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The purpose of the paper is to investigate the global and regional influences on the domestic term structure of nine Asian economies.

The dynamic Nelson Siegel model was used to extract the latent factors of a country’s yield curve movements in a state-space framework using the Kalman filter. The global and regional factors of the yield curve were extracted using the dynamic factor model. Further, the Bayesian inference of Gibbs sampling approach was used to identify the influence of global and regional factors on the domestic yield curve.

The results suggest that financial integration does not reduce the control of monetary authorities on the front end of the yield curve, and long-term interest rate is the potential transmission channel through which the contagion of the financial crisis spreads.

The results of this study would help the monetary authorities to understand the efficacy of the monetary policy transmission mechanism. It also offers the global investors diversification opportunities for investing in the Asian bond markets.

It is one of the earliest attempts to capture the global and regional yield curve movements and their impact on the emerging Asian economies yield curve. It contributes to literature by identifying the linkages in the long-term factor that is the potential channel through which crisis spreads.

The purpose of this paper is to analyze and forecast the Chinese term structure of interest rates using functional principal component analysis (FPCA).

The authors propose an FPCA-K model using FPCA. The forecasting of the yield curve is based on modeling functional principal component (FPC) scores as standard scalar time series models. The authors evaluate the out-of-sample forecast performance using the root mean square and mean absolute errors.

Monthly yield data from January 2002 to December 2016 are used in this paper. The authors find that in the full sample, the first two FPCs account for 98.68 percent of the total variation in the yield curve. The authors then construct an FPCA-K model using the leading principal components. The authors find that the FPCA-K model compares favorably with the functional signal plus noise model, the dynamic Nelson-Siegel models and the random walk model in the out-of-sample forecasting.

The authors propose a functional approach to analyzing and forecasting the yield curve, which effectively utilizes the smoothness assumption and conveniently addresses the missing-data issue.

To the best knowledge, the authors are the first to use FPCA in the modeling and forecasting of yield curves.

Using a discrete-time version of the arbitrage-free Nelson–Siegel (AFNS) term structure model, the authors examine how yield curves in the US and China react to exchange rate policy shocks as China introduces gradual reforms to make its exchange rate regime more flexible. The paper aims to discuss this issue.

The authors characterize the specification of the discrete-time AFNS model, prove the uniqueness of the solution for model identification, perform specification analysis on its canonical form and detail the MCMC estimation method with a fast and reliable prior extraction step.

Model decomposition reveals that in the US yield responses, changes in risk premia for medium- to long-term yields dominate changes in yield expectation for short- to medium-term yields, indicating that the portfolio rebalancing effect due to varying risk perception is stronger than the signaling effect due to policy rate expectation.

The results are helpful in diagnosing market sentiment and exchange rate risk pricing as China further internationalizes its currency.

The methodology can be easily extended to study yield curve responses to other scenarios of policy shocks or regime changes.

This study aims to shed more light on the relationship between probability of default, investment horizons and rating classes to make decision-making processes more efficient.

Based on credit default swaps (CDS) spreads, a methodology is implemented to determine the implied default probability and the implied rating, and then to estimate the term structure of the market-implied default probability and the transition matrix of implied rating. The term structure estimation in discrete time is conducted with the Nelson and Siegel model and in continuous time with the Vasicek model. The assessment of the transition matrix is performed using the homogeneous Markov model.

The results show that the CDS-based implied ratings are lower than those based on Thomson Reuters approach, which can partially be explained by the fact that the real-world probabilities are smaller than those founded on a risk-neutral framework. Moreover, investment and sub-investment grade companies exhibit different risk profiles with respect of the investment horizons.

The originality of this study consists in determining the implied rating based on CDS spreads and to detect the difference between implied market rating and the Thomson Reuters StarMine rating. The results can be used to analyze credit risk assessments and examine issues related to the Thomson Reuters StarMine credit risk model.

The politically unstable economies have high and volatile sovereign spread. The purpose of this paper is to investigate the impact of geopolitical uncertainty on sovereign bond yields.

The sovereign yields at various maturities were decomposed into three factors, namely, level, slope and curvature, using the Dynamic Nelson Siegel model. The relationship between geopolitical uncertainty and the yield curve factors was examined using a quantile causality test.

The study found that at the extreme high-rate regime, geopolitical uncertainty causes the yield curve factors positively, indicating bond investors demand a higher return for geopolitical uncertainty. On the other hand, during extreme low-rate regime geopolitical causes the short- and medium-term factors negatively. The extreme low-rate regime indicates the period of economic slowdown. During this regime, the central banks try to reduce the short-term rates to stimulate growth.

This is one of the few papers that investigates the relationship between the geopolitical risk and sovereign bond yields at the various maturities and interest rate regimes. Understanding the relationship between the geopolitical risk and short-term rates would help the central banks the efficacy of their policy actions. The long-term rates are influenced by the global investor preferences; examining the relationship with the long-term rates would help the investors frame the trading strategies.

Solvency-II is the current regulatory framework of insurance companies in the European Union. Under this standard, European Insurance and Occupational Pension Authority (EIOPA), as a regulatory board, has established that the Smith–Wilson (SW) model can be used as the model to estimate interest rate curve. This paper aims to analyze whether this model adjusts to the market curve better than Nelson–Siegel (NS) and whether the values set for the parameters are adequate.

This empirical study analyzes whether the SW interest rate curve shows lower root mean squared errors than the NS curve for a sample of daily prices of Spanish Government bonds between 2014 and 2019.

The results indicate that NS adjusts the market data better, the parameters recommended by the EIOPA correspond to the maximum values observed in the sample period and the current recommended curve for insurance companies underestimates company operations.

This paper verifies that the criterion of the last liquid point does not allow for selecting an optimal sample to adjust the curve and criteria based on prices without arbitrage opportunities are more appropriate.

This study comprehensively aims to review the key influential and intellectual aspects of spillovers between Islamic and conventional financial markets.

The study uses the bibliometric and content analysis methods using the VOSviewer software to analyse 52 academic documents derived from the Web of Sciences (WoS) between 2015 and June 2022.

The results demonstrate the influential aspects of spillovers between Islamic and conventional financial markets, including the leading authors, journals, countries and institutions and the intellectual aspects of literature. These aspects are synthesised into four main streams: research between stock indexes; studies between stock indexes, oil and precious metal; works between Sukuk, bond and indexes; and empirical studies review. The authors also propose future research directions in spillovers between Islamic and conventional financial markets.

Our study is subject to several limitations. Firstly, the authors only used the WoS database. Secondly, the study only includes papers and reviews written in English from the WoS. This study assists academic scholars, practitioners and regulatory bodies in further exploring the suggested issues in future studies and improving and predicting economic and financial stability.

To the best of the authors’ knowledge, no extant empirical studies have been conducted in this area of research interest.

Forecasting the future movement of yield curves contains valuable information for both academic and practical issues such as bonding pricing, portfolio management, and government policies. The purpose of this paper is to develop a dynamic factor approach that can provide more precise and consistent forecasting results under various yield curve dynamics.

The paper develops a unified dynamic factor model based on Diebold and Li (2006) and Nelson and Siegel (1987) three-factor model to forecast the future movement yield curves. The authors apply the state-space model and the Kalman filter to estimate parameters and extract factors from the US yield curve data.

The authors compare both in-sample and out-of-sample performance of the dynamic approach with various existing models in the literature, and find that the dynamic factor model produces the best in-sample fit, and it dominates existing models in medium- and long-horizon yield curve forecasting performance.

The authors find that the dynamic factor model and the Kalman filter technique should be used with caution when forecasting short maturity yields on a short time horizon, in which the Kalman filter is prone to trade off out-of-sample robustness to maintain its in-sample efficiency.

Bond analysts and portfolio managers can use the dynamic approach to do a more accurate forecast of yield curve movements.

The enhanced forecasting approach also equips the government with a valuable tool in setting macroeconomic policies.

The dynamic factor approach is original in capturing the level, slope, and curvature of yield curves in that the decay rate is set as a free parameter to be estimated from yield curve data, instead of setting it to be a fixed rate as in the existing literature. The difference range of estimated decay rate provides richer yield curve dynamics and is the key to stronger forecasting performance.

We analyze the interaction among the common and country-specific components for the inflation rates in 12 euro area countries through a factor model with time-varying parameters. The variation of the model parameters is driven by the score of the predictive likelihood, so that, conditionally on past data, the model is Gaussian and the likelihood function can be evaluated using the Kalman filter. The empirical analysis uncovers significant variation over time in the model parameters. We find that, over an extended time period, inflation persistence has fallen and the importance of common shocks has increased relatively to that of idiosyncratic disturbances. According to the model, the fall in inflation observed since the sovereign debt crisis is broadly a common phenomenon since no significant cross-country inflation differentials have emerged. Stressed countries, however, have been hit by unusually large shocks.

This paper aims to propose a scenario-based approach for measuring interest rate risks. Many regulatory capital standards in banking and insurance make use of similar approaches. The authors provide a theoretical justification and extensive backtesting of our approach.

The authors theoretically derive a scenario-based value-at-risk for interest rate risks based on a principal component analysis. The authors calibrate their approach based on the Nelson–Siegel model, which is modified to account for lower bounds for interest rates. The authors backtest the model outcomes against historical yield curve changes for a large number of generated asset–liability portfolios. In addition, the authors backtest the scenario-based value-at-risk against the stochastic model.

The backtesting results of the adjusted Nelson–Siegel model (accounting for a lower bound) are similar to those of the traditional Nelson–Siegel model. The suitability of the scenario-based value-at-risk can be substantially improved by allowing for correlation parameters in the aggregation of the scenario outcomes. Implementing those parameters is straightforward with the replacement of Pearson correlations by value-at-risk-implied tail correlations in situations where risk factors are not elliptically distributed.

The paper assumes deterministic cash flow patterns. The authors discuss the applicability of their approach, e.g. for insurance companies.

The authors’ approach can be used to better communicate interest rate risks using scenarios. Discussing risk measurement results with decision makers can help to backtest stochastic-term structure models.

The authors’ adjustment of the Nelson–Siegel model to account for lower bounds makes the model more useful in the current low-yield environment when unjustifiably high negative interest rates need to be avoided. The proposed scenario-based value-at-risk allows for a pragmatic measurement of interest rate risks, which nevertheless closely approximates the value-at-risk according to the stochastic model.