A Nonparametric Model of Term Structure Dynamics and the Market Price of Interest Rate Risk

Stanton, Richard

doi:10.1111/j.1540-6261.1997.tb02748.x

Cited by 384 publications

(208 citation statements)

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“…Nevertheless, several studies identify nonlinearity in interest rate dynamics, e.g., Aït-Sahalia (1996a,b), Hong and Li (2005), and Stanton (1997). In this subsection, we propose an alternative class of models that are equally tractable but can generate richer nonlinear interest-rate and default arrival dynamics.…”

Section: Nonlinear Interest-rate and Default Arrival Dynamics: A Quadmentioning

confidence: 99%

Dynamic Interactions Between Interest Rate, Credit, and Liquidity Risks: Theory and Evidence from the Term Structure of Credit Default Swap Spreads

Chen

Cheng

2005

SSRN Journal

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Using a large data set on credit default swaps, we study how default risk interacts with interest-rate risk and liquidity risk to jointly determine the term structure of credit spreads. We classify the reference companies into two broad industry sectors, two broad credit rating classes, and two liquidity groups. We develop a class of dynamic term structure models that include (i) two benchmark interest-rate factors to capture the libor and swap rates term structure, (ii) two credit-risk factors to capture the credit swap spreads of high-liquidity group of each industry and rating class, and (iii) both an additional credit-risk factor and a liquidity-risk factor to capture the difference between the high-and low-liquidity groups.Estimation shows that companies in different industry and credit rating classes have different credit-risk dynamics. Nevertheless, in all cases, credit risks exhibit intricate dynamic interactions with the interestrate factors. Interest-rate factors both affect credit spreads simultaneously, and impact subsequent moves in the credit-risk factors. Within each industry and credit rating class, we also find that the average credit default swap spreads for the high-liquidity group are significantly higher than for the low-liquidity group. Estimation shows that the difference is driven by both credit risk and liquidity differences. The low-liquidity group has a lower default arrival rate and also a much heavier discounting induced by the liquidity risk.JEL CLASSIFICATION CODES: E43, G12, G13, C51.KEY WORDS: Credit default swap; credit risk; credit premium; term structure; interest rate risk; liquidity risk; liquidity premium; maximum likelihood estimation. Dynamic Interactions Between Interest Rate, Credit, and Liquidity Risks: Theory and Evidence from the Term Structure of Credit Default Swap SpreadsIt is important to understand how credit risk interacts with interest-rate risk and liquidity risk in determining the term structure of credit spreads on different reference entities. Nevertheless, limited data availability has severely hindered the understanding. Since defaults are rare events that often lead to termination or restructuring of the underlying reference entity, researchers need to rely heavily on cross-sectional averages of different entities over a long history to obtain any reasonable estimates of statistical default probabilities.Although corporate bond prices contain useful information on the default probability and the price of credit risk, the information is often mingled with the pricing of the underlying interest-rate risk and other factors such as liquidity and tax. 1 The recent development in credit derivatives provides us with an excellent opportunity to better understand the pricing of credit risk, its interactions with interest-rate risk and liquidity, and the impacts on the term structure of credit spreads. The most widely traded credit derivative is in the form of credit default swap (CDS), written on a reference entity such as a sovereign country or a cor...

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Section: Nonlinear Interest-rate and Default Arrival Dynamics: A Quadmentioning

confidence: 99%

Dynamic Interactions Between Interest Rate, Credit, and Liquidity Risks: Theory and Evidence from the Term Structure of Credit Default Swap Spreads

Chen

Cheng

2005

SSRN Journal

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“…The swings in excess returns are notably larger in the two-regime model A RS 0 (3) for those periods with largest absolute excess returns (e.g., during the period of the "monetary experiment" in the early 1980's). On the other hand, during more "normal" times, variation in the excess returns appears larger in the 1 Ang and Bekaert [2002b] suggest that the mixing of regime-dependent state processes inherent in our DTSM can potentially replicate the nonlinear conditional means of short-term yields documented by AitSahalia [1996] and Stanton [1997]. While the non-parametric evidence for non-linearity in the short-rate process is somewhat controversial (see, e.g., Chapman and Pearson [2000]), the findings of Ang and Bekaert for a Gaussian autoregressive model of a short rate suggest that our regime-dependent state process introduces the flexibility to match such nonlinearity if it is present.…”

Section: Introductionmentioning

confidence: 99%

Regime Shifts in a Dynamic Term Structure Model of U.S. Treasury Bond Yields

2007

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This paper develops and empirically implements an arbitrage-free, dynamic term structure model with "priced" factor and regime-shift risks. The risk factors are assumed to follow a discrete-time Gaussian process, and regime shifts are governed by a discrete-time Markov process with state-dependent transition probabilities. This model gives closed-form solutions for zero-coupon bond prices and an analytic representation of the likelihood function for bond yields. Using monthly data on U.S. Treasury zero-coupon bond yields, we document notable differences in the behaviours of the market prices of factor risk across high and low volatility regimes. Additionally, the state-dependence of the regime-switching probabilities is shown to capture an interesting asymmetry in the cyclical behaviour of interest rates. The shapes of the term structures of bond yield volatilities are also very different across regimes, with the well-known hump in volatility being largely a low-volatility regime phenomenon.

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“…A variety of strategies have been proposed to estimate θ, including simulation-based approaches such as Indirect Inference (Gouriéroux, Monfort and Renault, 1993) or the Efficient Method of Moments (Gallant and Tauchen, 1996), Generalized Method of Moments approaches (Carrasco, Chernov, Florens and Ghysels, 2002;Duffie and Glynn, 2001), nonparametric (Aït-Sahalia, 1996a,b;Bandi and Phillips, 2003;Stanton, 1997) and Bayesian strategies (Eraker, 2001;Jones, 1999), among many others. A few others have been advanced to approximate the unknown transition density, and hence to allow efficient (albeit approximate) maximum likelihood estimation: among these, numerically solving the Fokker-Planck-Kolmogorov partial differential equation (Lo, 1988), closed-form analytic approximation based on Hermite polynomials expansion (Aït-Sahalia, 1999, 2002, and the simulation-based, Monte Carlo integration strategy suggested by Pedersen (1995) and Brandt and Santa-Clara (2002), and further explored by Durham and Gallant (2002).…”

Section: The Problemmentioning

confidence: 99%

Efficient importance sampling maximum likelihood estimation of stochastic differential equations

Pastorello

Rossi

2010

Computational Statistics & Data Analysis

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This paper considers ML estimation of a diffusion process observed discretely. Since the exact loglikelihood is generally not available, it must be approximated. We review the most efficient approaches in the literature, and point to some drawbacks. We propose to approximate the loglikelihood using the EIS strategy (Richard and Zhang, 1998), and detail its implementation for univariate homogeneous processes. Some Monte Carlo experiments evaluate its performance against an alternative IS strategy (Durham and Gallant, 2002), showing that EIS is at least equivalent, if not superior, while allowing a greater flexibility needed when examining more complicated models.JEL codes: C13, C15, C22

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A Nonparametric Model of Term Structure Dynamics and the Market Price of Interest Rate Risk

Cited by 384 publications

References 43 publications

Dynamic Interactions Between Interest Rate, Credit, and Liquidity Risks: Theory and Evidence from the Term Structure of Credit Default Swap Spreads

Dynamic Interactions Between Interest Rate, Credit, and Liquidity Risks: Theory and Evidence from the Term Structure of Credit Default Swap Spreads

Regime Shifts in a Dynamic Term Structure Model of U.S. Treasury Bond Yields

Efficient importance sampling maximum likelihood estimation of stochastic differential equations

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