[This article develops a rich class of discrete-time, nonlinear dynamic term structure models (DTSMs). Under the risk-neutral measure, the distribution of the state vector X t resides within a family of discrete-time affine processes that nests the exact discrete-time counterparts of the entire class of continuous-time models in Duffie and Kan (1996) and Dai and Singleton (2000). Under the historical distribution, our approach accommodates nonlinear (nonaffine) processes while leading to closed-form expressions for the conditional likelihood functions for zero-coupon bond yields. As motivation for our framework, we show that it encompasses many of the equilibrium models with habit-based preferences or recursive preferences and long-run risks. We illustrate our methods by constructing maximum likelihood estimates of a nonlinear discrete-time DTSM with habit-based preferences in which bond prices are known in closed form. We conclude that habit-based models, as typically parameterized in the literature, do not match key features of the conditional distribution of bond yields.]

Equilibrium, affine asset pricing models with Larry G. Epstein and Stanley E. Zin (1989)’s preferences typically generate time variation in risk premiums through time variation in the quantity of risks, with the market prices of risks (MPR) held constant. This is true of models with built in long-run consumption risks (LRR) (e.g., Ravi Bansal and Amir Yaron (2004), Bansal, Dana Kiku, and Yaron (2009)), as well as of the broader formulations in Bjorn Eraker and Ivan Shaliastovich (2008). For pricing bonds such formulations may be overly constrained as reduced form models suggest that it is time variation in the MPRs, more than stochastic yield volatilities, that resolve the expectations puzzles in bond markets. Constant MPRs are not an inherent feature of equilibrium pricing models with recursive preferences, but rather they arise as a consequence of the linearizations underlying the affine approximations to these models that have been explored empirically. The essential ingredients of these econometric formulations are (P1) recursive (Epstein-Zin) preferences, (P2) risk neutral (핈), affine pricing, and (P3) the assumption that the state of the economy is described by an affine process under the historical (핇) distribution. Key to achieving property (P2), given P1 and P3, is the assumption that the valuation ratio (the log “price/consumption” ratio) associated with the claim that pays aggregate consumption is an affine function of the state. We develop a dynamic term structure model with recursive preferences that preserves

Understanding the global financial network for sovereign debt, particularly with a focus on interaction and spillover effects of sovereign risk, has become important for policy makers as they look to protect the stability of their economies. Using high dimensional Vector Autoregression techniques and network simulation on Sovereign Credit Default Swaps (CDS)’ data of 57 countries, we identify that the global sovereign CDS network is fully integrated as there is virtually no country without any connection to at least one specific node in the system. However, each country has a unique attribute in the network, as a risk exporter or importer and/or risk transmitter. Among developed countries, the US (unsurprisingly) holds the dominant position as a risk exporter while Germany is identified as a connecting country that transmits shocks. The most connected countries in the sovereign CDS network belong to the new European Union members. We examine possible drivers of the network relationships observed, in order to better understand the risk transmission process, and find that connections in the sovereign risk network are stronger within regional groups and countries with the same level of economic development. Central and Eastern Europe and Middle East and Africa have more interactive networks than Northern Western Europe, Asia Pacific and Latin America. We also identify that financial volatility and economic policy uncertainty increase the interactions in market-based default risk assessment.

This study examines how U.S.-listed firms' asset redeployability affects their labor investment efficiency and documents a negative relationship between asset redeployability and labor investment efficiency. Asset redeployability increases overinvestment in labor in the forms of over-hiring and under-firing and provides managers more opportunities to conduct earnings management, which reduces financial reporting quality, readability, and comparability, thereby harming labor investment efficiency. Furthermore, our additional results indicate that the negative impact of asset redeployability on labor investment efficiency is mitigated for firms that have a higher degree of unionization, employ more skilled labor, or implement better corporate governance practices.

Yield curve fluctuations across different currencies are highly correlated. This paper investigates this phenomenon by exploring the channels through which macroeconomic shocks are transmitted across borders. Macroeconomic shocks affect current and expected future short-term rates as central banks react to changing economic environments. Investors could also respond to these shocks by altering their required compensation for risk. Macroeconomic shocks thus influence bond yields both through a policy channel and through a risk compensation channel. Using data from the US, the UK, and Germany, we find that world inflation and US yield level together explain over two-thirds of the covariance of yields at all maturities. Further, these effects operate largely through the risk compensation channel for long-term bonds.

This paper explores the implications of filtering and no-arbitrage for the maximum likelihood estimates of the entire conditional distribution of the risk factors and bond yields in Gaussian macro-finance term structure model (MTSM) when all yields are priced imperfectly. For typical yield curves and macro-variables studied in this literature, the estimated joint distribution within a canonical MTSM is nearly identical to the estimate from an economic-model-free factor vector-autoregression (factor-VAR), even when measurement errors are large. It follows that a canonical MTSM offers no new insights into economic questions regarding the historical distribution of the macro risk factors and yields, over and above what is learned from a factor-VAR. These results are rotation-invariant and, therefore, apply to many of the specifications in the literature.

This article develops a rich class of discrete-time, nonlinear dynamic term structure models (DTSMs). Under the risk-neutral measure, the distribution of the state vector Xt resides within a family of discrete-time affine processes that nests the exact discrete-time counterparts of the entire class of continuous-time models in Duffie and Kan (1996) and Dai and Singleton (2000). Under the historical distribution, our approach accommodates nonlinear (nonaffine) processes while leading to closed-form expressions for the conditional likelihood functions for zero-coupon bond yields. As motivation for our framework, we show that it encompasses many of the equilibrium models with habit-based preferences or recursive preferences and long-run risks. We illustrate our methods by constructing maximum likelihood estimates of a nonlinear discrete-time DTSM with habit-based preferences in which bond prices are known in closed form. We conclude that habit-based models, as typically parameterized in the literature, do not match key features of the conditional distribution of bond yields.

This research investigates the impact of cyberattacks on tax aggressiveness using a difference-in-differences analysis with a matched sample. We find that firms experiencing cyberattacks are more likely to have lower cash effective tax rates and greater discretionary book-tax differences. We further show that cyberattacks have a greater impact on corporate tax aggressiveness when firms are more exposed to financial distress. Additional analyses show tax aggressiveness increases less when firms are in states with enactments of notification laws and firms with ex ante higher cybersecurity investment. Our aggregate results suggest that firms take more tax risky positions in response to greater financial distress and information asymmetry, which are attributed to the consequences of cyberattacks.

Between 1996 and 2014, it was costless on average to hedge news about future variance at horizons ranging from 1 quarter to 14 years. Only unexpected, transitory realized variance was significantly priced. These results present a challenge to many structural models of the variance risk premium, such as the intertemporal CAPM and recent models with Epstein–Zin preferences and long-run risks. The results are also difficult to reconcile with macro models in which volatility affects investment decisions. At the same time, the data allows us to distinguish between different disaster models; a model in which the stock market has a time-varying exposure to disasters and investors have power utility fits the major features of the variance term structure.

This research empirically examines how customer concentration affects stock market liquidity of supplier firms. We find that firms with a concentrated customer base are strongly and positively associated with stock market liquidity, which is robust to a battery of model specifications and endogeneity issues. The positive relationship between customer concentration and liquidity is concentrated among firms with relatively small size, high financial constraints, and high information asymmetry. Further analyses provide supportive evidence on the monitoring role of principal customers in improving firms’ stock liquidity. Overall, these findings suggest that relationships with principal customers serve as valuable signals for the underlying quality of firms, and thus firms in such relationships are able to achieve favorable economic outcomes.