Modelling return and volatility spillovers in global foreign exchange markets

Abstract

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.

The spillover effects of tourism receipts, political risk, real exchange rate, and trade indicators in Turkey

“…The Diebold and Yilmaz (2012) have extensively been used to examine the intermarket returns and volatility spillovers (Antonakakis & Floros, 2016;Salisu, Oyewole, & Fasanya, 2018). While using the Diebold and Yilmaz by following the above procedures, the shock spillovers to return to tourism receipts, the political risk, real exchange rate, and trade indicators is examined.…”

Measuring Dynamic Return and Volatility Connectedness among Nigerian Financial Markets

“…Theoretical Framework There is no gainsaying that the Generalized Vector Autoregressive (VAR) method, developed by Koop, Pesaran and Potter (1996) and then Pesaran and Shin (1998) often referred as KPPS remain the basis of the various alternative methods often utilized for analyzing spillover in the literature (see Salisu et al, 2018). However, and partially due to its relative newness and robustness, the DY method has been widely accepted as the preferred measure of connectedness index.…”

“…Unlike the conventional VAR, the DY which uses decomposition of forecast error variance from VAR is suitable for the evaluation of the degree of interdependence among countries, assets and markets across different regions and within a country. There are many spillovers that can be generated using DY method, namely; Total-Spillovers, Directional-Spillovers, and Net-Pairwise-Spillovers (see Salisu et al, 2018;Sobti, 2018).…”

“…Measuring Dynamic Return and Volatility Connectedness among Nigerian Financial Markets Udeaja Salisu et al (2018), considers six most traded currency pairs in the world in their analysis of return and volatility spillovers using DY (2012) approach. Essentially, the study computes total, directional and spillover indexes covering the period from January, 1999 to December, 2014.…”

Measuring Return and Volatility Spillovers among Sectoral Stocks in Nigeria

“…From the policy perspective, there are compelling reasons for the analysis of volatility transmissions among sectoral stocks in the Nigerian stock market. First, "information about the intensity of these spillovers provides useful insights to portfolio investors on how to diversify their portfolio investments in order to maximize returns" (Salisu et al, 2018). Second, information about volatility transmissions would prove useful to policy makers in identifying likely sectors within the Nigerian Stock Market which may be vulnerable to higher risks (Fasanya and Akinde, 2019).…”