Research Review | 25 May 2018 | Business Cycle Risk

Is Fertility a Leading Economic Indicator?
Kasey Buckles (University of Notre Dame), at al.
March 28, 2018
Many papers show that aggregate fertility is pro-cyclical over the business cycle. In this paper we do something else: using data on more than 100 million births and focusing on within-year changes in fertility, we show that for recent recessions in the United States, the growth rate for conceptions begins to fall several quarters prior to economic decline. Our findings suggest that fertility behavior is more forward-looking and sensitive to changes in short-run expectations about the economy than previously thought.

Does Aggregate Economic Uncertainty Predict the Volatility of Financial Assets?
Zhuo Huang (Peking University), et al.
May 7, 2018
We investigate the role of economic uncertainty in predicting financial volatility. We use multiple popular measures of economic uncertainty in predictive regressions for a wide range of financial assets, including equities, bonds, currency, and commodities. We find two aggregate economic uncertainty significantly and positively predicts financial volatility across all the assets. The predictive power of economic uncertainty are heterogeneous across financial markets, and the principal component of uncertainty measures provides a good balance of such predictability. The improvements in predictive performance by using aggregate economic uncertainty appear to concentrate around highly volatile and recession periods. Our findings remain in the out-of-sample forecasting and after a number of robustness checks.

Polycyclic Portfolio Rebalancing
via an Economy’s Relative Information Processing Ratio

Edgar Parker (New York Life Insurance Company)
May 5, 2018
Procyclical assets tend to rise in value when the economy is expanding and fall with the advent of a recession. Countercyclical assets are instead negatively correlated with the state of the economy. Despite the use of optimization methods, hedging, and ad hoc rebalancing techniques most portfolios are procyclical in behavior and tend to lose value as the economy slows and equity markets fall. In this paper, a new portfolio rebalancing technique is introduced which has the potential to favorably and systematically pivot a portfolio between pro and counter cyclical strategies before regime changes in the economy. This “polycyclic” approach offers potential portfolio returns well above those of traditional methods. This technique can be implemented independently or in conjunction with other portfolio optimization methods.

When will the next recession strike? Monitor the outlook with a subscription to:
The US Business Cycle Risk Report

Finance and Business Cycles: The Credit-Driven Household Demand Channel
Atif R. Mian (Princeton University) and Amir Sufi (University of Chicago)
May 8, 2018
What is the role of the financial sector in explaining business cycles? This question is as old as the field of macroeconomics, and an extensive body of research conducted since the Global Financial Crisis of 2008 has offered new answers. The specific idea put forward in this article is that expansions in credit supply, operating primarily through household demand, have been an important driver of business cycles. We call this the credit-driven household demand channel. While this channel helps explain the recent global recession, it also describes economic cycles in many countries over the past 40 years.

Do Leading Indicators Forecast U.S. Recessions? A Nonlinear Re-Evaluation Using Historical Data
Vasilios Plakandaras (Democritus University of Thrace), et al.
June 15, 2017
This paper analyses to what extent a selection of leading indicators is able to forecast U.S. recessions, by means of both dynamic probit models and Support Vector Machine (SVM) models, using monthly data from January 1871 to June 2016. The results suggest that the probit models predict U.S. recession periods more accurately than SVM models up to six months ahead, while the SVM models are more accurate over longer horizons. Furthermore, SVM models appear to distinguish between recessions and tranquil periods better than probit models do. Finally, the most accurate forecasting models are those that include oil, stock returns and the term spread as leading indicators.

Macro Aspects of Housing
Charles K. Leung and Cho Yiu Joe Ng (City University of Hong Kong)
May 13, 2018
This paper aims to achieve two objectives. First, we demonstrate that with respect to business cycle frequency, there was a general decrease in the association between macroeconomic variables (MV) and housing market variables (HMV) following the global financial crisis (GFC). However, there are macro-finance variables that exhibited a strong association with the HMV following the GFC. For the medium-term business cycle frequency, we find that while some correlations exhibit the same change as the business cycle counterparts, others do not. These “new stylized facts” suggest that a reconsideration and refinement of existing “macro-housing” theories would be appropriate. We also provide a review of the recent literature, which may enhance our understanding of the evolving macro-housing-finance linkage.

Predicting US Recessions: A Dynamic Time Warping Exercise in Economics
Tasneem Raihan (University of California, Riverside)
September 10, 2017
Dynamic Time Warping (DTW) is a widely used algorithm in speech recognition for measuring similarity between two time series. This non-parametric technique overcomes the problems associated with Pearson’s correlation coefficient by allowing a non-linear mapping of one sequence to another obtained through the minimization of the distance between the two. Despite its superiority as a similarity measure, its application in the field of economics is almost non-existent. This paper seeks to fill this gap in the economics literature by providing a self-contained description of the method. In addition, it demonstrates an application of DTW to the prediction of recessions in US using Treasury term spread data. The exercise shows that DTW is successful in predicting the recessions of both 1999 and 2007. The predictions are stronger when asymmetric step-pattern is adopted. Also, compared to other non-parametric methods, DTW raises significantly fewer false signals of recessions. Finally, DTW concludes that given the current state of the economy there is a zero probability of a recession in the next one year.

Predictive Power of Markovian Models: Evidence from U.S. Recession Forecasting
Ruilin Tian and Gang Shen (North Dakota State University)
March 14, 2018
This paper brings new evidence of predicting the U.S. recessions through Markovian models. The Markovian models, including the Hidden Markov and Markov models, incorporate the temporal autocorrelation of binary recession indicators in a more traditional and natural way. Considering interest rates and spreads, stock prices, monetary aggregates, and output as the candidate predictors, we examine the out-of-sample performance of the Markovian models in predicting the recessions one to twelve months ahead. The Markovian models are superior to the probit models in detecting a recession and capturing the recession duration. We find the “one-month lag phenomenon” that the best Markovian model supported by statistical model selection procedures can always recognize the onset of a recession one month after it starts. In addition, the yield spread continues to serve as the most ecient predictor variable in explaining business cycles.

One thought on “Research Review | 25 May 2018 | Business Cycle Risk

  1. Pingback: Quantocracy's Daily Wrap for 05/26/2018 | Quantocracy

Comments are closed.