“Technical insolvency” is a phenomenon that a firm has surplus in financial statements but cannot fulfill its payment obligations. This phenomenon is especially worth noting during the recent market-wide financial crisis. In practice, liquidity crunch usually takes place before “stock-based” default (i.e. asset inadequacy default) because asset inadequacy relies upon the information generated by a time-lagged financial reporting system or, in many cases, by a complicated asset valuation process. Therefore, information on the probability of a liquidity crunch and the expected liquidity shortfall is important for determining the required internal liquidity reserve that supports a specific credit quality target, and is especially important for the periods of market liquidity crunch. However, existing Merton-type structural-form credit models ignore flow-based insolvency risk and consider only the difference between values of a firm’s assets and its liabilities.
Their most distinctive attribute is that they derive a firm’s asset value distribution from its equity market value and estimate its probability of default (PD) and recovery rate (RR) endogenously. Although researchers have developed many varieties from the original Merton model to overcome several major challenges to these models both in theory and practice, these modified models still barely consider corporate flow-based insolvency risk due to liquidity crunch. On the other hand, the reduced-form credit models, which are intensity-based, disregard any of a firm’s fundamental information, including internal liquidity, and rely on exogenous information such as credit ratings, recovery rates, or other default-related proxies to estimate a firm’s default probability. Consequently, they are limited in being able to provide the necessary information for credit risk management and to price liquidity related credit assets and derivatives. To fill this gap, this study develops an integrated structural-form credit risk model combining both stock-based an flow-based corporate credit risk information.
In developed economies, monetary policy is generally counter-cyclical. For instance, there is a widespread consensus that policy should be eased in a recession. By contrast, in the recent experience of emerging market economies, monetary policy has often been pro-cyclical, raising interest rates during a crisis, usually in order to defend the exchange rate. As an example, after the Asian-Russian crisis of 1997-98, interest rates fell in the US, Australia, Canada, and most other developed economies, while they rose in almost all emerging market economies (Edwards 2001). Related evidence from Calvo and Reinhart (2002) indicates that many emerging economies place a high weight on exchange rate stability, even in face of large macroeconomic shocks which in principle would call for exchange rate adjustment.
Why do we see such a contrast between the policy responses of developed economies and emerging markets? One explanation is market confidence. Many of these economies have a history of bad policy, so that in a crisis it is more important to raise interest rates to restore the confidence of international capital markets than to attempt to stabilize the domestic economy. But many economists (e.g. Krugman(1998), Stiglitz (2002)) have questioned this, arguing that tight monetary policies exacerbate the crisis rather than generating confidence.
Money and real activity are strongly correlated at business cycle frequencies. The strength of this correlation has led the profession to a widespread use of models employing nominal rigidities in an attempt to explain salient macroeconomic phenomena. Two distinct approaches to modeling nominal rigidities have been used in earlier work.
The first approach, exemplified by the work of Fisher (1977) and Taylor (1980), assumes that the timing of price changes is independent of shocks affecting the firm in each period. Institutional restrictions or information-gathering costs prevent firms from meeting too often and instead, firms have predetermined schedules of price adjustment. These are the so-called time-dependent models, which, because of their computational tractability, have received most of the profession’s attention in the last two decades.
