Bayesian approach to yield curve modelling with application to the simulation of EMU environments: generating scenarios by modelling yield curve movements

A Bayesian approach to yield curve modelling is developed where information on the current and recent yield curves is used to generate yield curve scenarios, and a model is proposed that generates return distribution for bonds. The predictive power of the model is developed by comparing out-of-sample lagged realized yields with forecast yields, and it is demonstrated that the returns generated by this scenario approach and those generated using the standard time series approach are consistent. The model is applied to pre-EMU and post-EMU environments. This paper assesses the implications of different assumptions on the early post-EMU environment for international bond portfolio selection, as well as the immediate short-term effect of EMU on risk and return.     

A structural framework for modelling contingent capital

This paper develops a structural model of contingent capital. In contrast to existing approaches we explicitly link the firm’s total payout to its cost of debt, leading to a total payout that is linear in—as opposed to proportional to—asset value. In the special case that asset value evolves as affine geometric Brownian motion we derive closed-form expressions for limiting (i.e. perpetual) bond values. The proposed model is flexible, so that it can be used to gauge the relative merits of different variations of contingent capital, and parsimonious, so that it is relatively easy to implement in practice. An empirical example using data from the Canadian banking sector is provided that illustrates how the model can generate insights into problems that are of interest to both regulators and issuers of contingent capital (e.g. what range of conversion prices would be consistent with regulatory guidelines, and how expensive is contingent debt over this range).     

A binomial approximation for two-state Markovian HJM models

This article develops a lattice algorithm for pricing interest rate derivatives under the Heath et al. (Econometrica 60:77–105, 1992) paradigm when the volatility structure of forward rates obeys the Ritchken and Sankarasubramanian (Math Financ 5:55–72) condition. In such a framework, the entire term structure of the interest rate may be represented using a two-dimensional Markov process, where one state variable is the spot rate and the other is an accrued variance statistic. Unlike in the usual approach based on the Nelson-Ramaswamy (Rev Financ Stud 3:393–430) transformation, we directly discretize the heteroskedastic spot rate process by a recombining binomial tree. Further, we reduce the computational cost of the pricing problem by associating with each node of the lattice a fixed number of accrued variance values computed on a subset of paths reaching that node. A backward induction scheme coupled with linear interpolation is used to evaluate interest rate contingent claims.     

A plausible model of yield curve dynamics

We present a simple model of yield curve dynamics which satisfies key criteria of plausibility. Specifically, yields are non-negative and the Sharpe ratio of a mean-variance optimal bond portfolio has a reasonable magnitude. The model matches stylized data features, in particular long-run moments of yields and excess returns.     

A structural decomposition of the US yield curve

By expanding the macro part of macro-finance models, historical fluctuations in US bond yields turn out to be largely consistent with the rational expectations hypothesis. We estimate a medium-scale macro-finance DSGE model of the term structure to establish this. Our finding contrasts with existing macro-finance models and suggests that their—small-scale or non-structural—perspective on the macroeconomy mutes expectations, thereby underestimating the expectations hypothesis’ potential. Out-of-sample forecasts are competitive with more flexible term structure models. Given the empirical validation, we interpret various episodes through the lens of the model and investigate which structural shocks cause the yield curve to contain information about future growth.     

A unified framework for robust modelling of financial markets in discrete time

Finance and Stochastics - We unify and establish equivalence between the pathwise and the quasi-sure approaches to robust modelling of financial markets in finite discrete time. In particular, we...     

A multi-factor Markovian HJM model for pricing American interest rate derivatives

This article presents a numerically efficient approach for constructing an interest rate lattice for multi-state variable multi-factor term structure models in the Makovian HJM [Econometrica 70 (1992) 77] framework based on Monte Carlo simulation and an advanced extension to the Markov Chain Approximation technique. The proposed method is a mix of Monte Carlo and lattice-based methods and combines the best from both of them. It provides significant computational advantages and flexibility with respect to many existing multi-factor model implementations for interest rates derivatives valuation and hedging in the HJM framework.

Exponential moments for HJM models with jumps

General HJM models driven by a Lévy process are considered. Necessary moment conditions for the discounted bond prices to be local martingales are derived. Under these moment conditions, it is proved that the discounted bond prices are local martingales if and only if a generalized HJM condition holds. Research supported in part by Polish KBN Grant P03A 034 29 “Stochastic evolution equations driven by Lévy noise”.     

Macroeconomic factors and yield curve for the emerging Indian economy

This article investigates the dynamic linkages between the estimated parameters of a zero coupon yield curve and macroeconomic variables like inflation, gross domestic product growth in the presence of a monetary policy indicator in India for the period July 1997 to February 2004. The study finds that there exists strong causality from financial factors, defined by three parameters of the yield curves (‘Level’, ‘Slope’, ‘Curvature’) to macroeconomic factors; growth, inflation and monetary policy indicators (changes in the call money rate). However, the causality in the opposite direction is found to be weaker. It is found that theyield and macro factors do not cause each other before the launch of a liquidity adjustment facility, so the evidence of causality from financial to macroeconomic factors can be attributed to the introduction of a liquidity adjustment facility in June 2000. The causality from yield factors to macro factors is primarily driven by the fact that the ‘changes in level’ of yield curve brings an impact on inflation through the changes in monetary policy. This finding suggests that monetary policy plays a key role in driving the causality. This also implies that the indirect instrument of monetary policy mechanism is becoming increasingly important to influence the aggregate demand in the economy.     

Disagreement about inflation and the yield curve

We show that inflation disagreement, not just expected inflation, has an impact on nominal interest rates. In contrast to expected inflation, which mainly affects the wedge between real and nominal yields, inflation disagreement affects nominal yields predominantly through its impact on the real side of the economy. We show theoretically and empirically that inflation disagreement raises real and nominal yields and their volatilities. Inflation disagreement is positively related to consumers’ cross-sectional consumption growth volatility and trading in fixed income securities. Calibrating our model to disagreement, inflation, and yields reproduces the economically significant impact of inflation disagreement on yield curves.     

Alternative models for hedging yield curve risk: An empirical comparison

We test alternative models of yield curve risk by hedging US Treasury bond portfolios through note/bond futures. We show that traditional implementations of models based on principal component analysis, duration vectors and key rate duration lead to high exposure to model errors and to sizable transaction costs, thus lowering the hedging quality. Also, this quality randomly varies from one model and hedging problem to the other. We show that accounting for the variance of modeling errors substantially reduces both hedging errors and transaction costs for all considered models. Additionally, it leads to much more stable weights in the hedging portfolios and – as a result – to more homogeneous hedging quality. On this basis, error-adjusted principal component analysis is found to systematically and significantly outperform alternative models.     

A nesting framework for Markov-switching GARCH modelling with an application to the German stock market

In this paper, we establish a generalized two-regime Markov-switching GARCH model which enables us to specify complex (symmetric and asymmetric) GARCH equations that may differ considerably in their functional forms across the two Markov regimes. We show how previously proposed collapsing procedures for the Markov-switching GARCH model can be extended to estimate our general specification by means of classical maximum-likelihood methods. We estimate several variants of the generalized Markov-switching GARCH model using daily excess returns of the German stock market index DAX sampled during the last decade. Our empirical study has two major findings. First, our generalized model outperforms all nested specifications in terms of (a) statistical fit (when model selection is based on likelihood ratio tests) and (b) out-of-sample volatility forecasting performance. Second, we find significant Markov-switching structures in German stock market data, with substantially differing volatility equations across the regimes.     

A Complete Markovian Stochastic Volatility Model in the HJM Framework

This paper considers a stochastic volatility version of the Heath, Jarrow and and Morton (1992) term structure model. Market completeness is obtained by adapting the Hobson and Rogers (1998) complete stochastic volatility stock market model to the interest rate setting. Numerical simulation for a special case is used to compare the stochastic volatility model against the traditional Vasicek (1977) model.     

A framework for the analysis of interview data from multiple field research sites

In general, papers reporting the results of research studies disclose little detail regarding attributes of study design, analytical processes and methods actually used by researchers. This paper describes in some depth the method choices and analytical protocol used in a field study project. The paper describes initially the link between research question, research design and analytical protocol. The major focus of the paper is the application of a systematic analytical protocol designed to encourage completeness and impartiality in the collection and analysis of qualitative data. These method issues are examined in the context of the author's experiences in the field and through the process of data analysis. The methods used are also examined critically in retrospect.     

An extended likelihood framework for modelling discretely observed credit rating transitions

The estimation of the parameters of a continuous-time Markov chain from discrete-time observations, also known as the embedding problem for Markov chains, plays in particular an important role for the modeling of credit rating transitions. This missing data problem boils down to a latent variable setting and thus, maximum likelihood estimation is usually conducted using the expectation-maximization (EM) algorithm. We illustrate that the EM algorithm is likely to get stuck in local maxima of the likelihood function in this specific problem setting and adapt a stochastic approximation simulated annealing scheme (SASEM) as well as a genetic algorithm (GA) to combat this issue. Above that, our main contribution is to extend our method GA by a rejection sampling scheme, which allows one to derive stochastic monotone maximum likelihood estimates in order to obtain proper (non-crossing) multi-year probabilities of default. We advocate the use of this procedure as direct constrained optimization (of the likelihood function) will not be numerically stable due to the large number of side conditions. Furthermore, the monotonicity constraint enables one to combine structural knowledge of the ordinality of credit ratings with real-life data into a statistical estimator, which has a stabilizing effect on far off-diagonal generator matrix elements. We illustrate our methods by Standard and Poor’s credit rating data as well as a simulation study and benchmark our novel procedure against an already existing smoothing algorithm.     

Economic determinants of the nominal treasury yield curve

Macroeconomic shocks account for most of the variability of nominal Treasury yields, inducing parallel shifts in the level of the yield curve. We develop a new approach to identifying macroeconomic shocks that exploits model-based empirical shock measures. Technology shocks shift yields through their effect on expected inflation and the term premium. Shocks to preferences for current consumption affect yields through their impact on real rates and expected inflation. For both shocks, the systematic reaction of monetary policy is an important transmission pathway. We find little evidence that fiscal policy shocks are an important source of interest rate variability.     

中国货币网移动版正式上线

为满足广大用户对银行间市场信息的移动应用需求,2014年1月,中国货币网移动版正式上线。移动版先期展示了银行间市场基准价格及市场行情等市场关注度较高的信息,后续将视市场需求添加更多内容与功能。版本特性：同步更新银行间市场信息三大板块：基准价格、外汇行情、本币行情