Term Structure Of Interest Rates Research Articles

Near-term forward rate spread and commodity index relationship with real economic activity in Brazil

This study investigates the relationship between Brazilian economic cycles and two key indicators: the international S&P GSCI commodity index and the near-term forward spread derived from the Brazilian interest rate term structure. We find that the forward spread serves as a robust leading indicator of economic downturns in Brazil, offering recession forecasts up to four quarters in advance. This underscores a link between yield curve steepness and recession likelihood in Brazil. However, we uncover distinct cyclical behaviors in Brazil's term structure that diverge from patterns in the United States. Furthermore, our research reveals the significant influence of commodity price fluctuations on Brazil's economic performance, underscoring a strong correlation between commodity markets and economic outcomes. Examining term structure and commodity prices together provides novel insights into monitoring emerging market cycles. These indicators can assist policymakers and improve early warning systems, especially in developing economies like Brazil.

A Multicountry Model of the Term Structures of Interest Rates with a GVAR

Abstract Extant multicountry affine term structure models (ATSMs) handle global financial interdependence at the cost of increasing model dimensionality. To address this challenge, we propose a novel no-arbitrage ATSM with risk factor dynamics following a global vector-autoregressive (GVAR). Compared to referenced benchmarks, the GVAR − ATSM offers a more parsimonious representation, enables a faster estimation process, produces more precise model estimates, yields more plausible term premia dynamics, and improves bond yield out-of-sample forecasting. Furthermore, our empirical findings reveal the significant impact of China’s economic stances on Latin American yield curve dynamics, underscoring its importance as a global economic player.

Supply and Demand and the Term Structure of Interest Rates

Supply and Demand and the Term Structure of Interest Rates

Estimating time-varying factors’ variance in the string-term structure model with stochastic volatility

This paper presents a novel term structure of interest rate (TSIR) model with stochastic volatility and jumps (SVJ) that combines the market framework proposed by Brace et al. (1997) with the string-shock framework of Santa-Clara and Sornette (2001). In this model, the factors’ variance is estimated through the eigendecomposition of a variance–covariance matrix obtained with a measure of market volatility derived from out-of-the-money option prices and historical correlations of interest rates traded in the futures market. The stochastic evolution of the factors’ variance is governed by the 4/2 model developed by Grasselli (2017), including jumps. A novel method is employed to estimate the parameters of the SVJ model that minimizes the distance between the sample moments and the moments of a gamma distribution. The empirical application of the model in the Brazilian derivatives market demonstrates its effectiveness in accurately capturing the volatility smile of interest rate options.

A recursive method for fractional Hawkes intensities and the potential approach of credit risk

This article explores the potential approach for credit risk, which is an alternative to structural and reduced models. In the context of credit risk, it consists in assuming that the survival probability of a company is equal to the ratio of the expected value of a supermartingale divided by its initial value. This approach, that was previously used for modeling the term structure of interest rates, is extended by the use of a fractional self-exciting process, or fractional Hawkes process. This is a self-exciting process that is time-changed by the inverse of an alpha-stable subordinator. A self-exciting process allows to take into account the clustering of economic events such as defaults, whereas the time-change allows to properly fit highly convex survival probability curves. We derive a new recursive method that allows to compute all the moments of a self-exciting process intensity, as well as its time-changed counterpart. We show that this method can be used to approximate the survival probabilities in the potential approach. More specifically, we prove that the approximation converges and we provide a bound on the numerical error. Finally, we calibrate the model and show that it allows to properly fit survival probability curves that are highly convex.

Fixed-income average options: a pricing approach based on Gaussian mean-reverting cyclical models

AbstractThis paper values fixed-income (discrete- and continuous-time) European Asian and Australian options. We assume that the term structure of interest rates is modelled by the specification proposed in Moreno et al. (Econ Model 72:140–150, 2018, https://doi.org/10.1016/j.econmod.2018.01.015). We obtain closed-form expressions for the premiums of geometric average options and, for arithmetic average options, premiums are computed by numerical methods. We also perform a sensitivity analysis with respect to different parameters for both (geometric and arithmetic) options.

A Theory of the Term Structure of Interest Rates under Limited Household Risk Sharing

Abstract We present a theory in which the interaction between limited sharing of idiosyncratic labor income risk and labor adjustment costs (that endogenously arise through search frictions) determines interest rate dynamics. In the general equilibrium, the interaction of these two ingredients relates bond risk premiums, cross-sectional skewness of income growth, and labor market tightness. Our model rationalizes an upward-sloping average yield curve and predicts a negative relation between labor market tightness and bond risk premiums. We provide evidence for our theory’s mechanism and predictions. (JEL E24, E43, E44, G12, J64)

The Effects of Conventional and Unconventional Monetary Policy Shocks on US REITs Moments: Evidence from VARs with Functional Shocks

AbstractWe use a vector autoregressive model with functional shocks, capturing the shift of the entire term structure of interest rates on monetary policy announcement dates, to empirically evaluate the effects of conventional and unconventional monetary policy decisions on the Real Estate Investment Trusts (REITs) markets of the United States (US). Using 5-min interval intraday data, we analyze not only the impact on REITs returns, but also its realized variance (RV), realized jumps (RJ), realized skewness (RSK), and realized kurtosis (RKU) over the daily period of September 2008 to June 2021. While the effects of conventional monetary policy shocks on the moments of REITs returns tend to conform with economic theories, the same is not necessarily the case with unconventional monetary policy shocks. In addition, though monetary policy shocks have the most persistent and strongest effects on RJ, the extreme behaviour of the REITs market is also observed through RSK and RKU. Moreover, when we look into 10 REITs sectors, there is indeed heterogeneity in terms of the strength of the effect, but not so much in terms of the sign of responses of the various moments compared to the overall market. Our results have important implications for REITs market participants, given its exponential growth as an asset class.

The Relationship between SOFR and Market Yield of U.S. Treasury Securities

This paper introduces the characteristic of the new benchmark rate Secured Overnight Financing Rate (SOFR), explain its connection to the futures price, and show its potential to provide new insight into the argument on interest rate term structure. Based on the future price discovery theory, this study set up a hypothesis that short-term interest rates should contribute to forecasting long-term ones. The causality between interest rates in different terms of, which are separately represented by bilateral repurchase rate and the market yield of long-term U.S. government bonds, is explored in this research using the VAR model, as well as the Granger causality test. The conclusion of this research proves the hypothesis and indicates a single-direction causality between interest rate of different terms. However, the research also has drawbacks such as ignoring non-linear relations and evading the change of government policy. These problems need to be solved in the following study.

The term structure of interest rates and economic activity: Evidence from the COVID‐19 pandemic

AbstractThis paper tests the accuracy and predictability of two term structure models using both yields‐only and factor‐augmented specifications focusing on the recent COVID‐19 crisis. In addition, we test the predictive ability of the yield curve on economic activity for the United States and other advanced countries. We provide evidence that models with an enhanced information set, including COVID‐19 factors, improve interest rate forecasts for this period. Also, we point out that term structure models can determine future variations in economic activity but are time‐ and country‐sensitive. Finally, out‐of‐sample analysis reveals that the use of factor‐augmented term structure models, to reflect the current economic and market conditions, improves their forecasting accuracy.

Static term structure of interest rate construction with tension interpolation splines

<abstract><p>Traditional theories of term structure of interest rate consist of four major classical theories, including Pure Expectation Theory, Liquidity Preference Theory, Preferred Habitat Theory and Market Segmentation Theory. However, they cannot be well interpreted by the traditional static term structure of interest rate methods such as polynomial spline and exponential spline. To address problems on low precision and weak stability of traditional methods in constructing static interest rate term structure curve, in this paper, we introduce the tension interpolation spline based on a fourth-order differential equation with local tension parameters calculated by Generalized Reduced Gradient (GRG) algorithm. Our primary focus is to illustrate its better prediction effect and stability with an empirical study conducted using datum of treasury bonds. Then, we divided the datum into intra-sample datum for estimating tension parameters and out-of-sample datum for evaluating their robustness of predicting stochastics collected from Shanghai Stock Exchange on $ {2^{{\rm{nd}}}} $ February, 2019. According to the principle of total least squares and total least absolute deviations, the result shows that the tension interpolation spline model has better precision and stronger stability in prediction of out-of-sample treasury bonds prices compared with the model established by polynomial spline and exponential spline. In addition, it can better explain the Liquidity Preference Theory, which confirms that it is suitable for constructing the static term structure of interest rates in the securities exchange market.</p></abstract>

Credit rating algorithm of corporate bonds based on Gaussian process mixture model and improved K-means

The primary challenge in credit analysis revolves around uncovering the correlation between repayment terms and yield to maturity, constituting the interest rate term structure-an essential model for corporate credit term evaluation. Presently, interest rate term structures are predominantly examined through economic theoretical models and quantitative models. However, predicting treasury bond yields remains a challenging task for both approaches. Leveraging the clustering analysis algorithm theory and the attributes of an insurance company’s customer database, this paper enhances the K-means clustering algorithm, specifically addressing the selection of initial cluster centers in extensive sample environments. Utilizing the robust data fitting and analytical capabilities of the Gaussian process mixture model, the study applies this methodology to model and forecast Treasury yields. Additionally, the research incorporates customer credit data from a property insurance company to investigate the application of clustering algorithms in the analysis of insurance customer credit.

On the Bias of the Unbiased Expectation Theory

The unbiased expectation theory stipulates that long-term interest rates are determined by the market’s expectations of future short-term interest rates. According to this hypothesis, if investors have unbiased expectations about future interest rate movements, the forward interest rates should be good predictors of future spot interest rates. This hypothesis of the term structure of interest rates has long been a subject of debate due to empirical and theoretical challenges. Despite extensive research, a satisfactory explanation for the observed systematic difference between future spot interest rates and forward interest rates has not yet been identified. In this study, we approach this issue from an arbitrage theory perspective, leveraging on the connection between the expectation hypothesis and changes in probability measures. We propose that the observed bias can be explained by two adjustments: a risk premia adjustment, previously considered in the literature, and a stochastic adjustment that has been overlooked until now resulting from two measure changes. We further demonstrate that for specific instances of the Vasicek and Cox, as well as the Ingersoll and Ross, stochastic interest rate models, quantifying these adjustments reveals that the stochastic adjustment plays a significant role in explaining the bias, and ignoring it may lead to an overestimation of the required risk premia/aversion adjustment. Our findings extend beyond the realm of financial economic theory to have tangible implications for interest rate modelling. The capacity to quantify and distinguish between risk and stochastic adjustments empowers modellers to make more informed decisions, leading to a more accurate understanding of interest rate dynamics over time.

Evolution of Risks Facing Commercial Banks in Kenya and Associated Strategic Responses

Purpose: The main objective of this study was to explore the evolution of risks facing commercial banks in Kenya and their strategic responses
 Methodology: The study adopted a desktop methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library.
 Findings: The study found that the commercial banking sector in Kenya is operating within a highly dynamic and ever-evolving risk landscape. The risks faced by these banks are multifaceted and include economic, operational risk, regulatory, technological, systematic, sovereign risk and market-related challenges. It also added that the strategic responses adopted by commercial banks in Kenya are diverse and multifaceted. The conclusions drawn emphasize the need for continual vigilance and adaptation in risk management, the pivotal role of technology and digital transformation, and the significance of regulatory compliance. By taking these conclusions into account, commercial banks in Kenya can better position themselves to thrive in the dynamic and competitive financial landscape of the country.
 Unique Contribution to Theory, Practice and Policy: The Modern Portfolio theory, liquidity preference and the Liquidity Coverage Ratio (LCR), Agency theory, the term structure of interest rates and duration and Credit Scoring Model and the Resource Based View model may be used to anchor future studies on the evolution of risks facing commercial banks in Kenya and their strategic responses. This study makes significant contributions to theory by developing a risk evolution framework and strategic risk response models. It has practical implications by offering enhanced risk management strategies and guidance for financial decision-making for practitioners. Additionally, the research provides valuable insights for banking regulators policymakers, and investors in Kenya, supporting risk-based supervision and financial stability initiatives.

Parameterizing Debt Maturity

AbstractWe examine ways to describe the maturity structure of public debts using few parameters. We compile a novel data set of all promised future payments for U.S. and UK government debt from every month since 1869, and more recently for Peru, Poland, Egypt, and Nigeria. We show there is a unique parametric form which does not arbitrarily restrict debt issuance. We use this model to parsimoniously describe the evolution of public debt maturities and to characterize the relationship between maturity and the term structure of interest rates in the United States since 1940.

Modeling the interest rates term structure using Machine Learning: a Gaussian process regression approach

The correct modeling of the interest rates term structure should definitely be considered an aspect of primary importance since the forward rates and the discount factors used in any financial and risk analysis are calculated from such structure. The turbulence of the markets in recent years, with negative interest rates followed by their recent substantial rise, the period of the COVID pandemic crisis, the political instabilities linked to the war between Ukraine and Russia have very often led to observe anomalies in the shape of the interest rate curve that are difficult to represent using traditional econometric models, to the point that researchers have to address this modeling problem using Machine Learning methodologies. The purpose of this study is to design a model selection heuristic which, starting from the traditional ones (Nelson-Siegel, Svensson and de Rezende-Ferreira) up to the Gaussian Process (GP) Regression, is able to define the best representation for a generic term structure. This approach has been tested over the past five years on term structures denominated in five different currencies: the Swiss Franc (CHF), the Euro (EUR), the British Pound (GBP), the Japanese Yen (JPY) and the U.S. Dollar (USD).

Dynamic Shrinkage Priors for Large Time-Varying Parameter Regressions Using Scalable Markov Chain Monte Carlo Methods.

Time-varying parameter (TVP) regression models can involve a huge number of coefficients. Careful prior elicitation is required to yield sensible posterior and predictive inferences. In addition, the computational demands of Markov Chain Monte Carlo (MCMC) methods mean their use is limited to the case where the number of predictors is not too large. In light of these two concerns, this paper proposes a new dynamic shrinkage prior which reflects the empirical regularity that TVPs are typically sparse (i.e. time variation may occur only episodically and only for some of the coefficients). A scalable MCMC algorithm is developed which is capable of handling very high dimensional TVP regressions or TVP Vector Autoregressions. In an exercise using artificial data we demonstrate the accuracy and computational efficiency of our methods. In an application involving the term structure of interest rates in the eurozone, we find our dynamic shrinkage prior to effectively pick out small amounts of parameter change and our methods to forecast well.

Interest Rates Term Structure Models Driven by Hawkes Processes

Interest Rates Term Structure Models Driven by Hawkes Processes

Functional Cointegration Test for Expectation Hypothesis of the Term Structure of Interest Rates in China

Functional Cointegration Test for Expectation Hypothesis of the Term Structure of Interest Rates in China

The asymmetry and uncertainty effects on the response of the yield curve to Brazilian monetary policy

We investigate the influence of the Central Bank of Brazil’s Monetary Policy Committee (COPOM) decisions on the term structure of interest rate. We consider four possible drivers of market movements: (i) the effective surprise in the decision compared to market expectations; (ii) the asymmetric market responses to positive or negative changes in the monetary policy interest rate; (iii) the influence of economic uncertainty around meetings; and (iv) the effects of these changes on market behavior over time. We find that the monetary surprise coefficients have negative values for long-term rates. Moreover, we find an asymmetric response of the sign of the change in the monetary policy interest rate. There is evidence that, in general, positive changes in the interest rate are more difficult to predict by the market. Higher uncertainty increases the effect of the monetary policy surprise component in the short-term and reduces the effect in the long-term yield rates. Moreover, there is a reduction in the impact of the surprise component on the Brazilian interest rate market over time.