A New Index of the Business Cycle William B. Kinlaw (State Street Global Markets), et al.January 2020The authors introduce a new index of the business cycle that uses the Mahalanobis distance to measure the statistical similarity of current economic conditions to past episodes of recession and robust growth. Their index has several important features that distinguish it from the Conference Board’s leading, coincident, and lagging indicators. It is efficient because as a single index it conveys reliable information about the path of the business cycle. Their index gives an independent assessment of the state of the economy because it is constructed from variables that are different than those used by the NBER to identify recessions. It is strictly data driven; hence, it is unaffected
James Picerno considers the following as important: Uncategorized
This could be interesting, too:
James Picerno writes Coronavirus Forecast Update: 29 March 2020
James Picerno writes Book Bits | 28 March 2020
JW Mason writes Endless austerity, state and local edition
James Picerno writes Research Review | 27 March 2020 | Coronavirus
A New Index of the Business Cycle
William B. Kinlaw (State Street Global Markets), et al.
The authors introduce a new index of the business cycle that uses the Mahalanobis distance to measure the statistical similarity of current economic conditions to past episodes of recession and robust growth. Their index has several important features that distinguish it from the Conference Board’s leading, coincident, and lagging indicators. It is efficient because as a single index it conveys reliable information about the path of the business cycle. Their index gives an independent assessment of the state of the economy because it is constructed from variables that are different than those used by the NBER to identify recessions. It is strictly data driven; hence, it is unaffected by human bias or persuasion. It gives an objective assessment of the business cycle because it is expressed in units of statistical likelihood. And it explicitly accounts for the interaction, along with the level, of the economic variables from which it is constructed.
Predicting Recessions: Financial Cycle versus Term Spread
Claudio E. V. Borio (Bank for International Settlements), et al.
Financial cycles can be important drivers of real activity, but there is scant evidence about how well they signal recession risks. We run a horse race between the term spread – the most widely used indicator in the literature – and a range of financial cycle measures. Unlike most papers, ours assesses forecasting performance not just for the United States but also for a panel of advanced and emerging market economies. We find that financial cycle measures have significant forecasting power both in and out of sample, even for a three-year horizon. Moreover, they outperform the term spread in nearly all specifications. These results are robust to different recession specifications.
Sector Rotation through the Business Cycle: A Machine Learning Regime Approach
Maximilian Sauer (University of Cambridge)
Sector returns should theoretically differ during business cycle regimes. The notion of cyclical and defensive sectors is clearly established among practitioners and academics alike. On the other hand, the persistence, now- and forecastability of business cycles has been documented by a vast amount of literature. This study tests whether both strands can be merged to construct an investable sector rotation strategy based on the analysis of macroeconomic data. I find that both relationships hold: If one has forward looking information about GDP, outperformance from sector rotation is possible. Furthermore, one can nowcast the current position in the business cycle with some accuracy. While nowcasting accuracy is too small to translate into constant outperformance, the value of the examined methodology lies in the timely identification of major economic crises and provides economically superior performance by significantly reducing drawdowns during such.
A Business Cycle Asset Pricing Model
Wai Man Tse (Chu Hai College of Higher Education)
An asset pricing model is introduced that captures the market, liquidity, credit, and business cycle risks. The explicit incorporation of economic-phase-switching business cycle risks makes the predicted return volatility equal to the observed return volatility. Therefore, the concerns over excessive volatility and equity premium puzzle become insignificant in the model. The risk-return tradeoff dynamic disequilibrium model builds on the equilibrium CAPM, taken as its steady state. It has no worse explanatory power than that of the Fama-French three-factor model and its variants but significantly better out-of-sample predictive power and ex post S&P 500 portfolio returns over the last 20-year period.
Identifying News Shocks with Forecast Data
Yasuo Hirose (Keio University) and Takushi Kurozumi (Bank of Japan)
The empirical importance of news shocks—anticipated future shocks—in business cycle fluctuations has been explored by using only actual data when estimating models augmented with news shocks. This paper additionally exploits forecast data to identify news shocks in a canonical dynamic stochastic general equilibrium model. The estimated model shows new empirical evidence that technology news shocks are a major source of fluctuations in U.S. output growth. Exploiting the forecast data not only generates more precise estimates of news shocks and other parameters in the model, but also increases the contribution of technology news shocks to the fluctuations.
Domestic and Global Uncertainty: A Survey and Some New Results
Efrem Castelnuovo (University of Melbourne)
This survey features three parts. The first one covers the recent literature on domestic (i.e., country-specific) uncertainty and offers ten main takeaways. The second part reviews contributions on the fast-growing strand of the literature focusing on the macroeconomic effects of uncertainty spillovers and global uncertainty. The last part proposes a novel measure of global financial uncertainty and shows that its unexpected variations are associated to statistically and economically fluctuations of the world business cycle.
Learn To Use R For Portfolio Analysis
Quantitative Investment Portfolio Analytics In R:
An Introduction To R For Modeling Portfolio Risk and Return
By James Picerno