency Black (1995). In practice, the two assets may not be perfect substitutes for reasons such as
convenience, default risk, or legal requirements. This may push the empirical lower bound into slightly negative or slightly positive territory. The derivations in Section 2 are easily modified to accommodate a lower bound at
. In particular, suppose

The last term is equal to the expression for the yield when the lower bound is zero, except that is subtracted from the shadow rate. Therefore, since , when the lower bound is nonzero we can compute zero-coupon yields as if the bound were zero, with in place of , and then add to the final result. The lower bound can be set to a specific value based on a priori reasoning, or treated as a free parameter in estimation.

A central task in term-structure modeling is the (analytical and/or numerical) computation of arbitrage-free bond prices (and hence yields) based on equation (3). Section 4.1 reviews the standard approach using differential equations formalized by Duffie and Kan (1996), best suited to affine models. While it can be adapted to the shadow-rate framework, it loses much of its analytical tractability, and becomes computationally infeasible as the number of factors increases. Section 4.2 discusses an alternative method proposed by Krippner (2012). It defines a pseudo-forward rate that satisfies the lower bound (though differs from the arbitrage-free forward rate), and uses relationship (5) to approximate bond prices. Finally, Section 4.3 proposes a new approximation technique for yields in the shadow-rate model based on the expansion of a cumulant-generating function.

4.1 Partial Differential Equation

Like any conditional expectation of an -measurable random variable, (the time price of a zero-coupon bond maturing at time in the unconstrained Gaussian model, discounted to time 0 ) follows a martingale under . This is an immediate consequence of the definition of a martingale, after an application of the Law of Iterated Expectations. Using It's Lemma and the Martingale Representation Theorem, we can therefore represent by the function , where solves the partial differential equation (PDE)

with boundary condition . Using a separation-of-variables argument, it can be verified that

solves (7), where and in turn solve ordinary differential equations (ODEs) in terms of the model parameters,

with , . Reducing problem (7) to the system of ODEs (9)-(10) simplifies the numerical computation of bond prices substantially as it reduces the dimensionality of the problem from to 1 (the time dimension). Direct computation reveals that

assuming is invertible.

A PDE analogous to (7) can be set up for the observed bond price in the shadow-rate model, . The only required modification is to replace the expression for the shadow short rate, , by that for the observed short rate, . Unfortunately, when this non-linearity is introduced, the separation-of-variables procedure no longer applies, and no solution as straightforward as (8) is available. It is possible to solve the modified version of (7) directly by numerical methods. This is the approach taken by Kim and Singleton (2012). It requires discretizing and on a multidimensional grid, which is computationally intensive and subject to the curse of dimensionality. Kim and Singleton (2012) therefore do not estimate models with more than two factors.

4.2 Forward Rate Approximation

Krippner (2012) proposes an alternative approach to computing yields in shadow-rate models, which is implemented empirically by Christensen and Rudebusch (2013). It is based on an approximation to the forward rate . Substituting for from the shadow-rate version of (3), and differentiating, we obtain

where , defined by the Radon-Nikodym derivative is referred to as the " -forward measure." Equation (12) says that today's time forward rate is equal to today's expectation under the -forward measure of the time short rate. It can be verified directly from (12) and the Law of Iterated Expectations that is a martingale under (note that by definition). Note also that (12) implies that the forward rate is subject to the same lower bound (6) as the short rate, by monotonicity of the mathematical expectation.

Analogously,

expresses the

The distribution of the shadow forward rate under can be derived more explicitly. First, from (5) and (8),

Therefore, by It's Lemma and the Martingale Representation Theorem,Thus, is Gaussian under conditional on , with

The final equality uses the It Isommetry and (11).

Krippner (2012) takes advantage of this distributional property of . He defines a pseudo-forward rate as a hybrid between observed forward rate (12) and shadow forward rate (13):

This is the expectation under the

This is formula (32) in Krippner (2012).

Zero-coupon bond prices and yields can be approximated by substituting from (17) into (5) and (4).

4.3 Cumulants

Since the PDE approach to pricing bonds in shadow-rate models becomes computationally intractable as the number of factors increases, and the approach proposed by Krippner (2012) relies on a forward rate that is not equal to the arbitrage-free forward rate, I propose a new cumulant-based technique to approximating yields in Gaussian shadow-rate models.

The quantity
appearing in the shadow-rate version of (4) is the conditional cumulant-generating function^{4} under
, evaluated at -1 , of the random variable
. It has the series representation

where is the th cumulant of under . An approximation to the zero-coupon yield can therefore be computed based on a finite number of terms in the series in (18). Below, I consider the first-order approximation

and the second-order approximation

where I make use of the fact that the first two cumulants of any random variable coincide with its first two centered moments.

The first-order approximation (19) is equivalent to the method proposed independently and contemporaneously by Ichiue and Ueno (2013). I present it here both for comparison and to assess its relative performance in Section 5 below. I will, however, mostly focus on the
second-order approximation (20), which I argue is particularly promising a priori because it is exact in the Gaussian benchmark case.^{6}
It can therefore be expected to perform well both for short maturities (where the higher-order terms in (18) are relatively small), and for long maturities as long as
is small for large (in which case
will behave approximately like a Gaussian process over sufficiently long horizons). Indeed, empirically, the second-order approximation turns out to be
highly accurate across maturities both during normal times and when rates are low (see Section 5).

4.3.1 Computation of the First Moment

Evaluating the first- and second-order approximations (19) and (20) to zero-coupon yields requires computation of the first two cumulants (equivalently, centered moments) of . This subsection will be concerned with the first moment. As an initial step,

by an application of Fubini's Theorem. Since and with known expressions for and in terms of the model parameters (as shown in Appendix A), it follows from Lemma A.1 in Appendix A that

where and denote the standard normal probability density function (pdf) and cumulative distribution function (cdf), respectively. That is, we can compute analytically up to the standard normal cdf, which software such as Matlab is able to evaluate precisely and efficiently. The first cumulant of can then be computed by numerical integration of over the time dimension, as in (22).

4.3.2 Computation of the Second Moment

Once we know the first moment of , it remains to evaluate

where the equality uses Fubini's Theorem and symmetry of the integrand. Since and , and are jointly normally distributed with mean , variances , and covariance (see Appendix A), we obtain from Lemma A.2 in Appendix A:

where , , , and denotes the decumulative bivariate normal cdf.

That is, we can compute
analytically up to the *bivariate* normal cdf, and we can then integrate this expression
numerically over and to obtain the second cumulant of
.

The following steps summarize the approximation procedure for zero-coupon yields for a given set of parameters and state vector :

- Compute the conditional mean and covariance matrix of , for , using (A.4) and (A.5).
- Using the results from step 1, compute and , for , as described in 4.3.1 and 4.3.2.
- Integrate numerically to obtain , and integrate numerically to obtain .
- Using the moments computed in step 3, approximate by or as defined in (19) and (20).

Note that the complexity of the algorithm does not depend on the number of yield curve factors , so it is not subject to the curse of dimensionality in the same way that some other
methods are.^{9}

For illustration purposes, in Appendix B I apply the second-order approximation method to estimate a three-factor shadow-rate model of the U.S. Treasury term structure.

5 Accuracy of Yield Approximation Methods

Section 4.3 argued intuitively that the second-order yield approximation (20) should be relatively precise. This section quantifies that claim. To get an initial sense of the relative numerical accuracy, I consider the stylized model used for illustration by Gorovoi and Linetsky (2004), and replicated for the same purpose in Krippner (2012). It is a one-factor model with
,
,
,
, and
. Gorovoi and Linetsky (2004) derive model-implied yield curves for states
corresponding to shadow short rates
. The four panels of Figure 1 plot the model-implied yield curves for each initial state. Within each panel, I compare four different yield approximation
schemes: Solving PDE (7) numerically (which, in a one-factor setting, is computationally feasible and can be considered the "exact" solution for comparison purposes), Krippner's (2012) approach described in Section 4.2, and the first- and second-order approximations proposed in Section
4.3. As the figure shows, the second-order approximation matches the exact PDE solution most closely, and consistently across states. The yield approximation error is uniformly less than one basis point. The first-order approximation generally overstates yields (an implication of the alternating
nature of series expansion (18)), with approximation errors increasing both in yield maturity and the level of the shadow short rate (in both cases, the first-order approximation is off by an increasingly large convexity adjustment arising from Jensen's inequality). Krippner's (2012) method generally undershoots yields, and is relatively more accurate when the shadow short rate is higher. Why this is the case can be seen intuitively by comparing the
-measure expressions for the arbitrage-free forward rate (12) and Krippner's (2012) approximate forward rate (16): While both use the same time short rate
, Krippner's (2012) formula discounts by the *shadow* rate rather than the observed short rate. This means the discount factor tends to be larger than it
should be when
is low, reducing the covariance between discount factor and
, and thus lowering the expectation of their product,
.

To compare the relative performance of the different yield approximations in a more realistic empirical setting, I use the estimated model parameters and smoothed states from Appendix B.3 to compute model-implied yield curves for all dates in the sample.^{10} Since this is a three-factor model, solving PDE (7) numerically is no longer practicable. I therefore replace this benchmark by a simulated yield
that consistently estimates the true yield
based on
randomly drawn

short-rate paths per sample date.^{11},^{12} Table 1 shows the mean simulation error of
, and the root-mean-square errors (RMSE) against
of the yield
computed by Krippner's (2012) method, the first-order approximation
defined in (19), and the second-order approximation
defined in (20). The table is divided into two panels. The top panel shows errors for the sub-sample Jan 1990-Nov 2008 (when
interest rates were at normal levels), and the bottom panel shows errors for the sub-sample Dec 2008-Dec 2012 (when the lower bound on nominal yields was binding at the short end of the yield curve). All methods are generally more precise at shorter maturities. As the first column in both panels
shows, the simulated yields are accurate to within approximately one fifth of a basis point at the ten-year maturity point. As shown in the second column of the tables, Krippner's (2012) method produces ten-year yields that are accurate to about one basis point during normal times, and to within four basis
points when the lower bound is binding. While the first-order method is more accurate when rates are low (the bottom panel), its errors remain substantial at the long end. The second-order method, the final column in the tables, produces ten-year yields that are accurate to approximately half a
basis point, both during normal times and when the lower bound is binding.

To further illustrate the time-varying relative performance of the three approximation schemes, Figure 2 plots the difference over time between the simulated ten-year yield and the three approximated yields. Krippner's (2012) method and the second-order approximation appear to be equally precise in the first few years of the sample,

Maturity | ||||

6m Sub-sample Kam 1990-Nov 2008 | 0.04 | 0.04 | 0.05 | 0.04 |

1y Sub-sample Kam 1990-Nov 2008 | 0.06 | 0.06 | 0.18 | 0.06 |

2y Sub-sample Kam 1990-Nov 2008 | 0.09 | 0.10 | 0.88 | 0.10 |

3y Sub-sample Kam 1990-Nov 2008 | 0.12 | 0.14 | 2.26 | 0.13 |

4y Sub-sample Kam 1990-Nov 2008 | 0.14 | 0.18 | 4.22 | 0.15 |

5y Sub-sample Kam 1990-Nov 2008 | 0.16 | 0.27 | 6.60 | 0.17 |

7y Sub-sample Kam 1990-Nov 2008 | 0.19 | 0.47 | 12.22 | 0.21 |

10y Sub-sample Kam 1990-Nov 2008 | 0.21 | 0.93 | 21.81 | 0.35 |

6m Sub-sample Dec 2008-Dec 2012 | 0.01 | 0.01 | 0.01 | 0.01 |

1y Sub-sample Dec 2008-Dec 2012 | 0.02 | 0.04 | 0.04 | 0.02 |

2y Sub-sample Dec 2008-Dec 2012 | 0.05 | 0.19 | 0.33 | 0.05 |

3y Sub-sample Dec 2008-Dec 2012 | 0.07 | 0.51 | 1.07 | 0.07 |

4y Sub-sample Dec 2008-Dec 2012 | 0.10 | 0.94 | 2.31 | 0.09 |

5y Sub-sample Dec 2008-Dec 2012 | 0.12 | 1.42 | 3.99 | 0.12 |

7y Sub-sample Dec 2008-Dec 2012 | 0.15 | 2.43 | 8.38 | 0.23 |

10y Sub-sample Dec 2008-Dec 2012 | 0.17 | 3.87 | 16.63 | 0.52 |

with fluctuations presumably largely due to simulation error.^{13} The discrepancy between simulated yield and Krippner's (2012) method
increases over time as the level of yields declines, and exceeds five basis points by December 2012. The discrepancy between simulated yield and second-order approximation remains small and appears to show little systematic variation over time, perhaps trending up modestly towards the end of the
sample. The first-order approximation has a large negative discrepancy initially, which shrinks over time but remains at a high absolute level even at the end of the sample.

Figure 2 also confirms that, just like in the simple one-factor model in Figure 1, the approximation errors under Krippner's (2012) method and the first-order scheme are largely systematic (rather than mere noise), in that the first-order approximation overstates arbitrage-free yields while Krippner's (2012) method tends to understate them.

In sum, the analysis above suggests that, empirically, the second-order yield approximation is accurate to within about one half of a basis point at maturities up to ten years, both during normal times and when the lower bound is binding. The approximation error is one order of magnitude smaller than both the model-implied observation error in yields (see Table 3) and the next best approximation method proposed by Krippner's (2012). In contrast, the first-order approximation is acceptable at most at the very short end of the yield curve.

To add perspective, the approximation error in is no greater than commonly accepted fitting error in the derivation of constant-maturity zero-coupon bond yields from observed coupon-bearing Treasuries (e.g., Gürkaynak et al., 2006). This puts the second-order approximation roughly on par with the numerical accuracy achieved

by "exact" bond pricing methods in standard affine models, to the extent that they rely on numerical methods (say, to solve the system of ODEs (9)-(10)).^{14}

This paper develops an approximation to arbitrage-free zero coupon bond yields in Gaussian shadow-rate term structure models. The complexity of the scheme does not depend on the number of factors. Further, I demonstrate that the method is computationally feasible by estimating a three-factor shadow-rate model of the U.S. Treasury yield curve. Based on Monte Carlo simulation, I also show that the yield approximation is approximately as precise as conventional approaches that are considered to be "exact."

Consider the continuous time stochastic process defined in (1). The following derivations hold under both the -measure and the -measure, hence for notational simplicity I will suppress dependence of moments and parameters on the measure. Since is a Gaussian process, all its finite-dimensional distributions are Gaussian Karatzas and Shreve, 1991). In particular, for , the vectors are jointly conditionally Gaussian, with

If is invertible, the integral on the right-hand side of (A.2) can be evaluated analytically using integration by parts and formula (10.2.15) in Hamilton (1994),

where " " denotes the Kronecker sum. Since as defined in (2) is a linear function of the Gaussian random vector , it is itself Gaussian, with

and

This section derives two useful mathematical results involving the moments of censored Gaussian random variables.

Thus, it only remains to compute where is a standard normal random variable, for arbitrary . By direct computation of the integral defining the expectation,

where is the standard normal cdf. Further,

Recursively substituting from (A.8) into (A.7), and finally into (A.6), establishes the result.

*If*

*where
,
,
, denotes the univariate
standard normal pdf, denotes the univariate standard normal cdf,
denotes the bivariate normal pdf when both variables have zero means, unit variances, and correlation , and and
denote the corresponding cumulative and decumulative bivariate Gaussian distribution functions, where in particular
.*

The second and third terms can be evaluated using Lemma A.1. For the first term, it suffices to be able to compute for random variables and that are bivariate normal with zero means, unit variances, and correlation , and for arbitrary . Using the properties of , this expectation can be expanded as follows:

The first double integral is simply , the bivariate normal cdf. The second and third double integrals correspond to expected values of truncated bivariate normal random variables, and the last integral is the expected cross product of a truncated bivariate normal random vector. These expected values are known up to the univariate standard normal cdf and the bivariate normal cdf, respectively (see Rosenbaum (1961)). Using the formulas in Rosenbaum (1961) to evaluate the integrals in (A.9), and substituting into (A.10), we obtain (A.9) after simplification.

B. Empirical Implementation

This appendix empirically estimates a three-factor Gaussian shadow-rate term structure model using the yield approximation methodology proposed in Section 4.3. The main purpose is to demonstrate the computational tractability of the method in the context of a realistic application. For more in-depth discussion and empirical analysis, see Kim and Priebsch (2013).

B.1 Data

I use end-of-month zero-coupon U.S. Treasury yields from January 1990 through December 2012, for maturities of 6 months, 1-5, 7, and 10 years. I derive the 6-month yield from the corresponding T-bill quote, while longer-maturity zero yields are extracted from the CRSP U.S. Treasury Database
using the unsmoothed Fama and Bliss (1987) methodology.^{15}

I augment the yield data with survey forecasts from Blue Chip, interpolated to constant horizons of 1-4 quarters (available monthly), as well as annually out to 5 years and for 5-to-10 years (available every six months).^{16} Model-implied survey forecasts are subject to the same lower-bound constraint as yields,^{17} but their computation is substantially simpler: Forecasters report their expectation of the *arithmetic* *mean* of future observed short rates,
. This is exactly (19) with the data-generating measure
in place of pricing measure
. Therefore, the first-order method described in Section 4.3 produces *exact* model-implied survey forecasts. Intuitively, unlike yields, survey forecasts
are not subject to compounding, so there are no higher-order Jensen's inequality terms to consider.

B.2 Filtering and Estimation

Since the statistical properties of the term structure model laid out in Section 2 are formulated in terms of the latent state vector , but the data actually observed by the
econometrician consist of yields, , and survey expectations, (see
Appendix B.1), I set up a joint estimation and filtering problem to obtain estimates of the model's parameters
.^{18} When discretely sampled at intervals
, the state vector follows a first-order Gaussian vector
autoregression,

where , and , , and can be computed from (A.1) and (A.2). Equation (B.1) represents the transition equation of the filtering problem.

Next, denote by
the (non-linear) mapping from states and parameters to model-implied yields , and by
the analogous mapping from states and parameters to model-implied survey forecasts . For estimation purposes, I compute through the second-order approximation (20), and
through the exact first-order method discussed in Appendix B.1. To simplify notation, denote the stacked mapping
by . If we assume that all yields
and survey expectations are observed with iid additive Gaussian errors, we obtain the *observation equation*

Together, equations (B.1) and (B.2) form a non-linear filtering problem.

The simple (linear) Kalman filter--optimal when measurement and observation equation are linear and all shocks are Gaussian--has been modified in a number of ways to accommodate nonlinearity as in (B.2). The *unscented Kalman filter*, proposed by Julier
et al. (1995), aims to deliver improved accuracy and numerical stability relative to the more traditional *extended Kalman filter*, without substantially increasing the computational burden.^{19},^{20} The algorithm is described in detail in Wan and van der Merwe (2001).
As a by-product of the filtering procedure, it conveniently produces estimates of the mean and covariance matrix of
conditional on the econometrician's information set as of time
. I use these to set up a quasi-maximum likelihood function based on (B.2),^{21} which I maximize numerically to obtain estimates of
the parameters as well as their asymptotic standard errors (following Bollerslev and Wo oldridge, 1992).

B.3 Estimation Results

To achieve econometric identification of the parameters in light of invariant transformations resulting in observationally equivalent models with different parameters (see Dai and Singleton, 2000 ), I follow Joslin et al. (2011) and impose the normalizations , , is diagonal and therefore completely determined by its ordered eigenvalues , and is lower triangular.

I estimate the model on the data set described in Appendix B.1, using the quasi-maximum likelihood (QML) procedure discussed in Appendix B.2. Table 2 displays the estimated model parameters , as well as their asymptotic standard errors.

0.0738 | |

:standard errors | (0.0043) |

0.0010 | |

:standard errors | (0.0001) |

vector, row 1 | - 0.1038 |

vector, row 1: standard error | (0.0226) |

vector, row 2 | - 0.3566 |

vector, row 2: standard error | (0.1177) |

vector, row 3 | - 0.8574 |

vector, row 3: standard error | (0.2876) |

vector, row 1 | - 0.0193 |

vector, row 1: standard error | (0.0052) |

vector, row 2 | - 0.0099 |

vector, row 2: standard error | (0.0224) |

vector, row 3 | 0.0278 |

vector, row 3: standard error | (0.0233) |

matrix, row 1, column 1 | 0.0268 |

matrix, row 1, column 1: standard error | (0.0084) |

matrix, row 2, column 1 | - 0.0324 |

matrix, row 2, column 1: standard error | (0.0110) |

matrix, row 2, column 2 | 0.0416 |

matrix, row 2, column 2: standard error | (0.0295) |

matrix, row 3, column 1 | 0.0068 |

matrix, row 3, column 1: standard error | (0.0100) |

matrix, row 3, column 2 | - 0.0397 |

matrix, row 3, column 2: standard error | (0.0302) |

matrix, row 3, column 3 | - 0.0090 |

matrix, row 3, column 3: standard error | (0.0007) |

matrix, row 1, column 1 | - 0.4679 |

matrix, row 1, column 1: standard error | (0.1574) |

matrix, row 1, column 2 | - 0.3415 |

matrix, row 1, column 2: standard error | (0.1490) |

matrix, row 1, column 3 | 0.3785 |

matrix, row 1, column 3: standard error | (0.6798) |

matrix, row 2, column 1 | - 0.5752 |

matrix, row 2, column 1: standard error | (0.6303) |

matrix, row 2, column 2 | - 1.1881 |

matrix, row 2, column 2: standard error | (1.1335) |

matrix, row 2, column 3 | - 1.1875 |

matrix, row 2, column 3: standard error | (0.5740) |

matrix, row 3, column 1 | 0.8908 |

matrix, row 3, column 1: standard error | (0.6982) |

matrix, row 3, column 2 | 1.3060 |

matrix, row 3, column 2: standard error | (1.0641) |

matrix, row 3, column 3 | 0.3990 |

matrix, row 3, column 3: standard error | (1.3561) |

Table 3 shows the QML-estimated standard deviations of the measurement errors in yields and survey variables ( in equation (B.2)). The average yield error is 8 basis points, and the average error in surveys is 21 basis points. For both yields and surveys, errors follow a U-shaped pattern, being largest at the short and long ends.

Figure 3 plots the model-implied shadow short rate over the sample period, based on the states implied by the Kalman smoother (that is, incorporating all information up to December 2012, the end of the sample). The shadow rate turned negative in December 2008, after the FOMC established a target federal funds rate range of 0 to 0.25 percent and the effective lower bound became binding, and has stayed negative through the end of the sample.

Maturity | |

6m | 0.0017 |

1y | 0.0014 |

2y | 0.0006 |

3y | 0.0003 |

4y | 0.0004 |

5y | 0.0003 |

7y | 0.0006 |

10y | 0.0015 |

Average | 0.0008 |

Maturity | |

1q | 0.0014 |

2q | 0.0002 |

3q | 0.0009 |

4q | 0.0014 |

2y | 0.0028 |

3y | 0.0026 |

4y | 0.0027 |

5y | 0.0031 |

5y-10y | 0.0034 |

Average | 0.0021 |

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* Federal Reserve Board, Washington D.C., `[email protected]`. The analysis and conclusions set forth in this paper are those of the author and do not indicate concurrence by other members of the research staff or the Board of Governors of the
Federal Reserve System. Return to Text

1. The integral in (15) has the same form as (A.2) in Appendix A and therefore can be computed analytically as in (A.3). Return to Text

2. Krippner (2012) motivates his derivation in terms of options on shadow bonds. To derive (16) from his equations (12) and (13), replace the call option price by
Then interchange the limit operations and expectation, and evaluate. Return to Text

3. Krippner (2012) uses the parametrization proposed by Chen (1995) and hence obtains his formula (31) for as a
special case of (15). Similarly, the results derived by by Christensen and Rudebusch (2013) are (essentially) special cases of (15) and (17) under their Nelson-Siegel parametrization (where some of the derivations must be modified
appropriately to account for the fact that their matrix
is singular). Don Kim (personal communication) independently derives the general expression for in (15) by generalizing Krippner's (2012) computations directly. Return to Text

4. The cumulant-generating function of a random variable is defined as the logarithm of its moment-generating function (for example, see Severini, 2005). Return to Text

5. Higher-order approximations following the same general logic are possible, but they are increasingly computationally costly while generating decreasing marginal benefits in terms of precision. Return to Text

6. The third- and higher-order cumulants of a Gaussian random variable are zero, so that (20) coincides with the usual affine-Gaussian yield formula in that case. Return to Text

7. Matlab's built-in function `mvncdf` uses adaptive numerical integration to compute the bivariate normal cdf. This is slower by several orders of magnitude than the algorithm I use. Return to Text

8. That is, without `for`-loops that grow with the number of quadrature points, states, dates, or maturities in the sample. Return to Text

9. The general approximation methodology I propose has its own curse of dimensionality in that the second-order approximation is substantially more computationally involved than the first-order approximation (and any higher-order approximation will be
substantially more involved than the second-order approximation). In practice, the second-order approximation appears to strike an acceptable balance between precision and computational complexity for many cases of interest, see Section 5 below. Return to
Text

10. The sample consists of end-of-month observations from January 1990 through December 2012. Return to Text

11. I simulate (1) under
based on moments (A.1)-(A.2) on a uniformly-spaced grid with
. For each simulated path , I compute
by the trapezoidal method. I then define
and
. The simulation error for
is computed as times the sample standard
deviation, and the simulation error for
is derived from the simulation error for
by the delta method. Return to Text

12. The simulation takes several hours to complete for the given parameter estimates and smoothed states. This approach would not, therefore, be feasible as part of an estimation strategy. Return to Text

13. Recall that when there is no lower bound, both methods produce yields equal to the exact arbitrage-free yield. In the early 1990s, the overall level of yields was sufficiently high for the effect of the lower bound to be negligible. Return to Text

14. Empirically, the second-order approximation
is exact to an absolute tolerance of approximately
(see Table 1). The default tolerance for numerical methods in Matlab is typically between and , depending on the complexity of the method. Return to Text

15. I am grateful to Anh Le for providing the code for this procedure. Return to Text

16. As discussed by Kim and Orphanides (2005), this potentially leads to more precise estimates of the parameters governing the data-generating distribution
. Return to Text

17. This follows from equivalence of the measures
and
, and more fundamentally from the absence of arbitrage. Return to Text

18. See Duan and Simonato (1999) for an early reference discussing this approach towards term structure model estimation. Return to Text

19. A detailed treatment of the unscented Kalman filter, and a comparison to the extended Kalman filter, can be found in Wan and van der Merwe (2001). Return to Text

20.Christoffersen et al. (2012) and Wu (2010) confirm that the unscented Kalman filter performs better than the extended Kalman filter in the specific setting of term structure model estimation. Return to
Text

21. This estimation approach is described and analyzed in Lund (1997). Return to Text