Anastasios G. Malliaris

We propose two narratives to analyze monthly returns for the S&P 500 Index. The first narrative emphasizes variables that represent the macroeconomy: Fed Funds Effective Rate, Real M2, 10-Year T-Note minus 2-Year T-Note, Shiller Housing Index, industrial production, and 1-Year Expected Inflation. The second narrative focuses on microeconomic fundamentals that include earnings, CBOE Volatility, consumer sentiment, interest rates, global price of copper, and the Dollar Index. We perform a methodology of 348 rolling regressions for each narrative, each with a sample of 60 monthly observations, and estimate the significance of the independent variables considered. We conclude that the microeconomic narrative with its indicators tied to stock market activities correlates with monthly returns more closely than macro fundamentals do. The new insight from this paper is that it is beneficial to employ both narratives as complementary rather than as competitive.

Competing macroeconomic hypotheses have been developed to explain the US housing market and possible bubble behavior. We employ both seasonally adjusted (SA) and non-seasonally adjusted (NSA) monthly data for about 30 independent variables to examine alternative macro hypotheses for home prices. Using a neural network model as an atheoretical non-linear approach to capture the relative importance of alternative macro variables, we show that these hypotheses generate different macro relevance. As an alternative to testing housing time series, we focus on bubble identification being hypothesis dependent. Model forecast errors (residuals) identify the potential presence of bubbles through standardized residual CUSUM tests for structural breaks. By testing for housing bubbles from these unstructured models, we generate conclusions on the presence of bubbles prior to the Great Financial Crisis and the post-pandemic periods. Competing macro hypotheses or narratives will generate different conclusions on the presence of bubbles and create bubble identification issues.

This paper contributes to a fuller understanding of macroeconomic outcomes to financial market disturbances and the central bank’s role in financial stability. Our two major contributions are conceptual and econometric. Conceptually, we introduce phases of the business cycle and econometrically we employ Bayesian VARs. We document that a shock that increases credit to non-financial sector leads to a persistent decline in economic activity. In addition, we examine whether the behavior of financial variables is useful in signaling the 2007–2009 recession. The answer is positive as our BVAR generates early warning signals pointing to a sustained slowdown in growth. We propose that the expansion phase of the business cycle can be subdivided into an early and a late expansion. Based on this distinction, we show that if the Fed had raised the policy rate when the economy moved from the early to late expansion, it could have mitigated the severity of the 2007–2009 recession.

The global financial crisis of 2007–2009 caused major economic disturbances in the oil market. In this paper, we consider five variables that describe the microeconomics of the supply of and demand for oil, and evaluate their importance before, during and after the global financial crisis. We consider five dissimilar regimes during the period of January 1986 to the end of 2020: two regimes prior to the global financial crisis, the regime during the crisis, and two regimes after the crisis. The main hypothesis tested is that oil fundamentals of supply and demand remained important, even though the five regimes were dissimilar. We built five boosted and over-fitted neural networks to capture the exact relationships between spot oil prices and oil data related to these prices. This analysis shows that, while the inputs into an accurate neural network can remain the same, the impact of each variable can change considerably during different regimes.

From the early 1970s to the Global Financial Crisis of 2007–09, U.S. crude oil production followed a declining trend. After the Global Financial Crisis, U.S. crude oil production increased rapidly. This paper addresses the important question “what economic factors have driven U.S. crude oil production since the Global Financial Crisis?”. We propose that factors such as: the price of oil, the one period lagged price of oil, the price of copper, the crude oil price volatility, the Trade Weighted U.S. Dollar Index, and the high yield index spread, are important explanatory variables. Using two modeling approaches, namely, multiple regression, and the random tree methodology, we conclude that the one month lagged price of oil is the most significant explanatory variable, among all considered, for the upward trend of U.S. oil production from 2009 to early 2020.

Since the Global Financial Crisis of 2007–2009, economists are reconsidering the appropriate role of monetary policy towards equity bubbles. This paper contributes to these deliberations by estimating the response of the stock market to monetary policy tightening by using a Bayesian time‐varying VAR model. By introducing the cyclically adjusted price/earnings ratio, we propose a method that estimates its fundamental and bubble components. We find that asset prices will initially fall and eventually rise again but without the risk of feeding the bubble. Counterfactual policy experiments provide additional evidence that monetary policy can lean against equity and housing prices.(JELE50, E52, E58)

This paper links the bursting of the housing asset price bubble around 2007 in the U.S. to the instability that arose in financial markets with the bankruptcy of Lehman Brothers in September 2008, and both of these to the Great Recession and the unconventional monetary policy that followed. Similar narratives about the Stock Market Crash of 1929, the Crash of 1987 and the Internet Bubble of 2000 are briefly presented to show their evolving financial nature, describe the financial instabilities produced by them and their costs and, finally examine the responses initiated, primarily, by monetary policy. This analytical synopsis of the four best-known U.S. asset bubble crashes guides us to an articulation of a few basic lessons learned.