This paper examines the effects of financial crises on the long memory volatility dependency of daily exchange returns focusing on the Asian crisis in 97-98 and the Global crisis in 08-09. By using the daily KRW-USD and JPY-USD exchange rates which have different trading regions and volumes, this paper first applies both the parametric FIGARCH model and the semi-parametric Local Whittle method to estimate the long memory volatility dependency of the daily returns and the temporally aggregated returns of the two exchange rates. Then it compares the effects of the two financial crises on the long memory volatility dependency of the daily returns. The estimation results reflect that the long memory volatility dependency of the KRW-USD is generally greater than that of the JPY-USD returns and the long memory dependency of the two returns appears to be invariant to temporal aggregation. And, the two financial crises appear to affect the volatility dynamics of all the returns by inducing greater long memory dependency in the volatility process of the exchange returns, but the degree of the effects of the two crises seems to be different on the exchange rates.
Keywords: Daily Foreign Exchange Rate, Financial Crisis, Long Memory Volatility Dependency, FIGARCH Model, Local Whittle Method, Temporal Aggregation.
JEL Classification: C14, C22, F31, G15
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This paper is concerned with the intriguing effects of financial crises on the long memory volatility dependency of daily foreign exchange rates. It has been well known that many financial time series data including foreign exchange rates exhibit the long memory dependency intrinsic to their conditional variance process with quite persistent and hyperbolic decaying autocorrelations (Baillie, 1996). And, there has been quite interest in finding the reasons and underlying causes for the empirical findings of the long memory dependency in the conditional variance process. Granger and Ding (1995) has presented that the contemporaneous aggregation of the stable Generalized Autoregressive Conditional Heteroskedasticity (GARCH) process can result in very persistent autocorrelations which is the typical feature of the long memory property. And, Andersen and Bollerslev (1997) has shown that the contemporaneous aggregation of weakly dependent information flow process can produce the long memory property in the conditional variance process.
While some papers have argued that the aggregation is behind the long memory property and the aggregation of heterogeneous individual Autoregressive (AR) process may produce the long memory property, other papers have suggested that the observed long memory property may be generated by the presence of various types of structural breaks or regime switches in financial markets. In particular, the empirical studies including Granger and Hyung (2004) and Choi and Zivot (2007) have presented the evidence that spurious long memory can be due to the presence of occasional structural breaks detected in the financial time series, and they have conjectured that the long memory persistence may be overstated due to the presence of the structural breaks. Similarly, Granger and Terasvirta (1999) and Diebold and Inoue (2001) have presented that a process that switches the regime or switches in sign could have the characteristics of the long memory property.
Furthermore, the financial crisis marked by several reasons including the sharp decrease in credit or the collapse of exchange rate regime generate extreme disruption of normal functions of financial and monetary system, thereby hurting the efficiency of the economy (Fratzscher, 2009; Kohler, 2010; Razin and Rosefielde, 2011; Goldstein and Razin, 2013)1. Clearly the last few years have been characterized by great turmoil in the world financial system. In this context, this paper focuses on the two important financial crises which affected the world foreign exchange markets significantly, the Asian crisis in 1997-1998 and the Global crisis in 2008-2009. …