۱- Introduction

۲- Hypotheses development

۳- Estimation approach

۴- Data and empirical findings

۵- Concluding remarks

References

Abstract

This paper uses 15-minute exchange rate returns data for the six most liquid currencies (i.e., the Australian dollar, British pound, Canadian dollar, Euro, Japanese yen, and Swiss franc) vis-a-vis the United States dollar to examine whether a GARCH model augmented with higher ` moments (HM-GARCH) performs better than a traditional GARCH (TG) model. Two findings are unraveled. First, the inclusion of odd/even moments in modeling the return/variance improves the statistical performance of the HM-GARCH model. Second, trading strategies that extract buy and sell trading signals based on exchange rate forecasts from HM-GARCH models are more profitable than those that depend on TG models.

Introduction

In this paper, we examine the role of high-order moments in influencing exchange rate behavior. We are not the first to explore the role of higher moments in understanding exchange rate behavior. There is a literature on this; see Aggarwal (1990), Harvey and Siddique (1999), and Mittnik and Paolella (2000). These studies show that higher moments improve the statistical performance of the models. However, these studies only consider up to the fourth moment. High-order moments in excess of the fourth moment have not been considered in terms of how they influence exchange rate behavior.1 There are several economic channels/mechanisms through which higher moments can impact exchange rate behavior. The first channel of effect is “liquidity spirals” that results from the theoretical model of Brunnermeier and Pedersen (2009). The basic idea of their model is that invested securities contain positive average returns and a negative skewness. They explain the source of this positive returns and negative skewness. Positive returns owe to the premium resulting from speculators’ provision of liquidity while negative skewness is because investors make heavy losses and relatively mild gains from negative shocks (such as financial constraints) and positive shocks (such as liquidity), respectively. In other words, the impact of shocks is asymmetric, skewed heavily in favor of negative shocks. More specifically, their work implies that funding constraints determine market liquidity.