As an important macro variable, the exchange rate has a significant influence on the whole economy. This study focuses on the impact of exchange rate volatility on trade performance in Australia given the evidence from the Autoregressive Distributed Lag (ARDL) bounds testing approach at aggregate, sectoral and bilateral trade data levels. Despite the considerable amount of research that has been undertaken to analyse the impact of exchange rate volatility on trade performance, studies of the impact of exchange rate volatility on trade performance have reported many conflicting results since the results are significantly influenced both by the authors' modelling strategies, for example, the choices of sampling period, model specification, measurements of exchange rate volatility and countries considered, and by the contexts of their investigations. Some studies demonstrate that there are negative relationships between exchange rate volatility and trade performance whereas other studies show positive relationships. Some empirical literature suggests that exchange rate volatilities may have both positive and negative impacts on trade flows, while other studies show that there is no significant relationship between exchange rate volatility and trade flows. This study intends to explore new and previously unused quarterly data ranging from 1983 to 2007 and apply the ARDL bounds testing approach to estimate the effects of exchange rate volatility on Australia’s trade performance. This study makes a contribution to current research in various ways. First, this study develops two sets of nominal and real exchange rate volatility, applying the most commonly used measurements generated from moving average standard deviation (MSD) and the GARCH models for each nominal and real exchange rate. Secondly, it is based on a substantially longer period of quarterly data than previous studies. In addition, this study empirically investigates the impact of exchange rate volatility on the export and import flows of Australia from aggregate, sectoral and bilateral trade data levels, which can deal with the aggregation bias and deepen the analysis step by step and ensure the results are more reliable and robust
