The authors explain exchange rate volatility dynamics, with emphasis on the more economically destabilizing components, using a frequency dependent methodology. Their results inform suggestions for exchange rate stabilization policies, with attention to differences across developed and developing economies.
The authors isolate the most destabilizing components of exchange rate volatility, defined as “high-frequency” components, from normal trend or cyclical components. The goal of this partitioning, accomplished using spectral analysis, is to identify those macroeconomic and policy variables best suited to reducing both overall volatility and a subset of irregular influences on exchange rate fluctuations. The authors control for such country-specific features as openness, rate regime, and population, which characterize the differences between developing and developed economies. Finally, they offer recommendations to decision makers in developing countries on the prevention and management of currency crises.
How Is This Research Useful to Practitioners?
Investors who are active outside their home financial markets, particularly in developing economies, might find the authors’ focus on the less predictable influences on exchange rate variation useful. Research on exchange rate movements related to trends and the business cycle is well established. Improved risk management techniques may result from a better understanding of the more erratic and obscure components of volatility, assuming they can be explained with specificity.
The authors note that developing countries’ currencies experience 1.11 times higher total volatility, on average, than those of developed countries. But for the high-frequency components of that volatility, the ratio increases to 4.3. So, the whole (i.e., total variability) is not just the sum of its parts, which is reflected in the finding that although a floating exchange rate regime is associated with greater overall volatility, the high-frequency component actually decreases in floating-rate versus fixed-rate contexts.
Such distinctive contextual effects carry over to the developed versus developing economies categories. Although developed economies can attain currency stability through anti-inflationary policies, developing economies are best served by seeking higher terms of trade for their exports. Additionally, higher levels of foreign reserves actually increase volatility in developing economies, which is an argument for actively intervening in exchange markets when rate stability is the goal. Similarly, while current account and budget deficits in developing economies do not appear to have pernicious volatility effects, growth in their equity markets seems to facilitate exchange rate stability.
How Did the Authors Conduct This Research?
Datastream data of daily bilateral spot exchange rates of 29 currencies against the US dollar for the period 1988–2010 at an annual frequency are used to delineate the components of rate volatility. Unlike previous research in which a “time domain” approach (i.e., variance/standard deviation–based metrics) is applied—subjecting the research to potentially spurious results if the component effects are not uniform—the authors take an approach similar to that of Orlov (Economics Letters 2006). They implement spectral methodology to isolate the impact of high-frequency components and account for their time-series and cross-sectional changes using panel regressions. This methodology is a form of frequency analysis whose goal is to determine the relative importance of cycles of different orthogonal components of exchange rate time series. The authors use Fourier transformations to obtain smoothed periodograms and, ultimately, kernel estimates of the studied variables. They justify their approach with the supposition that the most erratic components of exchange rate volatility are indeed the most disruptive.
Acknowledging that their sample period is relatively limited, the authors further adjust using a panel data setting. To account for all plausible influences on rate volatility, they initially model an extensive set of macroeconomic and policy variables. These variables cover the potential effects of real economic shocks and inflation, currency crises, exchange rate regimes, monetary variables, government budget and trade balances, and financial sector sophistication. Finally, after performing sensitivity analysis, the authors conclude that their model proves robust to stringent fitness tests.
Given increased explanatory sophistication is the sine qua non of scientific progress, the authors’ attempt to map the nuances of exchange rate volatility is an understandable goal. The particular challenge faced here, as in any model of price setting subject to many possible influences, is that of multicollinearity. Absent a sound theoretical foundation for causation, potentially significant findings run the risk of nonreplicability. Determining whether the study’s methodological adjustments have definitely met the research challenge may require the test of policymakers’ putting the authors’ suggestions into practice and judging the resultant efficacy, particularly in developing economies.