Do financial variables help predict the state of the business cycle in small open economies? Evidence from Switzerland

Abstract

We analyze the forecasting ability of financial variables to predict the state of the Swiss business cycle up to eight quarters ahead. Overall, our results suggest that financial variables convey leading information for the prediction of business cycles, even when applied to a small open economy. However, we clearly find that model specifications need to be extended to include variables accounting for external shocks, such as exchange rates or international commodity prices. It also appears that the forecasting contribution of individual variables changes over time. Specifically, in the last two decades, stock market liquidity has replaced the term spread as the best single predictor.

Stock Market Liquidity Impact on Economic Development

“…Concluding, the majority of the countries that compose our sample present a positive impact of the stock market liquidity on the countries' economic development, in accordance with various authors like Beck & Levine (2004), Cooray (2010), Enisan & Olufisayo (2009), Florackis et al (2014), Levine & Zervos (1998), Meichle et al (2011) or Naes et al (2011) to name a few.…”

“…The liquidity measure applied on this study is based on the Amihud's (2002) illiquidity measure (LIQi,t-1). This measure has been used lately in similar studies, presenting statistically significant results (Apergis et al, 2015;Florackis, Gregoriou, & Kostakis, 2011;Meichle et al, 2011;Naes et al, 2011;Smimou, 2014). Also, as cited before, it is one of the few measures that still represents faithfully the stock market liquidity when using more recent data (Goyenko et al, 2009).…”

“…Since Levine (1991) several other studies have been published on the topic, the majority of which state that there is a positive relationship between the economic development/growth and the stock market liquidity (Ahmad, Etudaiye-Muhtar, Matemilola, & Bany-Ariffin, 2016;Apergis et al, 2015;Beck & Levine, 2004;Caporale et al, 2004;Carp, 2012;Castillo-Ponce, Rodriguez-Espinosa, & Gaytan-Alfaro, 2015;Cooray, 2010;Enisan & Olufisayo, 2009;Florackis et al, 2014;Levine & Zervos, 1998;Meichle et al, 2011;Naes et al, 2011;Ngare, Nyamongo, & Misati, 2014;Nowbutsing & Odit, 2009;Pradhan et al, 2015Pradhan et al, , 2014Rahman & Salahuddin, 2010;Rousseau & Wachtel, 2000;Smimou, 2014;Srinivasan, 2014;Wu et al, 2010). The time span analysed by these authors vary significantly, both in terms of starting and ending years - Naes et al (2011) captured data since 1947 whereas Srinivasan (2014) analysed the data available until 2013as well as its length that diverge from 11 years (Cooray, 2010) to 61 years (Naes et al, 2011).…”

“…Lastly, many control variables were used in the analysed papers where each one applies mainly between three to seven different variables in its study. Meichle et al (2011) even apply 36 variables in total (including the dependent variables which in some tests are used as control variables). These are predominantly measures of other dimensions of the stock market -for example, capitalization, market returns, volatility, etc.…”

“…The relationship between the stock market liquidity and the economic growth was studied by many authors throughout the years, as Levine(1991), Levine & Zervos (1998) and more recently by Rahman & Salahuddin (2010), Meichle, Ranaldo, & Zanetti (2011), Naes, Skjeltorp, & Ødegaard (2011), Florackis, Giorgioni, Kostakis, & Milas (2014), Smimou (2014), Apergis, Artikis, & Kyriazis (2015) e Galariotis & Giouvris (2015), to name a few. However, they all focus on economic growth and not on economic development as a composition of various dimensions like composite indices.…”