Academic journal article The Lahore Journal of Economics

The Impact of Exchange Rate Volatility on Trade: A Panel Study on Pakistan's Trading Partners

Academic journal article The Lahore Journal of Economics

The Impact of Exchange Rate Volatility on Trade: A Panel Study on Pakistan's Trading Partners

Article excerpt

(ProQuest: ... denotes formulae omitted.)

1. Introduction

Exchange rates affect the true prices of commodities traded among countries of the world; it determines the price actually paid when each trade transaction is executed. At the same time, domestic inflation also plays a vital role in determining the changing patterns in the prices of tradable commodities (which may be intensified by exchange rate instability). However, using a single currency such as the US dollar (USD) as a vehicle currency can help insulate trade from such distortions by keeping the domestic currency exchange rate stable, either by setting a fixed rate or controlling it through a peg. This strategy can help low- income developing countries address uncertainties in trade flows.

For developing countries, problems regarding trade intensify when, along with inflation, external elements emerge in the form of "shocks" or "news" and disturb the flow of international prices paid for commodities or stocks. Such elements disrupt smooth and regular exchange rate flows and are often manifested through currency crises and stock market crashes (see Hernández & Schmidt-Hebbel, 2002. Sometimes, such distortions may also be artificially generated in the form of "speculation." Exchange rates often exhibit highly unstable and indeterminable patterns in response to these shocks, and the resulting pattern is referred to as "volatility." If such a shock lasts long and has a temporal impact that causes a wave in the flow of exchange rates, and if these waves deter or delay the movement of exchange rates back to their original state, the consequent trade patterns will disturb the stream of expected returns by raising the probability of loss for the traders concerned.

It is important to understand how frequently such shocks might occur and how long they might persist. This necessitates the formulation of effective strategies and policies to protect traders' interests and to keep their incentive to trade intact. A review of the historical research on exchange rate volatility reveals that there is no consensus on how to estimate the precise impact of volatility on international trade flows; such methods tend to split into four streams (see Table 1). Increasingly, the literature leans toward two main outcomes: (i) a significant negative impact of exchange rate volatility on trade and (ii) no or an insignificant impact on trade. However, these outcomes remain highly subjective due to the nature, size, and type of sample; the frequency of data; the nature of the volatility proxy; and the estimation techniques employed for analysis (Ozturk, 2006). Nevertheless, the more common outcome observed in the literature is that of a significantly inverse relationship between trade and exchange rate volatility (see Table 5.1).

Traders usually respond in a variety of ways when facing a "risk" element. Since trade-related risk intensifies on arrival of a news/shock element in the market, many traders may try to expand their trade to compensate for the expected loss in profit margins by revising their portfolios (the "modern [risk portfolio]" school of thought). Others may reduce their trade volume by diverting investments from high-risk ventures to low-risk ones (the "traditional" school of thought).

As far as data frequency is concerned, many studies have employed relatively low-frequency data such as annual, biannual, or quarterly series to measure the volatility impact (see Berger, Sturm, & de Haan, 2000; Bénassy-Quéré, Fontagné, & Lahrèche-Révil, 2001; Bahmani-Oskooee, 2002; Crowley & Lee, 2003; Mustafa & Nishat, 2004; Kemal, 2005; Azid, Jamil, & Kousar, 2005; Chit, Rizov, & Willenbockel, 2008; Aliyu, 2008). In time-series analyses, most models are developed to estimate high-frequency data. However, if such models are applied to low-frequency data, this can give rise to skepticism about the results because the first three moments' parameters may strongly influence the results. …

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