Financial history shows that asset price bubbles, crises and panics are intermittent but perennial characteristics of world financial markets. Experience from the Global Financial Crisis (GFC) over 2007-2009 and the present sovereign debt crisis underscores these historical lessons and bears witness to the cost of financial instability in terms of real economic activity and employment. New regulatory initiatives on capital requirements and the Basel III international financial accord have emphasized the need for improved surveillance of banks and financial markets to help avoid the "excessive credit creation" that typically accompanies asset price bubbles. An important practical issue in such market surveillance involves the assessment of what is "excessive" in the available evidence. Central bank economists and regulators cannot work to offset a speculative bubble unless they are able to assess whether one exists.
A prominent recent example of this conundrum occurred during the early phase of the 1990s Internet Bubble which created and destroyed $8 trillion in shareholder wealth in less than a decade. In 1996 Alan Greenspan famously spoke of "irrational exuberance", expressing concern about possible asset price inflation but with no supporting econometric evidence and no effect on market escalation or the subsequent crash. Similarly in the early 2000s, repeated warnings of excessive rises in house prices by the economist Robert Shiller failed to alert policy makers of the emerging speculative bubble in housing.
Econometric work can assist this complex exercise of monitoring financial markets by quantifying market excesses in relation to fundamentals. Research by the Principal Investigator (PI) has provided an early warning alert system of financial exuberance and market stress. Some of the PI's methods have been adopted by central bank research and surveillance teams to enhance the monitoring of financial asset, real estate, and commodity price markets. This work has involved econometric analysis that focuses on the revealed properties of individual financial time series and the presence of exuberance in the data. The current NSF project extends that work on detection techniques to provide anticipative dating algorithms that can help regulators in market-monitoring activity where there is risk of financial contagion and crisis concatenation over time and across different markets. Evidence-based warning diagnostics are useful as alert mechanisms for market participants as well as for regulators. But to be effective in financial surveillance and regulatory work, an econometric warning alert system needs to be reliable in revealing inflationary upturns in the market -- with a low false detection rate to avoid unnecessary policy measures and a high positive detection rate to assure appropriate policy implementation. The nonlinear structure of bubble phenomena typically diminishes the discriminatory power of test mechanisms. These power reductions complicate attempts at econometric dating and enhance the need for new approaches that do not suffer from this problem. One of the challenges of the current project is to develop and test such methods so that they may be used in an active policy environment. A second challenge lies in the application of these methods to the ballooning sovereign debt and credit default swap market, especially in the European Union periphery. Empirical applications are planned to analyze the European debt crisis using credit default swap spreads and to track migration of the phenomena through the financial system and real economies.
In addition to the main branch of research on crisis econometrics the project will develop automated methods for systems of high dimensional time series allowing for co-movement and linkages among the variables as well as nonstationarity in the data. Massive recent improvements in the availability of electronic data offer new opportunities for statistical analysis including the use of very high dimensional datasets in practical work. Modern econometric practice now frequently encounters systems where the dimensionality of the variables may exceed the sample size. In such cases sparse statistical methods can be useful in resolving degrees of freedom problems, but that methodology is presently developed only for linear systems with stationary regressors. Econometric work in finance and macroeconomics typically involves nonstationary and potentially co-moving data series. These features introduce challenging nonlinearities and identification issues that must be addressed in the use of sparse estimation techniques. The second branch of this project pursues those extensions. Some related research in the project will consider large dimensional dynamic panels where an unknown subset of series have a common feature or parameter such as an autoregressive unit root. The new methodology will provide a data-determined approach to subset classification in regression, where there is some commonality in the regression characteristics across certain individuals in the panel. This type of econometric classification covers many empirical examples of interest such as convergence clubs that arise in global and regional economic growth analysis. Research on these panel classification devices will substantially extend the range of existing shrinkage methods and their potential empirical applications in economics.
