US inflation dynamics on long-range data

In this article, we evaluate inflation persistence in the United States using long-range monthly and annual data. The importance of inflation persistence is crucial to policy authorities and market participants, since the level of inflation persistence provides an indication on the susceptibility of the economy to exogenous shocks. Departing from classic econometric approaches found in the relevant literature, we evaluate inflation persistence through the nonparametric Hurst exponent within both a global and a rolling window framework. Moreover, we expand our analysis to detect the potential existence of chaos in the data generating process, in order to enhance the robustness of our conclusions. Overall, we find that inflation persistence is high from 1775 to 2013 for the annual data-set and from February 1876 to May 2014 in monthly frequency, respectively. Especially from the monthly data-set, the rolling window approach allows us to derive that inflation persistence has reached to historically high levels in the post–Bretton Woods period and remained there ever since.     

Macroeconomic uncertainty,growth and inflation in the Eurozone: a causal approach

In this article, we evaluate the causal relationship between macroeconomic uncertainty indices, inflation and growth rate for 17 Eurozone countries on a county-level examination. In performing a series of linear and nonlinear causality tests, we find little evidence of a causal relationship between uncertainty and macroeconomic variables. Thus, macroeconomic analysis based on uncertainty indices should be treated with caution.     

Predicting European Union Recessions in the Euro Era: The Yield Curve as a Forecasting Tool of Economic Activity

Several studies have established the predictive power of the yield curve for the U.S. and various European countries. In this paper we use data from the European Union (EU15), from 1994:Q1 to 2008:Q3. We use the European Central Bank’s euro area yield spreads to predict European real GDP deviations from the long-run trend. We also augment the models tested with non monetary policy variables: the unemployment and a composite European stock price index. The methodology employed is a probit model of the inverse cumulative distribution function of the standard distribution using several formal forecasting and goodness of fit evaluation tests. The results show that the yield curve augmented with the composite stock index has significant forecasting power in terms of the EU15 real output.     

Interest Rates, Leverage, and Money

The current mainstream approach to monetary policy is based on the New Keynesian model and is expressed in terms of a short-term nominal interest, such as the federal funds rate in the United States. It ignores the role of leverage and also downplays the role of money in basic monetary theory and monetary policy analysis. But as the federal funds rate has reached the zero lower bound and the Federal Reserve is in a liquidity trap, the issue is whether there is a useful role of leverage and monetary aggregates in monetary policy and business cycle analysis. We address these issues and argue that there is a need for financial stability policies to manage the leverage cycle and reduce the procyclicality of the financial system. We also argue that in the aftermath of the global financial crisis and Great Contraction there is a need to get away from the New Keynesian thinking and back toward a quantity theory approach to monetary policy, based on properly measured monetary aggregates, such as the new Center for Financial Stability Divisia monetary aggregates.     

Episodic Nonlinearity in Leading Global Currencies

We perform non-linearity tests using daily data for leading currencies that include the Australian dollar, British pound, Brazilian real, Canadian dollar, euro, Japanese yen, Mexican peso, and the Swiss franc to resolve the issue of whether these currencies are driven by fundamentals or exogenous shocks to the global economy. In particular, we use a new method of testing for linear and nonlinear lead/lag relationships between time series, introduced by Brooks and Hinich (J Empir Finance 20:385–404, 1999), based on the concepts of cross-correlation and cross-bicorrelation. Our evidence points to a relatively rare episodic nonlinearity within and across foreign exchange rates. We also test the validity of specifying ARCH-type error structures for foreign exchange rates. In doing so, we estimate Bollerslev’s (J Econom 31:307–327, 1986) generalized ARCH (GARCH) model and Nelson’s (1988) exponential GARCH (EGARCH) model, using a variety of error densities [including the normal, the Student-t distribution, and the Generalized Error Distribution (GED)] and a comprehensive set of diagnostic checks. We apply the Brooks and Hinich (1999) nonlinearity test to the standardized residuals of the optimal GARCH/EGARCH model for each exchange rate series and show that the nonlinearity in the exchange rates is not due to ARCH-type effects. This result has important implications for the interpretation of the recent voluminous literature which attempts to model financial asset returns using this family of models.     

Does financial market liberalization increase the degree of market efficiency? The case of the Athens stock exchange

In this paper we assess if the financial market liberalization introduced in the beginning of the 1990s in Greece has changed the degree of market development (efficiency) by studying time-varying global Hurst exponents. Our results suggest that changes in financial market liberalization have important positive implications on the degree of development of stock markets. These results have important policy implications for the development of stock markets around the world.     

GDP trend deviations and the yield spread: the case of eight E.U. countries

Several studies have established the predictive power of the yield curve in terms of real economic activity. In this paper we use data for a variety of E.U. countries: both EMU (Germany, France, Italy, Portugal and Spain) and non-EMU members (Norway, Sweden and the U.K.). The data used range from 1991:Q1 to 2009:Q3. For each country, we extract the long run trend and the cyclical component of real economic activity, while the corresponding ECB euro area government benchmark bond interest rates of long and short term maturities are used for the calculation of the yield spreads. We also augment the models tested with non monetary policy variables: the respective unemployment rates and stock indices. The methodology employed in the effort to forecast real output, is a probit model of the inverse cumulative distribution function of the standard distribution, using several formal forecasting and goodness of fit evaluation criteria. The results show that the yield curve augmented with the non-monetary variables has significant forecasting power in terms of real economic activity but the results differ qualitatively between the individual economies examined raising non-trivial policy implications.     

Forecasting bank failures and stress testing: A machine learning approach

This paper presents a forecasting model of bank failures based on machine-learning. The proposed methodology defines a linear decision boundary that separates the solvent banks from those that failed. This setup generates a novel alternative stress-testing tool. Our sample of 1443 U.S. banks includes all 481 banks that failed during the period 2007–2013. The set of explanatory variables is selected using a two-step feature selection procedure. The selected variables were then fed to a support vector machines forecasting model, through a training–testing learning process. The model exhibits a 99.22% overall forecasting accuracy and outperforms the well-established Ohlson’s score.