What Microeconomic Fundamentals Drove Global Oil Prices during 1986–2020?

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

The Global Price of Oil, QE and the US High Yield Rate

Purpose Quantitative easing (QE) allowed the US economy to stabilize and return to slow growth. Oil prices increased to $100 during 2010–2013. Then in June 2014, they plunged again dramatically to$40. The purpose of this paper is to develop and test a model that describes the price of oil as depending on six inputs: Federal assets accumulated by the Federal Reserve during the period of QE, the 10-Year Treasury note rate, the price of copper, the trade-weighted dollar, the S&P 500 Index and the US high yield rate for bonds rated CCC or below. Design/methodology/approach We use 771 overlapping 52-week regressions to capture short-run oil price dynamics. Findings We find that QE was statistically significant only during 2009–2010, while the US high yield rate played a more significant role, both during and after the crisis. Research limitations/implications This paper does not explain the behavior of oil prices prior to 2003. Practical implications This paper emphasizes the role of the high yield rate on fracking technology in financing the extraction and production of oil. Originality/value The paper has both the theoretical value for researchers in the area of energy, as well as practical application for the oil industry

What Has Driven the U.S. Monthly Oil Production Since 2009? Empirical Results from Two Modeling Approaches

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

Five Themes of U.S. Home Price Cycles: A Unified Approach

The U.S. housing market, after the Global Financial Crisis (GFC), has been extensively studied from several dimensions to assess the causes for the price crash. In this paper, we study and compare five themes related to house price behavior and identify common determinants that drive prices. The themes studied include the macroeconomic business cycle environment, monetary policy, the global saving glut, the fundamentals of the housing market, and lastly housing expectations which may be associated with bubbles. We employ a neural network methodology to capture and explore the relative importance of non-linear relationships not found in classical regression modeling using monthly data between key market features and house prices. Additionally, given bubble identification may be model dependent, we use the structure of model forecast errors (residuals) to identify the potential presence of bubbles. The potential presence of a bubble can be measured against the features within a model theme. CUSUM tests show potential structural breaks (bubbles) in two of our themes around the time of the GFC

Asset Price Bubbles and Central Bank Policies: The Crash of the Jackson Hole Consensus

This chapter examines whether or not monetary policy should respond to asset price bubbles. More specifically, it asks how central banks respond while an asset bubble is growing and how they respond after the bubble bursts. It begins with a general overview of asset bubbles that supports the existence of the real and financial sectors of an economy before discussing how the bursting of asset price bubbles may cause financial instability that often adversely affects the real sector of an economy. It then describes the normative vs. positive responses of a central bank to asset price bubbles, along with the concept of macroprudential regulation as an approach for leaning against asset bubbles. It argues that the high costs associated with the 2007–2009 financial crisis undermined the so-called Jackson Hole Consensus and that the new central bank policy paradigm appears to have shifted toward “leaning against bubbles”

The Evolving Nature of Asset Price Bubbles, Financial Instability and Monetary Policy

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

House Bubbles, Global Imbalances and Monetary Policy in the US

This paper examines the factors driving housing price exuberance in the United States, specifically the influence of expansionary monetary policies and the global saving glut. We employ medium scale Bayesian VAR and time-varying VAR models to estimate the effects of monetary policy and global saving glut shocks on US housing bubbles. We find that, prior to the Global Financial Crisis, the impact of the saving glut shock is more enduring, powerful, and rapid in generating housing bubbles compared to monetary policy shocks. However, the recent housing boom that commenced in 2019 demonstrates a different pattern. Our results suggest that both monetary policy and the global saving glut contribute to the increase in house prices. Counterfactual policy experiments validate this conclusion

Speculative Non-Fundamental Components in Mature Stock Markets: Do They Exist and Are They Related?

Economists have long conjectured that movements in stock prices may involve speculative components, called bubbles. A bubble is defined as the difference between the market value of a security and its fundamental value. The topic of asset bubbles remains controversial because the existence of a bubble is inherently an empirical issue and no satisfactory test has yet been devised to estimate the magnitude of a bubble. This paper proposes a new methodology for testing for the existence of rational bubbles. Unlike previous authors, we treat both the dividend that drives the fundamental part and the nonfundamental process as part of the state vector. This new methodology is applied to the four mature markets of the US, Japan, England, and Germany to test whether a speculative component was present during the period of January 1951 to December 1998 in these markets. The paper also examines whether there are linkages between these national speculative components. We find evidence that the nonfundamental component in the US market causes the other three markets but we find no evidence for reverse causality

To Lean or Not to Lean Against an Asset Price Bubble? Empirical Evidence

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. (JEL E50, E52, E58

Are There Rational Bubbles in the US Stock Market? Overview and a New Test

A speculative bubble is usually defined as the difference between the market value of a security and its fundamental value. Although there are several important theoretical issues surrounding the topic of asset bubbles, the existence of bubbles is inherently an empirical issue that has not been settled yet. This paper reviews several important tests and offers one more methodology that improves upon the existing ones. The new test is applied to the annual US stock market data spanning over a century and at the monthly frequency covering the post-war period. Although we find evidence of stock price bubble in both cases, the post-war period exhibit only positive component whereas the annual data exhibit some episode of negative bubble