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Outlines Heath, Jarrow and Morton’s (1992) method (MJM) for modelling interest rates and refers to other research showing that although it is generally non‐Markov, this can be modified if the volatility structure depends on relative maturity term rather than calendar maturity date. Develops a re‐indexed MJM model, applies it to 1975‐1991 data on non‐callable US treasury bills, notes and bonds; and compares its goodness of fit with Jordan (1984). Finds the forward function consistent with constant parameters, that state variables can be identified from the cross‐section estimates and that they have zero mean first differences when analysed through time series. Concludes that the forward function follows a martingale and promises further research.

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.

Recent U.S. Treasury yields have been constrained to some extent by the zero lower bound (ZLB) on nominal interest rates. Therefore, we compare the performance of a standard affine Gaussian dynamic term structure model (DTSM), which ignores the ZLB, to a shadow-rate DTSM, which respects the ZLB. Near the ZLB, we find notable declines in the forecast accuracy of the standard model, while the shadow-rate model forecasts well. However, 10-year yield term premiums are broadly similar across the two models. Finally, in applying the shadow-rate model, we find no gain from estimating a slightly positive lower bound on U.S. yields.

This work aims to study the break-even inflation rates (BEIRs), a widely used market-based measure of expected inflation. The authors focus on Italian Government bonds, one of the most liquid debt markets in the euro area.

The authors set up an auto-regressive distributed lag model and regress the BEIR on a set of variables that proxy inflation, market risk aversion, protection against deflation, credit as well as liquidity risk to get some insights into the importance of these factors. Subsequently, to disentangle market participants’ inflation expectations from their associated risk premia, the authors estimate a term structure model for the joint pricing of the Italian Government’s nominal and real yield curves, considering also a credit and a liquidity pricing factor.

The results show that BEIRs could be a misleading measure of the expected inflation due to the importance of the inflation risk premium and the credit risk effect. According to the estimates, the decrease of market-based measures of inflation observed in the last part of the sample period seems to reflect a lowering of both inflation expectations and risk premia. Inflation premia co-move with a measure of the tail risk of the long-term inflation distribution, signalling that investors become more concerned with downside risks.

This study complements the existing literature primarily based on the USA and euro area data focusing on the Italian market. To this end, the authors modify and adapt a well-known term structure model developed for nominal and real curves.

This paper aims to illustrate how a Bayesian approach to yield fitting can be implemented in a non-parametric framework with automatic smoothing inferred from the data. It also briefly illustrates the advantages of such an approach using real data.

The paper uses an infinite dimensional (functional space) approach to inverse problems. Numerical computations are carried out using a Markov Chain Monte-Carlo algorithm with several tweaks to ensure good performance. The model explicitly uses bid-ask spreads to allow for observation errors and provides automatic smoothing based on them.

A non-parametric framework allows to capture complex shapes of zero-coupon yield curves typical for emerging markets. Bayesian approach allows to assess the precision of estimates, which is crucial for some applications. Examples of estimation results are reported for three different bond markets: liquid (German), medium liquidity (Chinese) and illiquid (Russian).

The result shows that infinite-dimensional Bayesian approach to term structure estimation is feasible. Market practitioners could use this approach to gain more insight into interest rates term structure. For example, they could now be able to complement their non-parametric term structure estimates with Bayesian confidence intervals, which would allow them to assess statistical significance of their results.

The model does not require parameter tuning during estimation. It has its own parameters, but they are to be selected during model setup.

This chapter analyses the ability of some structural models to predict corporate bankruptcy. The study extends the existing empirical work on default risk in two ways. First, it estimates the expected default probabilities (EDPs) for a sample of bankrupt companies in the USA as a function of volatility, debt ratio, and other company variables. Second, it computes default correlations using a copula function and extracts common or latent factors that drive companies’ default correlations using a factor-analytical technique. Idiosyncratic risk is observed to change significantly prior to bankruptcy and its impact on EDPs is found to be more important than that of total volatility. Information-related tests corroborate the results of prediction-orientated tests reported by other studies in the literature; however, only a weak explanatory power is found in the widely used market-to-book assets and book-to-market equity ratio. The results indicate that common factors, which capture the overall state of the economy, explain default correlations quite well.

The purpose of this paper is to ascertain the pattern of the implied volatility function for currency options traded on the National Stock Exchange of India (NSE), identify its potential determinants and to investigate any seasonality in the pattern.

The paper examines four different specifications for the implied volatility smile of exchange-traded dollar-rupee options. These specifications are tested by running Ordinary Least Squares (OLS) regressions on a daily basis for all options over the entire sample period. Seven potential determinants for the shape of the volatility function are identified. Contemporaneous and lead-lag relationships between these determinants and the shape of the volatility function are examined using OLS and multivariate VAR. Impulse response functions are employed to test the strength and persistence of the lead-lag relations. Seasonality of the smile pattern is tested using OLS.

The study shows that the implied volatility function for dollar-rupee options is asymmetric and varies with the time to maturity of the option. Historical volatility, momentum and jumps in the exchange rate, time to maturity, traded volume of options and volatility in the stock market appear to Granger-cause the shape of the volatility smile. Feedback causality is observed from the shape of the smile to the volatility, momentum and jumps in the exchange rate and trading volume of currency options. A weak day-of-the-week effect is observed in the pattern of the volatility smile.

The study sheds light on the potential determinants of the smile and highlights the predictive power of the smile which findings can be useful to market practitioners for pricing and hedging of dollar-rupee options. The study has strong practical implications during a period of increased volatility in the dollar-rupee pair.

Most of the existing literature regarding implied volatility smiles has focused either on the volatility smile of US equity index options or that of major liquid currencies. There is a need for such studies in the context of options on emerging market currencies such as the Indian rupee which are characterized by thin trading and frequent central bank intervention and signaling. To the best of the author’s knowledge this study is the first to focus on the volatility smile of exchange-traded options on the US dollar–Indian rupee.

This paper aims to investigate the impact of oil price shocks on the Turkish sovereign yield curve factors.

To extract the latent factors (level, slope and curvature) of the Turkish sovereign yield curve, we estimate conventional Nelson and Siegel (1987) model with nonlinear least squares. Then, we decompose oil price shocks into supply, demand and risk shocks using structural VAR (structural VAR) models. After this separation, we apply Engle (2002) dynamic conditional correlation GARCH (DCC-GARCH (1,1)) method to investigate time-varying co-movements between yield curve factors and oil price shocks. Finally, using the LP (local projections) proposed by Jorda (2005), we estimate the impulse-response functions to examine the impact of different oil price shocks on yield curve factors.

Our results demonstrate that the various oil price shocks influence the yield curve factors quite differently. A supply shock leads to a statistically significant increase in the level factor. This result shows that elevated oil prices due to supply disruptions are interpreted as a signal of a surge in inflation expectations since the cost channel prevails. Besides, unanticipated demand shocks have a positive impact on the slope factor as a result of the central bank policy response for offsetting the elevated inflation expectations. Finally, a risk shock is associated with a decrease in the curvature factor indicating that risk shocks influence the medium-term bonds due to the deflationary pressure resulting from depressed economic conditions.

Our results provide new insights to understand the driving forces of yield curve movements induced by various oil shocks to formulate appropriate policy responses.

The study contributes to the literature by two main dimensions. First, the recent oil shock identification scheme of Ready (2018) is modified using the “geopolitical oil price risk index” to capture the changes in the risk perceptions of oil markets driven by geopolitical tensions such as terrorism and conflicts and sanctions. The modified identification scheme attributes more power to demand shocks in explaining the variation of the oil price compared to that of the baseline scheme. Second, it provides recent evidence that distinguishes the impact of oil demand and supply shocks on Turkey's yield curve.

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.