The determinants of corporate bond yields

Previous studies have found that common factors explain a high proportion of corporate bond yields. In this paper, we test whether there is a systematic risk premium beyond that implied by a risk-neutral term structure model. We propose a reduced-form term structure model that incorporates both default and tax effects. After controlling the effects of personal taxes and default risk, empirical tests show that at least two of the Fama–French factors are important for corporate bond yields. Our results suggest that term structure models should incorporate aggregate common risk factors in order to better explain the dynamics of corporate bond yields.     

UNCOVERING THE COMMON RISK‐FREE RATE IN THE EUROPEAN MONETARY UNION

We introduce longitudinal factor analysis (LFA) to extract the common risk‐free (CRF) rate from a sample of sovereign bonds of countries in a monetary union. Since LFA exploits the typically very large longitudinal dimension of bond data, it performs better than traditional factor analysis methods that rely on the much smaller cross‐sectional dimension. European sovereign bond yields for the period 2006–2011 are decomposed into a CRF rate, a default risk premium and a liquidity risk premium. Our empirical findings suggest that investors chase both credit quality and liquidity, and that they price double default risk on credit default swaps. Copyright © 2013 John Wiley & Sons, Ltd.     

Assessment of time-varying systemic risk in credit default swap indices: Simultaneity and contagiousness

This study assesses systemic risk inherent in credit default swap (CDS) indices using empirical and statistical analyses. We define systemic risk in two perspectives: the possibilities of simultaneous and contagious defaults, and then quantify them separately across benchmark models. To do so, we employ a Marshall-Olkin copula model to measure simultaneous default risk, and an interacting intensity-based model to capture contagious default risk. For an empirical test, we collect daily data for the iTraxx Europe CDS index and its tranche prices in the period from 2005 to 2014, and calibrate model parameters varying across time. In addition, we select forecasting models that have minimal prediction errors for the calibrated time series. Finally, we identify significant changes in each dynamic of systemic risk indicator before and after default and downgrade-related episodes that have occurred in the global financial crisis and European sovereign debt crisis.     

Jumps in intensity models: investigating the performance of Ornstein-Uhlenbeck processes in credit risk modeling

This work presents intensity-based credit risk models where the default intensity of the point process is modeled by an Ornstein-Uhlenbeck type process completely driven by jumps. Under this model we compute the default probability over time by linking it to the characteristic function of the integrated intensity process. In case of the Gamma and the Inverse Gaussian Ornstein-Uhlenbeck processes this leads to a closed-form expression for the default probability and to a straightforward estimate of credit default swaps prices. The model is calibrated to a series of real-market term structures and then used to price a digital default put option. Results are compared with the well known cases of Poisson and CIR dynamics. Possible extensions of the model to the multivariate setting are finally discussed.     

The time-series properties of the risk premium in the Yen/Dollar exchange market

In this paper a VAR model is employed to construct a measure of the conditional expectations of the future yen/dollar spot rate. This measure allows us to examine the dynamics of an ex-ante time-series for the risk premium in the market. The VAR model produces ‘better’ forecasts than the survey responses for turbulent periods such as 1981–1982 and 1984–1985. The VAR-generated expectations are then used to construct a risk premium time-series. This risk premium series seems to be more reliable than the ones obtained using either survey data on expectations of the future spot exchange rate or the ex-post realized spot exchange rate. Tests on the risk premium series suggest that a risk premium was present, but that it was virtually constant throughout the sample. The conditional variance of the risk premium changed over time, but its unconditional distribution seemed stable across subsamples. Despite these features, the volatility of the series was substantial and varied considerably throughout the sample.     

大股东股权质押、风险承担水平与债券违约风险

基于2015—2019年发生实质性债券违约的上市公司数据,运用二元Probit回归模型,从风险承担水平角度研究大股东股权质押对上市公司债券违约风险的影响。研究发现：大股东股权质押率与债券违约风险显著正相关,风险承担水平在股权质押与债券违约之间发挥了中介作用,高股权质押率的企业通过对其风险承担水平的影响,增加债券违约风险。进一步研究产权性质和信息质量差异对股权质押与债券违约关系的影响,结果显示,在不同产权性质、不同程度信息质量组别中,股权质押比例对债券违约的正向影响均存在差异,且该正向作用在非国有企业、低信息质量组中更为强烈。     

When was the U.S. housing downturn predictable? A comparison of univariate forecasting methods

This paper uses three classes of univariate time series techniques (ARIMA type models, switching regression models, and state-space/structural time series models) to forecast, on an ex post basis, the downturn in U.S. housing prices starting around 2006. The performance of the techniques is compared within each class and across classes by out-of-sample forecasts for a number of different forecast points prior to and during the downturn. Most forecasting models are able to predict a downturn in future home prices by mid 2006. Some state-space models can predict an impending downturn as early as June 2005. State-space/structural time series models tend to produce the most accurate forecasts, although they are not necessarily the models with the best in-sample fit.     

Endemic-epidemic models with discrete-time serial interval distributions for infectious disease prediction

Multivariate count time series models are an important tool for analyzing and predicting the spread of infectious disease. We consider the endemic-epidemic framework, a class of autoregressive models for infectious disease surveillance counts, and replace the default autoregression on counts from the previous time period with more flexible weighting schemes inspired by discrete-time serial interval distributions. We employ three different parametric formulations, each with an additional unknown weighting parameter estimated via a profile likelihood approach, and compare them to an unrestricted nonparametric approach. The new methods are illustrated in a univariate analysis of dengue fever incidence in San Juan, Puerto Rico, and a spatiotemporal study of viral gastroenteritis in the 12 districts of Berlin. We assess the predictive performance of the suggested models and several reference models at various forecast horizons. In both applications, the performance of the endemic-epidemic models is considerably improved by the proposed weighting schemes.     

Returns-to-scale and the equity premium puzzle

A model of heterogenous firms facing idiosyncratic risk is proposed which generates an equity premium of 6 per cent and a risk-free rate of 1.5 per cent even if aggregate returns are risk-free. The premium in this model reflects diminishing returns-to-scale and the fact that equity shares are equal claims to firm output. In the bond market, the risk-free rate reflects trade in assets at marginal rates of return with a linear technology and thus the equity premium in excess returns reflects a comparison of average returns with marginal returns. In the model, credit constraints lower the equity premium and, absent such constraints, the equity premium would roughly double. Since the model may be interpreted as a model of entrepreneurship, this paper also presents estimates from a structural model of entrepreneurship using data from the Survey of Consumer Finances and also finds only a modest level of risk aversion is sufficient to replicate entrepreneurial returns.     

Time series properties of ARCH processes with persistent covariates

We investigate the time series properties of a volatility model, whose conditional variance is specified as in ARCH with an additional persistent covariate. The included covariate is assumed to be an integrated or nearly integrated process, with its effect on volatility given by a wide class of nonlinear volatility functions. In the paper, such a model is shown to generate many important characteristics that are commonly observed in financial time series. In particular, the model yields persistence in volatility, and also well predicts leptokurtosis. This is true for any type of volatility functions considered in the paper, as long as the covariate is integrated or nearly integrated. Stationary covariates cannot produce important characteristics observed in many financial time series. We present two empirical applications of the model, which show that the default premium (the yield spread between Baa and Aaa corporate bonds) affects stock return volatility and the interest rate differential between two countries accounts for exchange rate return volatility. The forecast evaluation shows that the model generally outperforms GARCH and FIGARCH at relatively lower frequencies.     

Do interest rate options contain information about excess returns?

There is strong empirical evidence that long-term interest rates contain a time-varying risk premium. Options may contain valuable information about this risk premium because their prices are sensitive to the underlying interest rates. We use the joint time series of swap rates and interest rate option prices to estimate dynamic term structure models. The risk premiums that we estimate using option prices are better able to predict excess returns for long-term swaps over short-term swaps. Moreover, in contrast to the previous literature, the most successful models for predicting excess returns have risk factors with stochastic volatility. We also show that the stochastic volatility models we estimate using option prices match the failure of the expectations hypothesis.     

Analysis of high dimensional multivariate stochastic volatility models

This paper is concerned with the Bayesian estimation and comparison of flexible, high dimensional multivariate time series models with time varying correlations. The model proposed and considered here combines features of the classical factor model with that of the heavy tailed univariate stochastic volatility model. A unified analysis of the model, and its special cases, is developed that encompasses estimation, filtering and model choice. The centerpieces of the estimation algorithm (which relies on MCMC methods) are: (1) a reduced blocking scheme for sampling the free elements of the loading matrix and the factors and (2) a special method for sampling the parameters of the univariate SV process. The resulting algorithm is scalable in terms of series and factors and simulation-efficient. Methods for estimating the log-likelihood function and the filtered values of the time-varying volatilities and correlations are also provided. The performance and effectiveness of the inferential methods are extensively tested using simulated data where models up to 50 dimensions and 688 parameters are fit and studied. The performance of our model, in relation to various multivariate GARCH models, is also evaluated using a real data set of weekly returns on a set of 10 international stock indices. We consider the performance along two dimensions: the ability to correctly estimate the conditional covariance matrix of future returns and the unconditional and conditional coverage of the 5% and 1% value-at-risk (VaR) measures of four pre-defined portfolios.     

Modeling frailty-correlated defaults using many macroeconomic covariates

We propose a novel time series panel data framework for estimating and forecasting time-varying corporate default rates subject to observed and unobserved risk factors. In an empirical application for a U.S. dataset, we find a large and significant role for a dynamic frailty component even after controlling for more than 80% of the variation in more than 100 macro-financial covariates and other standard risk factors. We emphasize the need for a latent component to prevent a downward bias in estimated default rate volatility and in estimated probabilities of extreme default losses on portfolios of U.S. debt. The latent factor does not substitute for a single omitted macroeconomic variable. We argue that it captures different omitted effects at different times. We also provide empirical evidence that default and business cycle conditions partly depend on different processes. In an out-of-sample forecasting study for point-in-time default probabilities, we obtain mean absolute error reductions of more than forty percent when compared to models with observed risk factors only. The forecasts are relatively more accurate when default conditions diverge from aggregate macroeconomic conditions.     

Forecasting and stress testing credit card default using dynamic models

We present discrete time survival models of borrower default for credit cards that include behavioural data about credit card holders and macroeconomic conditions across the credit card lifetime. We find that dynamic models which include these behavioural and macroeconomic variables provide statistically significant improvements in model fit, which translate into better forecasts of default at both account and portfolio levels when applied to an out-of-sample data set. By simulating extreme economic conditions, we show how these models can be used to stress test credit card portfolios.     

After Modigliani,Miller, and Hamada: A new way to estimate cost of capital

In this article, we discuss the impact of financial debt on shareholder value using a new approach that aims: (a) to explain the effect that leverage from debt has on a stock’s systematic risk, or what we shall call here “the systematic cost of leverage,” and (b) to account for default risk in the cost of equity, or what we shall call here “the cost of default.” Our assessment of systematic risk is based on a stochastic approach that is materially different from the one proposed by Hamada: the risk premium remunerates the investor for the probability of equity (expressed as market value) generating a return below that of the risk‐free rate. Furthermore, the approach we use to account for default risk is derived from reduced‐form models, but in this case, (a) we use real probabilities of default and not risk‐neutral probabilities, and (b) we extend the approach to stocks.     

Debt stabilization games in the presence of risk premia

As a result of the recent financial crisis and the ensuing economic recession, fiscal deficits have soared in many OECD countries. As a consequence, government debt has been on the rise again after a period of stable or declining government debt. In this paper we analyze debt stabilization in a country that features endogenous risk premia, imposed by financial markets that evaluate the probability of debt default by governments. Endogenous risk premia arise by assuming, e.g., simple linear relations between risk premia and the level of debt. As a result the real interest rate on government debt can be written as a constant (measuring the risk-free real interest rate corrected for real output growth) plus an endogenous risk premium that depends on the debt level. We bring such an endogenous risk premium into Tabellini (1986) model and analyze the impact of it. This gives rise to a non-linear differential game. We solve this game for both a cooperative setting and a non-cooperative setting. The non-cooperative game is solved under an open-loop information structure. We present a bifurcation analysis w.r.t. the risk premium parameter.     

Forecasting cryptocurrencies under model and parameter instability

This paper studies the predictability of cryptocurrency time series. We compare several alternative univariate and multivariate models for point and density forecasting of four of the most capitalized series: Bitcoin, Litecoin, Ripple and Ethereum. We apply a set of crypto-predictors and rely on dynamic model averaging to combine a large set of univariate dynamic linear models and several multivariate vector autoregressive models with different forms of time variation. We find statistically significant improvements in point forecasting when using combinations of univariate models, and in density forecasting when relying on the selection of multivariate models. Both schemes deliver sizable directional predictability.     

INFORMATION IN THE YIELD CURVE: A MACRO‐FINANCE APPROACH

We use a macro‐finance model, incorporating macroeconomic and financial factors, to study the term premium in the US bond market. Estimating the model using Bayesian techniques, we find that a single factor explains most of the variation in bond risk premiums. Furthermore, the model‐implied risk premiums account for up to 40% of the variability of one‐ and two‐year excess returns. Using the model to decompose yield spreads into an expectations and a term premium component, we find that, although this decomposition does not seem important to forecast economic activity, it is crucial to forecast inflation for most forecasting horizons. Copyright © 2012 John Wiley & Sons, Ltd.     

Measuring connectedness of euro area sovereign risk

We introduce a method for measuring the default risk connectedness of euro zone sovereign states using credit default swap (CDS) and bond data. The connectedness measure is based on an out-of-sample variance decomposition of model forecast errors. Due to its predictive nature, it can respond to crisis occurrences more quickly than common in-sample techniques. We determine the sovereign default risk connectedness using both CDS and bond data in order to obtain a more comprehensive picture of the system. We find evidence that there are several observable factors that drive the difference between CDS and bonds, but both data sources still contain specific information for connectedness spill-overs. In general, we can identify countries that impose risk on the system and the respective spill-over channels. Our empirical analysis covers the years 2009–2014, such that the recovery paths of countries exiting EU and IMF financial assistance schemes and the responses to the ECB’s unconventional policy measures can be analyzed.     

自愿披露客户信息影响了债券违约风险吗？

以2010—2021年沪深A股上市公司发行的公司债为样本,实证检验了自愿披露客户信息对债券违约风险的影响。研究发现,自愿披露客户信息产生的额外风险加剧了债券违约风险,这种影响在高专有成本和高融资约束的企业中表现更为显著,但企业发行的绿色债券并未受到影响。机制检验表明,经营风险增加和机构投资者持股降低是自愿披露客户信息影响债券违约风险的重要途径。研究表明,企业需要慎重考虑披露客户信息带来的风险,审慎制定信息披露策略。