Term Structure Factors Research Articles

Modeling Corporate CDS Spreads Using Markov Switching Regressions

Abstract This paper investigates the determinants of the European iTraxx corporate CDS index considering a large set of explanatory variables within a Markov switching model framework. The influence of financial and economic variables on CDS spreads are compared using linear, two, three, and four-regime models in a sample post-subprime financial crisis up to the COVID-19 pandemic. Results indicate that four regimes are necessary to model the CDS spreads. The fourth regime was activated during the COVID-19 pandemic and in high volatility periods. Further, the effect of the covariates differs significantly across regimes. Brent and term structure factors became relevant after the outbreak of the COVID-19 pandemic.

Measuring the dynamic term structure of the FFA market

ABSTRACT Forward Freight Agreements (FFAs) are exchange-traded futures and the main means of risk management for shipping spot freight. The relationship between the FFA market and the shipping spot market has attracted much attention, but few studies focus on the dynamic FFA term structure and its correlation with the spot price. In this study, we establish the dynamic term structure of the FFA market by quantifying the level factor, the slope factor and the curvature factor of the FFA term structure to reveal the underlying information in the shipping derivatives market, and further scrutinize the impact of the FFA term-structure factors on the shipping spot market. The empirical results indicate that the term-structure factors have significant time-varying effects on the BDI and BPI: The effect of the term-structure factors on BDI and BPI changes with shipping market conditions and economic environment. The level factor has a positive effect, while the slope factor and the curvature factor both have negative effects, with the impacts on the BPI being about twice as large as the impacts on the BDI. The three factors all have their strongest effect in the short term and the weakest effect in the long term.

The Fortune and crash of common risk factors in Chinese commodity markets

This paper investigates the performance of nine commonly discussed market anomalies in the Chinese commodity market. By studying a data sample from 2005 to 2020, we find the common risk factors associated with term structure and momentum anomalies effectively explain the cross-sectional excess returns and generate profitable sorting portfolios. Meanwhile, we empirically demonstrate that the term structure and momentum risk factors significantly crash during periods of high market stress, although they bring overall good outperformance in out-of-sample. We attribute these crashes to high time-varying volatility. Inspired by the augmented momentum crash strategy of Daniel and Moskowitz (2016), we construct augmented term structure and momentum risk factors to improve their performances in the Chinese commodity futures market. The out-of-sample Sharpe ratios of the term structure and momentum risk factors increase from 0.75 to 1.08 and from 0.66 to 0.77, respectively. In particular, both risk factors exhibit over 100% increments in out-of-sample Sharpe ratios during bear markets.

The relative pricing of WTI and Brent crude oil futures: Expectations or risk premia?

This paper studies the spread of Brent-WTI futures prices using a no-arbitrage term structure model with one common and two latent idiosyncratic risk factors. We document more negative risk premia for WTI than for Brent, and the differences are more pronounced at longer maturities. The expectation of future spot price dominates the risk premium in determining the term structure of Brent-WTI futures spread, especially at short maturities. The common risk premia in both markets are negative and similar, while their corresponding idiosyncratic risk premia have opposite signs. The common risk prices of WTI and Brent are generally related to the US crude commercial stock, inflation, economic uncertainty, and hedging pressure; however, idiosyncratic risk prices are more related to their corresponding local production, short rate, and the term structure factors. The variance decomposition indicates that the idiosyncratic factors account for a considerable part at longer forecast horizons in both markets.

Oil price shocks and yield curve dynamics in emerging markets

In a local projections framework, we study the impact of oil price shocks, based on a refined approach to disentangle oil price movements, on the dynamics of the entire yield curve in nineteen emerging economies with different positions on the oil market. Responses of the term structure factors to oil market shocks are shown to differ conditional on not only the underlying sources that drive oil price, but also based on the oil-dependence of these economies. In particular, we find that oil price risk shocks put upward pressure on the level, slope, and curvature of interest rates across the board. Supply-driven shocks in oil markets cause a rise in the level of interest rates in oil-importing economies more significantly, yet the downward impact on yield curve slope is more pronounced in oil-exporting countries. Demand-driven shocks have a significant and persistent upward impact on level factors in oil-importing countries. Furthermore, the effect of precautionary demand shocks on the curvature factor is more pronounced in oil-importing countries vis-à-vis oil-exporters. Significance, direction, and duration of our results may guide monetary policymakers in emerging countries as well as international investors in portfolio and hedging decisions.

Dimensionality Reduction and State Space Systems: Forecasting the US Treasury Yields Using Frequentist and Bayesian VARs

Using a state-space system, I forecasted the US Treasury yields by employing frequentist and Bayesian methods after first decomposing the yields of varying maturities into its unobserved term structure factors. Then, I exploited the structure of the state-space model to forecast the Treasury yields, and compared the forecast performance of each model using the metric - mean squared forecast error, as the loss function. Among the frequentist methods, I applied the two-step Diebold-Li, two-step principal components, and one-step Kalman filter approaches. Likewise, I imposed the five different priors in Bayesian VARs -Diffuse, Minnesota, natural conjugate, independent normal inverse-Wishart, and the stochastic search variable selection priors. After forecasting the Treasury yields for 9 different forecast horizons, I found that the BVAR with Minnesota prior generally minimizes the loss function. I augmented the above BVARs by including macroeconomic variables and constructed impulse response functions with a recursive ordering identification scheme. Finally, I fitted a sign-restricted BVAR with dummy observations.

Information in the Term Structure: A Forecasting Perspective

The existing literature finds that information not captured by traditional term structure factors helps predict excess bond returns. When estimating no-arbitrage affine term structure models, aligning in-sample and out-of-sample objective functions results in term structure factors that capture information that remains hidden from existing approaches. Specifically, the estimates of the third term structure factor radically differ and are related to the fourth principal component, which helps forecast bond returns. The new objective function leads to substantial improvements in forecasting performance. It also results in higher model term premiums and lower expected future short rates.This paper was accepted by David Simchi-Levi, finance.

Research on RMB exchange rate forecast based on the neural network model and the Nelson–Siegel model

This paper expands the neural network model to predict exchange rate based on the factors extracted from the Nelson–Siegel model. Based on the theory about exchange rate forecasting, interest could be used to predict the movement of exchange rate. Therefore, this paper analyzes the interest rate term structure factors based on the US and China yield curves data, then uses the Nelson–Siegel model to extract the factors of the interest rate term structure. Finally, the factors of yield curves are used as input data to of the neural network model. And the mean forecasting squared errors, mean absolute errors, mean absolute percentage errors of neural network model, Nelson–Siegel regression model, and ARIMA model are compared. The results show that the neural network model has a superior ability to explain the exchange rate fluctuations of the CNY and USD, and the prediction ability is better than the exchange rate prediction ability of the Nelson–Siegel regression model and ARIMA model.

Structure factors for tunneling ionization rates of molecules: General grid-based methodology and convergence studies.

We present a general methodology for evaluating structure factors defining the orientation dependence of tunneling ionization rates of molecules, which is a key process in strong-field physics. The method is implemented at the Hartree-Fock level of electronic structure theory and is based on an integral-equation approach to the weak-field asymptotic theory of tunneling ionization, which expresses the structure factor in terms of an integral involving the ionizing orbital and a known analytical function. The evaluation of the required integrals is done by three-dimensional quadrature which allows calculations using conventional quantum chemistry software packages. This extends the applications of the weak-field asymptotic theory to polyatomic molecules of almost arbitrary size. The method is tested by comparison with previous results and illustrated by calculating structure factors for the two degenerate highest occupied molecular orbitals (HOMOs) of benzene and for the HOMO and HOMO-1 of naphthalene.

Fed Rate Hikes and the Effects on Turkish Banking System. Could It Catch a Cold This Time?

Could a Fed rate hike affect Turkish banks' stock prices? Could it lead to an increase in the exposure to interest rate risk of Turkish banks, generating negative spillover effects? By means of a new data-rich environment IVAR model with both observables and latent factors for a panel of 18 countries (iDREAM), we estimate the spillover effects of a Fed rate hike on the Turkish economy through a generalized impulse response analysis with sign restrictions. Then, we use a Kalman filter to assess Turkish banks' stock log-returns sensitivities to both iDREAM variables (business cycle, prices, real effective exchange rate and monetary policy) and Nelson-Siegel latent factors of the Turkish interest rate term structure (so-called, level, slope and curvature). Finally, we investigate how the time-varying sensitivity depends on bank-level characteristics and bank-ownership over the period 2006-2017. Our preliminary findings show a strong relationship between Nelson-Siegel latent factors and banks' stock log-returns. In particular, a change in the level has significant effects on the Turkish banking sector, while the other latent factors of the yield curve have only modest effects. Moreover, banks with larger balance sheets, higher capital ratios and higher short-term debt or deposits are less sensitive to level changes.

Iterated Combination Forecast and Treasury Bond Predictability

Using a large number of predictors and based on an extended iterated combination approach of Lin, Wu, and Zhou (2017), we document both statistical and economic significance of Treasury bond return predictability. Macroeconomic and aggregate liquidity variables contain predictive information for bond returns and combining them with term structure and Ludvigson-Ng macro factors significantly improve out-of-sample forecast gains. We also find that variance forecasts can be substantially improved with our approach, yielding significant gains in asset allocation decision. Our results show that information from a large number of predictors collectively contributes to the time-varying Treasury bond premia, and this is robust to different return measures, horizons and sample periods.

The impact of oil price shocks on the term structure of interest rates

In a structural VAR framework, we study the impact of oil price shocks in the global crude oil market on the dynamics of the entire yield curve in four industrialised countries with different positions on the oil market; the US, Canada, Norway, and South Korea. Responses of the term structure factors to oil market shocks are shown to differ contingent on the underlying sources that drive oil price shocks and the country's dependence on oil. Oil market-specific demand shocks result in increases in the level factor in oil-importing countries, but have no such effect in oil-exporting countries. Oil supply disruptions have short-lived negative responses of the slope factors in the US and Canada, associated with loosening monetary policy, whilst demand side shocks tend to lead to increases the slope in all countries. Overall, oil supply and demand shocks jointly account for a considerable amount of the observed variation in the term structure of interest rates.

Self-consistent description of local density dynamics in simple liquids. The case of molten lithium

The dynamic structure factor is the quantity, which can be measured by means of Brillouin light-scattering as well as by means of inelastic scattering of neutrons and x-rays. The spectral (or frequency) moments of the dynamic structure factor define directly the sum rules of the scattering law. The theoretical scheme formulated in this study allows one to describe the dynamics of local density fluctuations in simple liquids and to obtain the expression of the dynamic structure factor in terms of the spectral moments. The theory satisfies all the sum rules, and the obtained expression for the dynamic structure factor yields correct extrapolations into the hydrodynamic limit as well as into the free-particle dynamics limit. We discuss correspondence of this theory with the generalized hydrodynamics and with the viscoelastic models, which are commonly used to analyze the data of inelastic neutron and x-ray scattering in liquids. In particular, we reveal that the postulated condition of the viscoelastic model for the memory function can be directly obtained within the presented theory. The dynamic structure factor of liquid lithium is computed on the basis of the presented theory, and various features of the scattering spectra are evaluated. It is found that the theoretical results are in agreement with inelastic x-ray scattering data.

Fitting and forecasting yield curves with a mixed-frequency affine model: Evidence from China

This paper proposes a novel mixed-frequency affine term structure model to improving the fit and forecasting ability of yield curves. We also show the Bayesian estimation method related to this mixed-frequency model. Then we conduct an empirical study using Chinese macro and financial data. The empirical results show that compared with the traditional same-frequency affine model, the mixed-frequency affine model offers superior performance for fitting the yield curve and term structure factors. Specifically, this mixed-frequency affine model can provide more accurate out-of-sample forecast results of the yield curve.

Loss Functions for Forecasting Treasury Yields

Recent advances in the term structure literature focus on model specification and estimation. While forecasting is considered important, it is not explicitly taken into account at the estimation stage. We estimate affine term structure models by aligning in-sample and out-of-sample loss functions. Aligning loss functions provides substantial improvements in forecasting performance, especially for long forecast horizons. The resulting parameter estimates imply factors that differ from traditional term structure factors, especially for curvature. This suggests that the improvement in fit results from identification of the third factor, which captures information hidden to conventional loss functions, consistent with Cochrane and Piazzesi (2005).

Towards the extrapolation of the valence‒valence electron partial structure factor for liquid Mg near freezing from a combination of theory and experiment

Egelstaff, March, and McGill (1973) proposed the extraction of electron correlation functions in liquids from scattering data. Here, we appeal to computer simulation by de Wijs et al. (1995) on the partial structure factor between ions (i) and valence electrons (v) for liquid Mg near freezing, to write the valence‒valence partial structure factor in terms of and the neutron structure factor , to high accuracy.

A Dynamic Yield Curve Model with Stochastic Volatility and Non-Gaussian Interactions: An Empirical Study of Non-Standard Monetary Policy in the Euro Area

We develop an econometric methodology for the study of the yield curve and its interactions with measures of non-standard monetary policy during possibly turbulent times. The yield curve is modeled by the dynamic Nelson-Siegel model while the monetary policy measurements are modeled as non-Gaussian variables that interact with latent dynamic factors, including the yield factors of level and slope. Yield developments during the financial and sovereign debt crises require the yield curve model to be extended with stochastic volatility and heavy tailed disturbances. We develop a flexible estimation method for the model parameters with a novel implementation of the importance sampling technique. We empirically investigate how the yields in Germany, France, Italy and Spain have been a ected by monetary policy measures of the European Central Bank. We model the euro area interbank lending rate EONIA by a log-normal distribution and the bond market purchases within the ECB's Securities Markets Programme by a Poisson distribution. We nd evidence that the bond market interventions had a direct and temporary e ect on the yield curve lasting up to ten weeks, and nd limited evidence that purchases changed the relationship between the EONIA rate and the term structure factors.

Can Spanned Term Structure Factors Drive Stochastic Yield Volatility?

The ability of the usual factors from empirical arbitrage-free representations of the term structure -- that is, spanned factors -- to account for interest rate volatility dynamics has been much debated. We examine this issue with a comprehensive set of new arbitrage-free term structure specifications that allow for spanned stochastic volatility to be linked to one or more of the yield curve factors. Using U.S. Treasury yields, we find that much realized stochastic volatility cannot be associated with spanned term structure factors. However, a simulation study reveals that the usual realized volatility metric is misleading when yields contain plausible measurement noise. We argue that other metrics should be used to validate stochastic volatility models

Can Spanned Term Structure Factors Drive Stochastic Yield Volatility?

The ability of the usual factors from empirical arbitrage-free representations of the term structure --- that is, spanned factors --- to account for interest rate volatility dynamics has been much debated. We estimate new arbitrage-free Nelson-Siegel (AFNS) term structure specifications that allow for stochastic volatility to be linked to one or more of the spanned AFNS yield curve factors. Our results using U.S. Treasury yields indicate that much observed stochastic volatility cannot be associated with spanned term structure factors in terms of time-series correlations at high frequency. However, a simulation study reveals this frequently to be the case when the yield data contain a certain level of noise like ours. As a consequence, we argue that other measures must be used to validate models with stochastic volatility and provide several examples.

Keep it Simple: Dynamic Bond Portfolios Under Parameter Uncertainty

We empirically investigate the importance of parameter uncertainty to bond investors. Using a Bayesian approach, we quantify the expected utility loss due to parameter uncertainty from following seemingly optimal dynamic portfolio strategies. Expected utility losses are increasing in the number of term structure factors and the complexity of the risk premium specification. Even with long data sets to estimate parameters, an investor with typical risk aversion is better off following a portfolio strategy implied by a misspecified but parsimonious model than a correctly-specified but difficult-to-estimate three-factor affine model with time-varying risk premia.