Recession Prediction Using Yield Curve and Stock Market Liquidity Deviation Measures

Erdoğan, Oral; Bennett, Paul B.; Ozyildirim, Cenktan

doi:10.1093/rof/rft060

Cited by 30 publications

(14 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, financial historians, like Ahamed (2009) and Ferguson (2008), have suggested that financial crises are often preceded by bubbles in the asset and commodity markets, which, in turn is vindicated by Phillips and Yu (2011) based on formal tests of bubble detection in real-time. 1 See for example, Rasche and Tatom (1977), Mork and Hall (1980), Hamilton (1983Hamilton ( , 2011, and Hickman et al (1987), Balke et al, (2002Balke et al, ( , 2010, Brown and Yücel (2002), Barsky and Kilian (2004), Jones et al (2004), Kilian (2008aKilian ( ,b, 2009a, Elder and Serletis (2010), Nakov and Pescatori (2010), Baumeister and Peersman (2013a,b), Kang and Ratti (2013a, b), Antonakakis et al, (2014a), Bjørnland and Larsen (2015), and Baumeister and Kilian (2015), and references cited therein.. 2 More recent studies followed, and includes that of of Fischer and Merton (1984), Barro (1990), Fama (1981Fama ( , 1990, Harvey (1989), Stock and Watson (1989), Choi et al (1999), Schwert (1990), Estrella and Mishkin (1998), Colombage (2009), Nyberg (2010), Mili et al (2012), and Erdogan et al, (2015). 3 Interestingly, Campbell et al (2001) proposes that the variance of stock returns, rather than the returns themselves, have predictive content for output growth.…”

Section: Introductionmentioning

confidence: 99%

Common cycles and common trends in the stock and oil markets: Evidence from more than 150 years of data

2017

View full text Add to dashboard Cite

This paper investigates the role of permanent and transitory shocks, within the framework of common cycles and common trends, in explaining stock and oil prices. We perform a multivariate variance decomposition analysis of monthly data on the West Texas Intermediate (WTI) oil price and the S&P500. The dataset used in the study spans a long period of 150 years and therefore contains a rich history to examine both the short-and longrun comovement properties of oil and stock prices. Given that the oil and stock markets might comove both in the short-and long-run, it is of interest to see the relative impacts of transitory and permanent shocks on both variables. We find that (log) oil price and (log) S&P 500 share a common stochastic trend for our full sample of September 1859 to July 2015, but a common cycle only exists during the post-WW II period. Full and post-WW II samples have quite different common feature estimates in terms of the impact of permanent and transitory shocks as measured by the impulse responses and forecast error variance decompositions. We also find that in the short-run oil is driven mostly by cycles (transitory shocks) and stock market is mostly driven by permanent shocks. But, permanent shocks dominate in the long-run.1

show abstract

Section: Introductionmentioning

confidence: 99%

Common cycles and common trends in the stock and oil markets: Evidence from more than 150 years of data

2017

View full text Add to dashboard Cite

show abstract

“…Erdogan et al . () employ the probit model and provide in‐sample and out‐of‐sample evidence of the improvement in forecasting US recession by combining the yield curve with the stock market depth and liquidity deviation factor.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Kessel () was the first to focus specifically on the behaviour of the term spread across the business cycle. When downward sloping, the yield curve has been found to be consistent with a heightened probability of a period of negative or sharply lower real economic growth (Erdogan, Bennett, & Ozyildirim, ). However, there is some evidence that the yield curve's predictive ability is deteriorating (Chinn and Kucko, ; Dombrosky & Haubrich, ; Estrella & Mishkin, ; Khomo & Aziakpono, ).…”

Section: Introductionmentioning

confidence: 96%

Forecasting the Probability of Recessions in South Africa: the Role of Decomposed Term Spread and Economic Policy Uncertainty

Aye

Christou

Gil-Alana

et al. 2018

J of Intl Development

View full text Add to dashboard Cite

This paper decomposes the term spread into the expectation and the term premium components using a fractional integration approach and subsequently uses same with the economic policy uncertainty index to forecast the probability of recession in South Africa. We use different specifications of the probit model and quarterly data from 1990:1 to 2012:1. Our out-of-sample results show that the model that incorporates the expectation component and economic policy uncertainty provides the best forecast of recession. All three recession periods in our sample were accurately dictated by the prediction models and the best forecast occurred at the four quarters ahead horizon. A robustness check with a longer sample from 1946q1 to 2017q4 but excluding the factors and economic policy uncertainty due to data limitation provided justification for decomposing the term spread as the model with the expected spread turned out to be the best. We draw the implications of these findings.

show abstract

“…In the same vein, there is also a large literature that links movement in stock prices (again within and out-of-sample) with economic activity, dating as far back as Mitchell and Bums (1938). More recent studies followed, like Estrella and Mishkin (1998), Stock and Watson (2003), Weber (2004) Nyberg (2011), and have been surveyed in detail in Erdogan et al, (2015), and Balcilar, Gupta and Wohar (forthcoming).…”

Section: A Brief Review Of the Related Literaturementioning

confidence: 99%

Forecasting oil and stock returns with a Qual VAR using over 150 years off data

Gupta

Wohar

2017

Energy Economics

View full text Add to dashboard Cite

The extant literature suggests that oil price, stock price and economic activity are all endogenous and the linkages between these variables are nonlinear. Against this backdrop, the objective of this paper is to use a Qualitative Vector Autoregressive (Qual VAR) to forecast (West Texas Intermediate) oil and (S&P500) stock returns over a monthly period of 1884:09 to 2015:08, using an in-sample period of 1859:10-1884:08. Given that there is no data on economic activity at monthly frequency dating as far back as 1859:09, we measure the same using the NBER recession dummies, which in turn, can be easily accommodated in a Qual VAR as an endogenous variable. In addition, the Qual VAR is inherently a nonlinear model as it allows the oil and stock returns to behave as nonlinear functions of their own past values around business cycle turning points. Our results show that, for both oil and stock returns, the Qual VAR model outperforms the random walk model (in a statistically significant way) at all the forecasting horizons considered, i.e., one-to twelve-months-ahead. In addition, the Qual VAR model, also outperforms the AR and VAR models (in a statistically significant manner) at long-run horizons for oil returns, and short-to medium-run horizons for stock returns.

show abstract

Recession Prediction Using Yield Curve and Stock Market Liquidity Deviation Measures

Cited by 30 publications

References 24 publications

Common cycles and common trends in the stock and oil markets: Evidence from more than 150 years of data

Common cycles and common trends in the stock and oil markets: Evidence from more than 150 years of data

Forecasting the Probability of Recessions in South Africa: the Role of Decomposed Term Spread and Economic Policy Uncertainty

Forecasting oil and stock returns with a Qual VAR using over 150 years off data

Contact Info

Product

Resources

About