Measuring Inflation Anchoring and Uncertainty: A U.S. and Euro Area Comparison

We use several U.S. and euro‐area surveys of professional forecasters to estimate a dynamic factor model of inflation featuring time‐varying uncertainty. We obtain survey‐consistent distributions of future inflation at any horizon, both in the U.S. and the euro area. Equipped with this model, we propose a novel measure of the anchoring of inflation expectations that accounts for inflation uncertainty. Our results suggest that following the Great Recession, inflation anchoring improved in the United States, while mild de‐anchoring occurred in the euro area. As of our sample end, both areas appear to be almost equally anchored.     

Contracting and Reporting Conservatism around a Change in Fiduciary Duties*

We exploit an influential 1991 Delaware court ruling to examine simultaneously two types of conservatism that play important roles in resolving creditor–owner agency conflicts: contracting conservatism and reporting conservatism. The ruling expanded managerial fiduciary duties in favor of creditors for Delaware-incorporated firms in the vicinity of insolvency. In those firms, following the ruling, debt contracts are less likely to include conservative adjustments to accounting numbers used for covenant compliance (i.e., contracting conservatism decreases), while public financial reporting becomes more conservative (i.e., reporting conservatism increases). The decrease in contracting conservatism is concentrated in firms that exhibit a greater increase in reporting conservatism, suggesting that reporting conservatism is more cost-effective in resolving agency conflicts. In addition, the substitution effect is more pronounced in firms facing greater business uncertainty and firms with greater board independence.     

MODELING SOVEREIGN RISKS: FROM A HYBRID MODEL TO THE GENERALIZED DENSITY APPROACH

Motivated by the European sovereign debt crisis, we propose a hybrid sovereign default model that combines an accessible part taking into account the evolution of the sovereign solvency and the impact of critical political events, and a totally inaccessible part for the idiosyncratic credit risk. We obtain closed‐form formulas for the probability that the default occurs at critical political dates in a Markovian setting. Moreover, we introduce a generalized density framework for the hybrid default time and deduce the compensator process of default. Finally, we apply the hybrid model and the generalized density to the valuation of sovereign bonds and explain the significant jumps in long‐term government bond yields during the sovereign crisis.     

Deep learning for modeling the collection rate for third-party buyers

This study evaluates a wide range of machine learning techniques such as deep learning, boosting, and support vector regression to predict the collection rate of more than 65,000 defaulted consumer credits from the telecommunications sector that were bought by a German third-party company. Weighted performance measures were defined based on the value of exposure at default for comparing collection rate models. The approach proposed in this paper is useful for a third-party company in managing the risk of a portfolio of defaulted credit that it purchases. The main finding is that one of the machine learning models we investigate, the deep learning model, performs significantly better out-of-sample than all other methods that can be used by an acquirer of defaulted credits based on weighted-performance measures. By using unweighted performance measures, deep learning and boosting perform similarly. Moreover, we find that using a training set with a larger proportion of the dataset does not improve prediction accuracy significantly when deep learning is used. The general conclusion is that deep learning is a potentially performance-enhancing tool for credit risk management.     

Volatility forecasting: long memory,regime switching and heteroscedasticity

In this article, we account for the first time for long memory, regime switching and the conditional time-varying volatility of volatility (heteroscedasticity) to model and forecast market volatility using the heterogeneous autoregressive model of realized volatility (HAR-RV) and its extensions. We present several interesting and notable findings. First, existing models exhibit significant nonlinearity and clustering, which provide empirical evidence on the benefit of introducing regime switching and heteroscedasticity. Second, out-of-sample results indicate that combining regime switching and heteroscedasticity can substantially improve predictive power from a statistical viewpoint. More specifically, our proposed models generally exhibit higher forecasting accuracy. Third, these results are widely consistent across a variety of robustness tests such as different forecasting windows, forecasting models, realized measures, and stock markets. Consequently, this study sheds new light on forecasting future volatility.     

VIX term structure and VIX futures pricing with realized volatility

Using an extended LHARG model proposed by Majewski et al. (2015, J Econ, 187, 521–531), we derive the closed-form pricing formulas for both the Chicago Board Options Exchange VIX term structure and VIX futures with different maturities. Our empirical results suggest that the quarterly and yearly components of lagged realized volatility should be added into the model to capture the long-term volatility dynamics. By using the realized volatility based on high-frequency data, the proposed model provides superior pricing performance compared with the classic Heston–Nandi GARCH model under a variance-dependent pricing kernel, both in-sample and out-of-sample. The improvement is more pronounced during high volatility periods.     

Estimating yield curves of the U.S. Treasury securities: An interpolation approach

Following the approach of interpolation, this paper proposes the multiple exponential decay model to fit yield curves for both the U.S. TIPS market and the conventional Treasury security market. Several estimation methods, including the unconstrained/constrained nonlinear minimization, quadratic programming, and the iterative linear least squares, are applied to estimate the unknown parameters according to different curve‐fitting purposes. Comparisons between the proposed model and the alternatives show that the multiple exponential decay successfully (1) adapts to a variety of shapes associated with yield curves, (2) (partially) keeps in line with the economic interpretations of Nelson–Siegel summarized by Diebold and Li ( 2006 ), and (3) dominates the competing models in curve‐fitting performance measured by mean fitted‐price errors over the sample period. In addition, the exact specification of a nonparametric interpolation model is pinned down by applying three statistical tools, which enable us to jointly take into account validity, optimality, and parsimoniousness of the proposed model.     

Some Potential Linkages for Input-Output Analysis with Flow-of-Funds

Three major, interrelated accounting statements, at the frontiers of quantitative economic analysis, are three interrelated systems, namely: (1) National income and product accounts (NIPA), (2) The input-output tableaux, (IO), and (3) flow-of- funds accounts (FF). The third-mentioned system is somewhat less available and used in only limited areas of macroeconomic analysis. This paper is mainly concerned with use of FF accounting systems. This system shows where financial resources originate, and where they go in support of real capital formation. In this respect, interest rates and other market-based financial rates are of key importance. While much macroeconomic analysis is based on the rates that fit the yield curve, showing the interest rate structure over various maturities of debt associated with a given degree of risk. In contrast, the FF accounts throw light on the whole spectrum of interest rates, across maturities and debt qualities. For example, in analysis of the real estate market and funding of capital formation there, it is important to have a full understanding of the course of mortgage rates of different maturities and qualities. In short, it is necessary to develop a full appreciation of supply and demand forces in the mortgage market, which often is not obviously related to movements of the operative rate for monetary policy, such as very short-term inter bank rates or call money rates. This paper attempts to provide material from the flow-of-funds accounts that would make it possible to analyze the movement of relevant mortgage rates or whatever other rates are needed to understand the financing of capital formation in real estate.     

Beyond Single-Factor Affine Term Structure Models

This article proposes a new approach to testing for the hypothesisof a single priced risk factor driving the term structure ofinterest rates. The method does not rely on any parametric specificationof the state variable dynamics or the market price of risk.It simply exploits the constraint imposed by the no-arbitragecondition on instantaneous expected bond returns. In order toachieve our goal, we develop a Kolmogorov-Smirnov test and applyit to data on Treasury bills and bonds for both the United Statesand Spain. We find that the single risk factor hypothesis cannotbe rejected for either dataset.     

我国国债的风险评估与防范

从国债的可持续性、国债的货币扩张效应、国债对经济增长的影响三个层面评估了我国当前的国债风险，并针对我国的国债特点，提出了防范国债风险的措施。