Directional predictability and time-varying spillovers between stock markets and economic cycles

Bekiros, Stelios; Shahzad, Syed Jawad Hussain; Arreola-Hernandez, Jose; Rehman, Mobeen Ur

doi:10.1016/j.econmod.2017.10.003

Cited by 17 publications

(6 citation statements)

References 52 publications

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“…Looking at the U.S. experience, Chen et al (1986), Kim (2003), Humpe and Macmillan (2009) and Schwert (1990) detected a positive significant impact of industrial sector output on equity prices. More recently, Bekiros, Shahzad, Arreola‐Hernandez, and Rehman (2018) have found sensitivity of U.S. stock returns to business cycle shocks only during extreme stock market states.…”

Section: Literature Reviewmentioning

confidence: 99%

U.S. stock prices and macroeconomic fundamentals: Fresh evidence using the quantile ARDL approach

2020

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This paper explores the long‐run relationship and the associated short‐run dynamics between the U.S. stock market and three major macroeconomic fundamentals, namely the U.S. industrial production index, the U.S. 10‐year Treasury bond yield and the West Texas Intermediate oil price, for the time period covering 1985–2015. The quantile autoregressive distributed lag (QARDL) model presented by Cho et al. (2015) Journal of Econometrics, 188, 281–300, which combines the autoregressive distributed lag model of Pesaran and Shin (1998), Cambridge University Press, and Pesaran et al. (2001) Journal of Applied Econometrics, 16, 289–326, and the quantile regression methodology of Koenker and Bassett (1978), Econometrica, 46, 33–50, is used. This approach analyses the links among the variables over a range of quantiles which represent different states of the equity market. The findings show a significant quantile‐varying cointegration relationship between U.S. equity prices and the three selected macroeconomic factors. The U.S. industrial production index is found to have the largest long‐run influence on the equity market. These findings highlight the importance of market players as well as policymakers to develop a greater understanding and awareness of how macro variables impact stock prices under different stock market conditions, particularly in relation to optimal asset allocation and risk management strategies, among others.

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Section: Literature Reviewmentioning

confidence: 99%

U.S. stock prices and macroeconomic fundamentals: Fresh evidence using the quantile ARDL approach

2020

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“…Similar methodologies have been used to investigate for the presence of volatility spillovers in stock markets ( Awartani and Maghyereh, 2013 ; Bekiros et al, 2018 ; Liow, 2015 ; Shahzad et al, 2017 ). 2 Alternatively, Salisu and Oloko (2015) identify results somewhat contrasting to other literature, identifying a significant positive return spillovers from the US stock market to oil markets.…”

Section: Introduction and Motivationsmentioning

confidence: 99%

Co-movements and spillovers of oil and renewable firms under extreme conditions: New evidence from negative WTI prices during COVID-19

2020

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We test for the existence of volatility spillovers and co-movements among energy-focused corporations during the outbreak of the COVID-19 pandemic, inclusive of the April 2020 events where West Texas Intermediate (WTI) oil future prices became negative. Employing the spillover index approach of Diebold and Yilmaz (2012); as well as developing a DCC-FIGARCH conditional correlation framework and using estimated spillover indices built on a generalised vector autoregressive framework in which forecast-error variance decompositions are invariant to the variable ordering, we examine the sectoral transmission mechanisms of volatility shocks and contagion throughout the energy sector. Among several results, we find positive and economically meaningful spillovers from falling oil prices to both renewable energy and coal markets. However, this result is only found for the narrow portion of our sample surrounding the negative WTI event. We interpret our results being directly attributed to a sharp drop in global oil, gas and coal demand, rather than because of a sudden increase in oil supply. While investors observed the US fracking industry losing market share to coal, they also viewed renewables as more reliable mechanism to generate long-term, stable and low-cost supply.

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“…Our study is comparable and further extension from Zhou et al (2019) through using three different oil types, that is, WTI, Brent and OPEC and the exchange rates of the E7 + 1 countries. Jiang et al (2016) and Bekiros et al (2018) also used CQ. Studies are considering the agricultural commodities and spillover effects on US stock market respectively.…”

Section: Resultsmentioning

confidence: 99%

Directional predictability from energy markets to exchange rates and stock markets in the emerging market countries (E7 + 1): New evidence from cross‐quantilogram approach

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Directional predictability and time-varying spillovers between stock markets and economic cycles

Cited by 17 publications

References 52 publications

U.S. stock prices and macroeconomic fundamentals: Fresh evidence using the quantile ARDL approach

U.S. stock prices and macroeconomic fundamentals: Fresh evidence using the quantile ARDL approach

Co-movements and spillovers of oil and renewable firms under extreme conditions: New evidence from negative WTI prices during COVID-19

Directional predictability from energy markets to exchange rates and stock markets in the emerging market countries (E7 + 1): New evidence from cross‐quantilogram approach

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