Learning and the dynamics of consumer unsecured debt and bankruptcies

During the Great Moderation, the consumer unsecured debt-to-income ratio nearly doubled and the personal bankruptcy filing rate more than quadrupled. This historically tranquil period ended in 2008 with a severe recession and a protracted credit crunch. We develop a theory of learning in which consumers and lenders adjust their beliefs about the riskiness of the economic environment over time in response to the realized sequence of aggregate shocks and then embed it into an otherwise standard quantitative model of consumer default. Simulations of the model suggest that learning can explain as much as half of the recent boom and bust cycle in consumer unsecured debt and a modest fraction of the rise in bankruptcy filings prior to 2005.     

Forecasting the term structure of government bond yields

Despite powerful advances in yield curve modeling in the last 20 years, comparatively little attention has been paid to the key practical problem of forecasting the yield curve. In this paper we do so. We use neither the no-arbitrage approach nor the equilibrium approach. Instead, we use variations on the Nelson–Siegel exponential components framework to model the entire yield curve, period-by-period, as a three-dimensional parameter evolving dynamically. We show that the three time-varying parameters may be interpreted as factors corresponding to level, slope and curvature, and that they may be estimated with high efficiency. We propose and estimate autoregressive models for the factors, and we show that our models are consistent with a variety of stylized facts regarding the yield curve. We use our models to produce term-structure forecasts at both short and long horizons, with encouraging results. In particular, our forecasts appear much more accurate at long horizons than various standard benchmark forecasts.     

Bond risk, bond return volatility, and the term structure of interest rates

This paper explores the time variation in the bond risk, as measured by the covariation of bond returns with stock returns and consumption growth, and in the volatility of bond returns. A robust stylized fact in empirical finance is that the spread between the yields on long- and short-term bonds forecasts future excess returns on bonds at varying horizons positively; in addition, the short-term nominal interest rate forecasts both the stock return volatility and the exchange rate volatility positively. This paper presents evidence that movements in both the short-term nominal interest rate and the yield spread are positively related to changes in the subsequent realized bond risk and bond return volatility. The yield spread appears to proxy for business conditions, while the short rate appears to proxy for inflation and economic uncertainty. A decomposition of bond betas into a real cash flow risk component and a discount rate risk component shows that yield spreads have offsetting effects in each component. A widening yield spread is correlated with a reduced cash-flow (or inflationary) risk for bonds, but it is also correlated with a larger discount rate risk for bonds. The short rate only forecasts the discount rate component of the bond beta.     

Evolving macroeconomic perceptions and the term structure of interest rates

We explore the role of evolving beliefs regarding the structure of the macroeconomy in improving our understanding of the term structure of interest rates within the context of a simple macro-finance model. Using quarterly vintages of real-time data and survey forecasts for the United States over the past 40 years, we show that a recursively estimated VAR on real GDP growth, inflation and the nominal short-term interest rate generates predictions that are more consistent with survey forecasts than a benchmark fixed-coefficient counterpart. We then estimate a simple term structure model under the assumption that investor risk attitude is driven by near-term expectations of the three state variables. When we allow for evolving beliefs about the macroeconomy, the resulting term structure model provides a better fit to the cross section of yields than the benchmark model, especially at longer maturities, and exhibits better performance in out-of-sample predictions of future yield movements.     

The term structure of volatility predictability

Volatility forecasting is crucial for portfolio management, risk management, and pricing of derivative securities. Still, little is known about the accuracy of volatility forecasts and the horizon of volatility predictability. This paper aims to fill these gaps in the literature. We begin this paper by introducing the notions of spot and forward predicted volatilities and propose describing the term structure of volatility predictability by spot and forward forecast accuracy curves. Then, we perform a comprehensive study of the term structure of volatility predictability in stock and foreign exchange markets. Our results quantify the volatility forecast accuracy across horizons in two major markets and suggest that the horizon of volatility predictability is significantly longer than that reported in earlier studies. Nevertheless, the aforesaid horizon is observed to be much shorter than the longest maturity of traded derivative contracts.     

On the informational role of term structure in the US monetary policy rule

This paper uses a structural approach based on the indirect inference principle to estimate a standard version of the new Keynesian monetary (NKM) model augmented with term structure using both revised and real-time data. The estimation results show that the term spread and policy inertia are both important determinants of the US estimated monetary policy rule whereas the persistence of shocks plays a small but significant role when revised and real-time data of output and inflation are both considered. More importantly, the relative importance of term spread and persistent shocks in the policy rule and the shock transmission mechanism drastically change when it is taken into account that real-time data are not well behaved.     

Moral hazard,debt overhang and capital structure

This paper investigates the impact of managerial moral hazard on the debt overhang of a firm by constructing a contingent claims model in which the manager faces costly effort. Using a calibrated capital structure model, we show that the costs of debt overhang become more serious in the presence of managerial moral hazard. Such costs even account for more than half of the total agency costs at a high level of cash flow. Moreover, in contrast to the results of Hackbarth and Mauer (2012), our model predicts a U-shaped relationship between the leverage ratio and investment opportunities of a firm, which is caused by managers’ moral hazard. Finally, by considering this moral hazard, we also show the coexistence of low leverage ratios and high credit spreads, which explains the phenomenon of “low debt levels and high credit spreads” observed in practice.     

Anticipation,learning and welfare: the case of distortionary taxation

We study the impact of anticipated fiscal policy changes in a Ramsey economy where agents form long-horizon expectations using adaptive learning. We extend the existing framework by introducing distortionary taxes as well as elastic labor supply, which makes agents’ decisions non-predetermined but more realistic. We detect that the dynamic responses to anticipated tax changes under learning have oscillatory behavior that can be interpreted as self-fulfilling waves of optimism and pessimism emerging from systematic forecast errors. Moreover, we demonstrate that these waves can have important implications for the welfare consequences of fiscal reforms.     

Credit rating changes and debt structure

Through examination of the relationship between rating levels and subsequent annual net debt changes, Kisgen (2006) provides support for the Credit Rationing – Capital Structure (CR-CS) hypothesis which maintains that “+” or “−” notch firms are more likely than non-notch firms to reduce net debt levels to increase the likelihood of a beneficial rating change. We add to the credit rating literature by focusing on quarterly net debt changes over the two years before and after rating changes to provide evidence that notch firms are generally not associated with lower net debt levels, greater net debt reductions, or higher probability of upgrades than non-notch firms before rating changes. Instead, notch firms with CW announcements are associated with relatively greater net debt level increases beginning three quarters before rating changes and these increases continue for firms both without and with CW announcements after the rating change. Further, in analysis of the strength of upgrades (UP) relative to downgrades (DOWN) at the time of rating change, we show that the UP/DOWN ratio is more a function of the presence of prior CreditWatch (CR) announcements than notch status. Firms without and with CW announcements exhibit UP/DOWN ratios of 0.8455 and 0.3628, respectively, with no significant differences in these ratios between notch and non-notch firms.     

Optimal fiscal policy under learning

This paper characterizes optimal fiscal policy when agents learn about future taxation. A benevolent and fully rational government chooses taxes on labor income and state-contingent bonds to finance public spending, considering that private agents form their expectations through a learning algorithm. Facing a trade-off between distortionary taxes and distorted expectations, the Ramsey planner chooses the policy that minimizes the total cost of distortions. The analysis produces two main results. First, the government will use fiscal variables to manipulate expectations, reducing taxes and issuing debt at times of pessimism and doing the opposite at times of optimism. This speeds up learning. Second, the expectation-dependent fiscal plan is also history-dependent, and it prescribes taxes that are not as smooth and more persistent than under rational expectations. These findings are robust to alternative learning algorithms.     

Monetary policy and the term premium

The term premium has become increasingly important in discussions of monetary policy formulation. This paper reviews two approaches to embedding a variable term premium into an otherwise standard modern DSGE model. The first approach maintains frictionless asset trade but alters preferences so that agents are more averse to the risk in long bonds. The second approach uses traditional preferences, but segments asset trade between long and short bonds. Policy issues are also discussed.     

Adaptive learning and monetary exchange

This paper investigates three classic questions in monetary theory: How can an intrinsically worthless asset, such as fiat money, maintain value as a medium of exchange? What are the short-run and long-run effects of a change in the money supply? What is the social cost of inflation? I answer these questions using a microfounded model of monetary exchange that replaces the rational expectations assumption with an adaptive learning rule. First, I show that monetary exchange is a robust arrangement in the sense that agents are able to learn the stationary monetary equilibrium while the non-monetary equilibrium is unstable under learning. Second, an unanticipated monetary injection has real effects in the short-run because learning the value of money takes time. In the long run, agents successfully learn the value of money, hence money is neutral. Third, under a constant money growth policy, an increase in the growth rate of money increases output in the short-run producing a short-run Phillips curve. A ten percent increase in the money growth rate has a social cost of 0.41 percent of output per year. Alternatively, a ten percent decrease in the money growth rate has a social benefit of 0.37 percent of output per year.     

Adaptive learning,endogenous uncertainty,and asymmetric dynamics

I present a simple model where forecasting confidence affects aggregate demand. It is shown that this model has similar stability properties, under statistical and evolutionary learning, as a model without a confidence affect. From this setup, I introduce “Expectational Business Cycles” where output fluctuates due to learning, heterogeneous forecasting models and random changes in the efficient forecasting model. Agents use one of two forecasting models to forecast future variables while heterogeneity is dictated via an evolutionary process. Increased uncertainty, due to a shock to the structure of the economy, may result in a sudden decrease in output. As agents learn the equilibrium, output slowly increases to its equilibrium value. Expectational business cycles tend to arrive faster, last longer and are more severe as agents possess less information.     

Monetary policy and learning from the central bank's forecast

We examine the expectational stability (E-stability) of rational expectations equilibrium (REE) in a standard New Keynesian model in which private agents refer to the central bank's forecast in the process of adaptive learning. To satisfy the E-stability condition in this environment, the central bank must respond more strongly to the expected inflation rate than the extent to which the Taylor principle suggests. However, the central bank's strong reaction to the expected inflation rate raises the possibility of indeterminacy of the REE. In considering these problems, a robust policy requires responding to the current inflation rate to a certain degree.     

Adaptive learning and distributional dynamics in an incomplete markets model

Recent research shows that several DSGE models provide a closer fit to the data under adaptive learning. This paper extends this research by introducing adaptive learning in the model of Krusell and Smith (1998) with uninsurable idiosyncratic risks and aggregate uncertainty. A first contribution of this paper establishes that the equilibrium of this framework is stable under least-squares learning. The second contribution consists of showing that bounded rationality enhances the ability of this model to match the distribution of income in the US. Learning increases significantly the Gini coefficients because of the opposite effects on consumption of the capital-rich and of the capital-poor agent. The third contribution is an empirical exercise that shows that learning can account for increases in the income Gini coefficient of up to 25% in a period of 28 years. Overall, these findings suggest that adaptive learning has important distributional repercussions in this class of models.     

Adaptive learning forecasting,with applications in forecasting agricultural prices

We introduce a new forecasting methodology, referred to as adaptive learning forecasting, that allows for both forecast averaging and forecast error learning. We analyze its theoretical properties and demonstrate that it provides a priori MSE improvements under certain conditions. The learning rate based on past forecast errors is shown to be non-linear. This methodology is of wide applicability and can provide MSE improvements even for the simplest benchmark models. We illustrate the method’s application using data on agricultural prices for several agricultural products, as well as on real GDP growth for several of the corresponding countries. The time series of agricultural prices are short and show an irregular cyclicality that can be linked to economic performance and productivity, and we consider a variety of forecasting models, both univariate and bivariate, that are linked to output and productivity. Our results support both the efficacy of the new method and the forecastability of agricultural prices.     

Discrete time Wishart term structure models

This paper reveals that the class of Affine Term Structure Models (ATSMs) introduced by Duffie and Kan (1996) is larger than previously considered in the literature. In the framework of risk factors following a Wishart autoregressive process, we define the Wishart Term Structure Model (WTSM) as an extension of a subclass of Quadratic Term Structure Models (QTSMs), derive simple parameter restrictions that ensure positive bond yields at all maturities, and observe that the usual constraint on affine processes requiring that the volatility matrix be diagonal up to a path independent linear invertible transformation can be considerably relaxed.     

Asset sale,debt restructuring,and liquidation

This paper considers a dynamic model in which shareholders of a firm in distress have a choice of whether the firm proceeds to debt restructuring or direct liquidation at an arbitrary time. In the model, we show the following results. Fewer asset sales, lower financing, debt renegotiation, and running costs, a lower premium to the debt holders, a lower cash flow volatility, and a higher initial coupon increase the shareholders׳ incentive to choose debt restructuring to avoid full liquidation. In the debt renegotiation process, the shareholders arrange the coupon reduction and use equity financing to retire a part of the debt value to the debt holders. The timing of debt restructuring always coincides with that of liquidation without debt renegotiation. Most notably, the shareholders do not prefer asset sale in debt restructuring even if they face high financing costs. The possibility of debt renegotiation in the future increases the initial leverage ratio in the optimal capital structure.     

The influence of ownership structure and board independence on the cost of debt in BRIC countries

Agency theory predicts that the default premium on debt is determined by the intensity of agency conflicts since they affect the risk of debtholders. This effect is especially important in emerging countries with high ownership concentration and low protection of minority owners. This paper presents an empirical analysis of the influence of ownership structure and board independence on the cost of debt in BRIC countries over the period 2007–2020. The main finding of the study is the presence of significant country-specific effects of ownership structure on the cost of debt measured with the G-spread on corporate bonds, as well as the absence of effects of board independence. According to our results, concentrated ownership and state ownership increase the cost of debt in Brazil and Russia, while decreasing it in China. We reveal that institutional investors help mitigate the risks of debtholders in China, while insider ownership decreases the default risk in Brazil.