Are yield-curve/monetary cycles’ approaches enough to predict recessions?

Business Economics - This paper proposes a new framework that identifies a threshold between the fed funds rate and the 10-year Treasury yield and, when the threshold is breached, the risk of a...     

Is Productivity Growth Too Strong For Our Own Good?

Productivity growth and improvement in a nation's standard of living are widely thought to go hand in hand. During the past 15 years, however, the gap between productivity growth and growth of living standards has widened, igniting a debate about whether a larger share of the benefits from productivity gains has gone to capital rather than labor. The first phase of our study characterizes U.S. productivity growth for the period 1948–2011. Our statistical analysis found that productivity growth did not follow one particular pattern over time, and we therefore doubt that it would follow one pattern (either a higher or lower growth rate) in the near future. Our analysis concludes that the “productivity resurgence” era of 1996:Q1 to 2011:Q4 is associated with lower growth rates of real per capita income, employment, and consumer confidence relative to productivity. That may validate the “savage cost-cutting” and “polarization” hypotheses. The stable and higher growth rates of corporate profits and the S&P 500 index indicate that capital and higher skilled workers may have gained benefits from productivity growth over time. A simultaneous rise in food stamp recipients and income share of the top 0.01 percent during the post-mid-1990s era suggest that the distribution of the stronger productivity growth gains is asymmetric.     

Estimating the economic cost of the COVID-19 pandemic

Business Economics - Our study presents a framework to estimate economic costs of the COVID-19 pandemic for the U.S. economy. We characterize whether the pandemic-related damages are short-lived or...     

An Ordered Probit Approach to Predicting the Probability of Inflation/Deflation

This paper provides an ordered probit approach that estimates the probability six months in the future of three distinct scenarios for prices: inflation, deflation, or price stability. The traditional way of forecasting inflation is to predict a single level and/or growth rate of the PCE deflator. However, this approach is not useful for identifying options or risks facing decision-makers, especially in financial markets. Also, point estimates of inflation convey a sense of overconfidence. Our approach is more practical for decision-makers who must hedge their portfolios, but it is also useful for policymakers, investors, and consumers who must attach a probability with each possible scenario of future price trends. Our results indicate that since June 2011 the probability of deflation has been persistently higher than of the other two scenarios. Thus, the recent years’ higher deflation probabilities may offer a justification for the persistence of the Federal Reserve’s highly accommodative monetary policy during 2012–14.     

Three Simple Techniques to Analyze a Complex Economic Phenomenon: The Case of Profits

This paper seeks to characterize the behavior of profits over the business cycle as a model for analyzing any economic series by a practicing business economist. It addresses three fundamental questions about profits that are common and critical to identifying the behavior of any macroeconomic series—mean-reversion, volatility, and trend. First, does profit growth over time exhibit mean-reverting behavior? Second, how volatile are profits, and does this volatility obscure the message of average profit growth? Third, how can we estimate a long-run trend growth component for profits and thereby separate profit cycles from its long-run trend growth component?     

Predicting future financial performance of banks from management’s tone in the textual disclosures

Quality & Quantity - Predicting bank performance is important for investors and regulatory authorities. Previous research on non-financial firms has shown that augmenting the numeric...     

Market Risk,Interest Rate Risk,and Interdependencies in Insurer Stock Returns: A System‐GARCH Model

We examine market risk, interest rate risk, and interdependencies in returns and return volatilities across three insurer segments within a System‐GARCH framework. Three main results are obtained: market risk is greatest for accident and health (A&H) insurers, followed by life (Life) and property and casualty (P&C) insurers; interest rate sensitivity is negative and greatest for Life insurers; and interdependencies in returns are significant with the magnitude being strongest between P&C and A&H insurers. The implication is that greatest diversification benefits arise between Life and the other segments of the insurance industry. Market risk and interest rate risk for diversified firms are smaller than those for nondiversified firms for both product and geographic diversification.     

Real-Estate Risk Effects on Financial Institutions’ Stock Return Distribution: a Bivariate GARCH Analysis

This paper examines two relationships using the bivariate generalized autoregressive conditionally heteroskedastic (GARCH) methodology. First, the relationship between equity returns of commercial banks, savings and loans (S&Ls) and life insurance companies (LICs), and those of the real-estate investment trusts (REITs), a proxy for the real-estate sector performance. Second, the relationship between conditional volatilities of the stock returns of these financial intermediaries (FIs) and that of REITs. The former relationship allows the spillover of returns between the real-estate and the financial intermediation sector to be analyzed. The latter allows an investigation of the prevalence, direction and strength of inter-sectoral risk transmission to be carried out. Several interesting results are obtained. First, the equity returns of the FIs considered follow a GARCH process and should be modeled accordingly. Second, as found in the literature, returns on REITs should be modeled using the Fama-French multiple factor model. However, this model has to be extended to incorporate a GARCH error structure. Third, all FI returns considered are highly sensitive to REIT returns and the effects are both statistically and economically significant. This is an indication that shocks to REITs returns spillover to the former markets. Fourth, spillover of increased volatility in the real-estate sector to S&Ls and LICs is significant but not to commercial banks.     

Time Series Data Mining with an Application to the Measurement of Underwriting Cycles

Underwriting cycles are believed to pose a risk management challenge to property-casualty insurers. The classical statistical methods that are used to model these cycles and to estimate their length assume linearity and give inconclusive results. Instead, we propose to use novel time series data Mining algorithms to detect and estimate periodicity on U.S. property-casualty insurance markets. These algorithms are in increasing use in data science and are applied to Big Data. We describe several such algorithms and focus on two periodicity detection schemes. Estimates of cycle periods on industry-wide loss ratios, for all lines combined and for four specific lines, are provided. One of the methods appears to be robust to trends and to outliers.