Aaa Corporate Bonds Research Articles

Quantification of GARCH (1, 1) Model Misspecification with Three Known Assumed Error Term Distributions

Generalized autoregressive conditional heteroscedastic (GARCH) models have become significant tools in the assessment of time series data, largely the traditional normal distribution of GARCH models because of their ease of use in practice. However, it is proven that high frequency financial data have heavy tails leading to the resulting estimates being inefficient. The Student-t and General error (GED) distributions are more capable of representing these financial series. In this paper, we conduct a series of simulations for the GARCH (1, 1) model assuming the error terms follow a Normal distribution. We fit the simulated returns to the GARCH (1, 1) with Normal, Student-t and GED innovations and varying sample sizes. The return series of Samsung electronics daily stock prices, Bitcoin-USD daily cryptocurrency and Moody’s seasoned AAA corporate bond yield (BAAA) are fitted to the GARCH (1, 1) with Normal, Student-t and GED innovations. We investigate if these models are subject to model misspecification if the error terms do not assume similar distributions as the simulated data and real data innovations. Model misspecification was identified in the GARCH model building process of the simulated and real datasets.

Modelling bursts and chaos regularization in credit risk with a deterministic nonlinear model

In this paper, we suggest a deterministic approach for modelling credit risk time series even in distressed periods (including COVID-19). We examine the Moody’s Seasoned Aaa Corporate Bond Yield Relative to Yield on 10-Year Treasury Constant Maturity as well as the ICE BofA US High Yield Index Option-Adjusted Spread and we find that the proposed model could fit well the alternation between periods of low and high volatility. This result is compared to the ARIMA-EGARCH model to determine how a chaotic deterministic model stands with respect to a stochastic model expressly designed for handling moving average, autoregression, cointegration and heteroscedastic volatility. According to recent literature, we find that both models give comparable results.

Dimension Reduction via Penalized GLMs for Non-Gaussian Response: Application to Stock Market Volatility

We fit U.S. stock market volatilities on macroeconomic and financial market indicators and some industry level financial ratios. Stock market volatility is non-Gaussian distributed. It can be approximated by an inverse Gaussian (IG) distribution or it can be transformed by Box–Cox transformation to a Gaussian distribution. Hence, we used a Box–Cox transformed Gaussian LASSO model and an IG GLM LASSO model as dimension reduction techniques and we attempted to identify some common indicators to help us forecast stock market volatility. Via simulation, we validated the use of four models, i.e., a univariate Box–Cox transformation Gaussian LASSO model, a three-phase iterative grid search Box–Cox transformation Gaussian LASSO model, and both canonical link and optimal link IG GLM LASSO models. The latter two models assume an approximately IG distributed response. Using these four models in an empirical study, we identified three macroeconomic indicators that could help us forecast stock market volatility. These are the credit spread between the U.S. Aaa corporate bond yield and the 10-year treasury yield, the total outstanding non-revolving consumer credit, and the total outstanding non-financial corporate bonds.

Explain the Determinants of Credit Spreads in the US

This paper focuses on how economic variables affect Baa corporate bond spreads in the US from January 1990 to December 2018. Credit spreads in this paper are defined as the Baa corporate bond yield minus the Aaa corporate bond yield, and are explained by four variables which are interest rates, the slope of yield curve, the stock market volatility and the economic environment. Cointegration analysis and VAR model are used in this paper to estimate the effects of the determinants of the credit spreads in the long-run and in the short-run respectively. The impacts of the industrial production index and the slope of yield curve on the Baa credit spread are negative, and the impacts of 10 year Treasury bond rate and the stock market volatility on the credit spreads are positive in the long run. In the short-run dynamic relationship, the impact of the industrial production index and the 10 years Treasury bond interest rate are negative for the credit spreads, and the slope of yield curve and stock market volatility are positive for the Baa credit spreads.

Monetary policy and long‐term interest rates: Evidence from the U.S. economy

AbstractThis paper addresses the ability of central banks to affect the structure of interest rates. We assess the causal relationship between the short‐term Effective Federal Funds Rate (FF) and long‐term interest rates associated with both public and private bonds and specifically, the 10‐Year Treasury Bond (GB10Y) and the Moody's Aaa Corporate Bond (AAA). To do this, we apply Structural Vector Autoregressive models to U.S. monthly data for the 1954–2018 period. Based on results derived from impulse response functions and forecast error variance decomposition, we find: a bidirectional relationship whenGB10Yis considered as the long‐term rate and a unidirectional relationship that moves from short‐ to long‐term interest rates whenAAAis considered. These conclusions show that monetary policy is able to permanently affect long‐term interest rates and the central bank has a certain degree of freedom in setting the levels of the short‐term policy rate.

Flights from Stocks

This paper examines the role of asset performance, volatility, illiquidity, and monetary policy on flight-to-quality episodes. We consider flights from stocks into long and short-term Treasuries, and Moody's AAA corporate bonds. Flights are shown to be associated with stock market volatility. Illiquidity appears to have a differential effect on different types of flights, as predicted by the asset pricing model with illiquidity by Vayanos (2004).Monetary policy, lagged and contemporaneous, is shown to decrease flight incidence. Our work also links market instability with the effects of overconfidence, as measured by the profitability of the momentum strategy.

Interest rate next-day variation prediction based on hybrid feedforward neural network, particle swarm optimization, and multiresolution techniques

Multiresolution analysis techniques including continuous wavelet transform, empirical mode decomposition, and variational mode decomposition are tested in the context of interest rate next-day variation prediction. In particular, multiresolution analysis techniques are used to decompose interest rate actual variation and feedforward neural network for training and prediction. Particle swarm optimization technique is adopted to optimize its initial weights. For comparison purpose, autoregressive moving average model, random walk process and the naive model are used as main reference models. In order to show the feasibility of the presented hybrid models that combine multiresolution analysis techniques and feedforward neural network optimized by particle swarm optimization, we used a set of six illustrative interest rates; including Moody’s seasoned Aaa corporate bond yield, Moody’s seasoned Baa corporate bond yield, 3-Month, 6-Month and 1-Year treasury bills, and effective federal fund rate. The forecasting results show that all multiresolution-based prediction systems outperform the conventional reference models on the criteria of mean absolute error, mean absolute deviation, and root mean-squared error. Therefore, it is advantageous to adopt hybrid multiresolution techniques and soft computing models to forecast interest rate daily variations as they provide good forecasting performance.

The impact of liquidity on senior credit index spreads during the subprime crisis

This paper examines the effects of liquidity during the 2007–09 crisis, focussing on the Senior Tranche of the CDX.NA.IG Index and on Moody's AAA Corporate Bond Index. It aims to understand whether the sharp increase in the credit spreads of these AAA-rated credit indices can be explained by worse credit fundamentals alone or whether it also reflects a lack of depth in the relevant markets, the scarcity of risk-capital, and the liquidity preference exhibited by investors. Using cointegration analysis and error correction models, the paper shows that during the crisis lower market and funding liquidity are important drivers of the increase in the credit spread of the AAA-rated structured product, whilst they are less significant in explaining credit spread changes for a portfolio of unstructured credit instruments. Looking at the experience of the subprime crisis, the study shows that when the conditions under which securitisation can work properly (liquidity, transparency and tradability) suddenly disappear, investors are left highly exposed to systemic risk.

The Efficiency of Bonds: Implications for Determining the Ratings of Corporate Bonds

This study develops a new test of market efficiency of bonds using data from the Yahoo Bond Screener on 15 March 2014. The test examines three types of bonds; zero coupon US Treasury Bonds, Fitch rated AAA corporate bonds and BB corporate bonds. AAA was the most efficient types of bonds, Zeros were next and BB rated bonds were the least efficient. This provides an empirical way to rate bonds which can supplement the expert judgment of analysts. It may also be used to identify bonds which are mispriced.

US Stocks and the US Dollar

The purpose of this paper is to study the relation between US stock prices, as exemplified by the S&P 500 stock index, and the change in eleven foreign exchange rates against the US dollar. The null hypothesis of no-cointegration fails to be rejected for all dual specifications. Granger-causality tests on the log returns reveals no effect of the exchange rates on stock prices, but there is Granger causality of US stock prices upon three foreign exchange rates. The model developed requires the inclusion in the regressions of the change in the cost of equity. The latter is substituted for by the baa or the aaa corporate bond yield. All regressions are estimated with a GARCH(1,1) model of the conditional variance. The regressions of stock log returns on the log returns of each foreign exchange rate uncover significant impacts for five different rates. However when the ‘fundamental variable’ is added to the regressions, which is the change in the cost of equity, replaced by the change in the baa corporate bond yield, the above impacts reduce to only one with an additional impact that is marginally significant. If the change in the aaa corporate bond yield replaces the change in the cost of equity two significant impacts out of the eleven are found, with one additional marginal result. The evidence is therefore rather strong that the US stock market and the US dollar are effectively independent of each other once fundamentals are accounted for.

The Impact of Liquidity on Senior Credit Spreads During the Subprime Crisis

This paper examines the effects of liquidity during the 2007-09 crisis, focussing on the senior tranche of the CDX.NA.IG Index and on Moody's AAA Corporate Bond Index. The aim is to understand whether these senior credit indices were discounted below fair value and to what extent this discount reflects a lack of depth in the relevant markets, scarcity of risk-capital, and liquidity preference exhibited by investors. Using cointegration analysis, the paper shows that during the crisis lower market and funding liquidity and higher investors' risk-aversion are important drivers of the increase in the spread of triple-A structured securities, while they are less significant to explain the spread of a portfolio of unstructured credits. Looking at the experience of the subprime crisis, the study explores the pitfalls of securitization when the conditions under which it can work properly (transparency and tradability) suddenly disappear; leaving investors highly exposed to systemic risk factors.

Estimating Return Parameters with Short Historical Data: The Case of U.S. Treasury Inflation-Protected Securities

Academic research on consumption and portfolio choices over an individual’s life cycle has been flourishing for almost four decades. Since the seminal work of Merton (1969) and Samuelson (1969), economists’ models have supplied many asset allocations that would achieve optimal intertemporal consumption. Additional considerations on one’s labor decisions or unforeseen rare events have substantiated these models’ predictions and encouraged their implementation by industry practitioners. However, applications of even fairly complex models have quickly exposed the limitations and shortcomings of simplistic assumptions. For example, most models restrict the investment opportunity set to stocks and a riskless asset. Bonds, derivatives and housing are seldom included while other categories are dismissed altogether. Although some model specifications could easily allow for more financial alternatives, it is sometimes data availability (or lack thereof) that determines what variables are included. In this respect, Treasury Inflation-Protected Securities (TIPS) are challenging. The U.S. Government’s first issue was in 1997, long after TIPS were introduced in the U.K., Canada, Israel, Turkey and New Zealand. Naturally, data that span over only 15 years are of little use for the study of an individual’s optimal lifetime behavior: oftentimes, the planning horizon is set at age 90 or 100, with retirement starting at 65. Ultimately, the too recent history of TIPS returns has deterred fund managers from offering them as long-term/retirement assets and, in turn, has curbed their market reach. This project develops a rigorous methodology to estimate the real term structure of TIPS between 1970 and 1997. The resulting series is then merged with the actual data to produce a continuous series of real interest rates from 1970 until now. A summary description of our techniques and preliminary results is attached. The goodness of the newly obtained rates is tested by computing historical correlations with: Moody’s Seasoned AAA Corporate Bond Yield, the 30-Year Conventional Mortgage Rate, the OFHEO’s House Pricing Index, the Dow Jones Industrial Average Index and the S&P500 Index. We perform the exercise for all maturities (5, 10, 20 and 30 years). The outcome of this analysis is twofold. First, it provides practitioners with a reliable tool to better measure the risk-return tradeoff of portfolio strategies with TIPS. Second, it helps establishing guidelines that policy makers can use to choose qualified default investment alternatives (QDIA). Both dimensions offer fertile ground to financial experts who have long cheered TIPS as valuable retirement vehicles.

Global financial crisis and US interest rate swap spreads

This article investigates the determinants of US interest rate swap spreads in the period including the financial crisis. The asymmetric impacts of the financial crisis on interest rate swap spreads are focused by dividing the whole sample period into two. Four determinants of swap spreads – default risk, the slope of yield curve, T-bill and EuroDollar (TED) spread and volatility – are chosen. The default risk measured both in Aaa and Baa corporate bonds are negatively incorporated in the period of financial crisis. The slope is positively incorporated in short- and long-term maturities in the period of financial crisis. The liquidity premium is positively incorporated in short- and long-term maturities in normal period and only in short-term maturity in the period of financial crisis. The market participants were uncertain as for the future of monetary policy by Federal Reserve Board (FRB). Thus the speculation on the path of monetary policy is considered to cause more volatility in the market. The volat...

Investigating Long Memory in Yield Spreads

This article investigates the presence of long memory in bond yield spreads, looking at the spread between Moody’s Baa and Aaa corporate bond yields as well as the spread between each of these series and the 10-year Treasury bond yield. Using the aggregated series and wavelet OLS estimator methods, the authors test the hypothesis that yield spreads follow a fractionally integrated process. We find significant evidence of the presence of long memory in these spreads, which suggests that a time element exists and should be accounted for when analyzing yield spreads, making investment allocation decisions, or using yield spreads as forecasting tools.

An Empirical Investigation of New Bond Issue Yield Spreads, Default Risk and Split Ratings

This paper attempts to explain the yield spreads charged to new corporate debt issues by comparing the initial yields of a set of 3,287 securities issued over eleven years in the US. We use the measure of constant maturity Treasury rates on the day of issue against the Moody’s Aaa Corporate Bond index for the week prior to the issue, and the yield on a daily index of long-term Treasury securities on the issue date. The influences of credit ratings and disagreement between rating agencies as reflected in split ratings and the interactions between these characteristics are measured. The contributions of sinking fund provisions, call or refunding status, overseas issue and contractual security arrangements are evaluated separately. The results support the view that the higher yields are observed when ratings of agencies differ and that factors associated with the issues also are significant drivers of the yield difference.

Forecasting in efficient bond markets: Do experts know better?

Term structure theory suggests that bond rates in efficient markets approximately follow a random walk. We show that the random walk forecasts of 10-year U.S. Treasury and Moody's Aaa corporate bond rates for 1988–2005 are generally unbiased. Blue Chip forecasts, however, are both biased and inferior to random walk forecasts. Both models produce unbiased forecasts of the default spread, with the random walk again outperforming the Blue Chip. In addition, Blue Chip fails to accurately predict directional change. Emphasizing that the success of the random walk model is theoretically expected, we discuss why experts fail to beat random walk predictions.

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.

Time Series Properties of ARCH Processes with Persistent Covariates

We consider ARCH processes with persistent covariates and provide asymptotic theories that explain how such covariates affect various characteristics of volatility. Specifically, we propose and study a volatility model, named ARCH-NNH model, that is an ARCH(1) process with a nonlinear function of a persistent, integrated or nearly integrated, explanatory variable. Statistical properties of time series given by this model are investigated for various volatility functions. It is shown that our model generates time series that have two prominent characteristics: high degree of volatility persistence and leptokurtosis. Due to persistent covariates, the time series generated by our model has the long memory property in volatility that is commonly observed in high frequency speculative returns. On the other hand, the sample kurtosis of the time series generated by our model either diverges or has a well-defined limiting distribution with support truncated on the left by the kurtosis of the innovation, which successfully explains the empirical finding of leptokurtosis in financial time series. We present two empirical applications of our model. It is shown that the default premium (the yield spread between Baa and Aaa corporate bonds) predicts stock return volatility, and the interest rate differential between two countries accounts for exchange rate return volatility. The forecast evaluation shows that our model generally performs better than GARCH(1,1) and FIGARCH at relatively lower frequencies.

Credit spread determinants: An 85 year perspective

This paper estimates a set of credit spread forecasting models using an 85 year history for AAA and BAA corporate bond yield data for the US. Credit spreads are defined as the corporate bond yield less the 20 year yield on US government bonds and are explained by a set of intuitively appealing financial and economic variables. Initial results relate to the application of cointegration techniques to provide long and short run estimates of the key determinants of credit spreads. The analysis is then extended to allow for an unobservable latent variable Markov Switching specification across two separate states. Finally a deterministic regime model based upon an inflation threshold is estimated demonstrating that key causal relationships exist independently across different inflationary environments.

Credit Spread Modeling with Regime-Switching Techniques

Tests on Moody9s AAA and BAA corporate bond yield data consider the determinants of the excess yield earned on corporate debt over U.S. Treasuries. Cointegration estimation techniques reveal significant long- and short-run relationships. Models that allow for distinct regimes over time generate a better fit to the data, and there are some interesting differences in the key determinants of credit spreads across different regimes. Regimes are assumed to be determined by a first-order Markov process or a self-extracting threshold autoregressive process.