Exchange rate determination and equity prices: Evidence from the UK

Litsios, Ioannis

doi:10.1016/j.jeca.2014.01.002

Cited by 6 publications

(1 citation statement)

References 19 publications

(13 reference statements)

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“…Most of the studies appear to focus more prominently on the role of oil price shocks in the stock price model. 1 A host of other studies examine the influence of exchange rate risks in stock markets models (see for example; Aydemir and Demirham, 2009;Kutty, 2010;Litsios, 2013;Dellas and Tavlas, 2013;Zubair, 2013;Al-Shboul and Anwar, 2014;Raza et al, 2016;Zivkov et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Modelling return and volatility spillovers in global foreign exchange markets

Salisu

Oyewole

Fasanya

2018

Journal of Information and Optimization Sciences

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The extant literature on exchange rate forecasting on the basis of the Dornbusch-Frankel, Frenkel-Bilson and Hooper-Morton models prominently reveals the dominance of the autoregressive models over the theory-based models. Some studies have however attempted to upturn the results by including the lagged dependent variable in the theory-based models which somewhat implies comparing a modified random walk with a traditional random walk. We follow a different approach both in terms of theory and methodology. We offer an innovative exposition of the Portfolio Balance theory to stock price-exchange rate nexus. Consequently, a predictive model for exchange rate where stock price is a predictor is formulated. The formulated model is expressed in both linear and nonlinear form in order to account for the role of asymmetric changes in stock prices in exchange rate forecasting. Thereafter, we employ the Lewellen (2004) and Westerlund and Narayan (2014) methods which account for any inherent statistical properties of the predictors. Our results validate the Portfolio Balance theory where we show that the sector-level stock prices consistently turn up as good predictors of the exchange rates. The predictive model proposed in this work does not require the inclusion of a lagged dependent variable to beat the autoregressive models which is the practice in the existing literature. We further demonstrate that asymmetry matters to a large extent in the nexus both for the in-sample and out-of-sample predictability.

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