The Relationship Between Exchange Rate Volatility Finance Essay

Moccero and Winograd (2006, p.2) indicate that the theoretical literature on the impact of exchange rate fluctuations on trade and the resulting demand for stable anchors (exchange rate fixing) have long been a highly debated topic among economists. Traditional models examined the exchange rate volatility effect on trade based on the producer theory of the firm under uncertainty, where firm profitability is related to exchange rate fluctuations. Some theoretical models point to a positive relationship. Baron (1976) shows how an increase in exchange rate volatility may not necessarily lead to an adverse effect on the level of trade when hedging opportunities exist.

Furthermore, some authors have shown that an increase in exchange rate volatility may be beneficial for trade (De Grauwe, 1988; Franke, 1988). The most obvious case is that in which exporters are risk lovers. However, De Grauwe (1988) shows that, when exporters are sufficiently risk-averse, a positive relationship may still arise. Very risk-averse firms will worry about the worst possible scenario. When risk increases, the way to avoid a drastic decline in export revenues is by increasing the export volume. Franke (1991) highlights that volatility may increase exports if it also associated with an increase in the real exchange rate level.

One of the issues that have received considerable attention in recent years is the effect of exchange rate risk on the volume of trade. Many analysts of international economics concur that the present floating exchange rate has engendered substantial volatility in both the nominal and the real exchange rate. This in turn has led to a decrease in international trade transactions.

Since the beginning of the current float, numerous theoretical papers have been written to explain the effects of increased exchange-rate volatility on trade, and even more have been published evaluating these ideas empirically. These studies have applied different methods and obtained different results, but no consensus has been reached regarding how to model, or even how to properly measure, exchange-rate volatility.

There are a large number of theoretical and empirical studies that analyse the relationship between exchange rate volatility and international trade, there are theoretical models supporting both negative and positive relationships between them. Empirical studies do not provide clear-cut results, either; most of the empirical results present a negative relationship, but this relationship is not always robust. Moreover, studies often find insignificant negative or positive relationships when employing other estimation methods such as instrument variable estimation or the introduction of fixed country effects.

In 1950, after the Second World War, Canada became the first major country to adopt a floating exchange rate. Only between 1962 and 1970 it went back to a fixed exchange rate and thereinafter floating currency regime has been applied. Overall, the Canadian dollar has floated for 42 out of the past 50 years. No other major country has had as much experience with a floating exchange rate.

Canada is a major trading nation whose combined value of exports and imports is the equivalent of more than two-thirds of the country's GDP. Canada trades with the world but its main trading partner is the U.S., accounting for roughly three-quarters of trade and the majority of capital moving in and out of Canada.

Canada has one of the highest levels of economic freedom in the world. Today Canada closely resembles the U.S. in its market-oriented economic system, and pattern of production. In 2008 USA shared 77.7% of total exports market and 52.4% of total imports market of Canada. [1]

Table 1.1: Canada Import and Export from 2000 to 2009

Year

Import from USA

Export to USA

Total Imports

Total Exports

Share of USA from Total Canada Imports

Share of USA from Total Canada Exports

2000

154,630

241,909

240,363

278,218

64.33%

86.95%

2001

140,972

227,161

221,581

260,959

63.62%

87.05%

2002

139,139

219,929

222,216

252,416

62.61%

87.13%

2003

145,427

233,172

239,837

271,966

60.64%

85.74%

2004

169,565

267,479

273,427

316,762

62.01%

84.44%

2005

177,589

301,882

314,360

360,164

56.49%

83.82%

2006

192,096

316,685

350,113

388,314

54.87%

81.55%

2007

205,528

330,859

378,925

419,064

54.24%

78.95%

2008

213,216

352,237

407,113

453,646

52.37%

77.65%

2009

163,579

236,274

319,763

314,990

51.16%

75.01%

Source: http://www.ic.gc.ca (Industry Canada).

1.1 Problem Statement

Based on the theory that says higher exchange-rate volatility leads to higher cost for risk-averse traders and to less foreign trade. The volume of the real exports from one country to the other is a function of the real exchange rates between the two countries. This is because the exchange rate is agreed on at the time of the trade contract, but payment is not made until the future delivery actually takes place. If changes in exchange rates become unpredictable, this creates uncertainty about the profits to be made and, hence, reduces the benefits of international trade.

If traders were uncertain as to how the exchange rate affects their firms' revenue, the volume of trade will be reduced. Meanwhile if forward markets are not sufficiently developed, traders may still be unsure of how much foreign exchange they want to cover.

Because importers and exporters are on opposite sides of a risky trading relationship, their respective roles are reversed. Under very general conditions, a firm might benefit from increased volatility and thus increase the volume of its exports in response.

Previous empirical studies have investigated various hypotheses and subjected them to robustness checks. Some of the studies perform long-time series analyses and employ samples involving a large number of countries. Various kinds of volatility measures are employed in the literature. The studies also compare the impact of volatility on trade among developed countries with that among developing countries. An argument put forward by the opponents of the floating exchange rates is that such rates introduce uncertainty into the foreign exchange market, which could deter trade flows. However, a theoretical argument is put forward by some to show that uncertainty could also boost trade flows if traders increase their trade volume to offset any decrease in future revenue due to exchange rate volatility. Recent theoretical developments suggest that there are situations in which the volatility of exchange rates could be expected to have either negative or positive effects on trade volume.

This study investigates that how the exchange rate volatility of CAD and USD (Canadian dollar and United States dollar) affects the flows of bilateral trade between two countries.

Since this theory is not presenting a robust result, then this study will examine it using the related data of these two countries to see the effects of volatility of exchange rate on trade flows.

1.1.1 Research Question:

What is the extent to which exchange the rate volatility affects the volume of trade?

1.2 Research Objectives

The purpose or general objective of this study is to investigate the effect of exchange-rate volatility on bilateral trade flows between Canada and United States.

The specific objectives are:

Determining the volatility of real exchange rate which is used as a proxy for exchange-rate uncertainty.

Estimate the elasticity of fluctuations of real exchange rate of CAD to USD on bilateral trade flows of Canada and USA.

1.3 Research Questions

The research must reveal these points:

How is the effect of exchange rate risk on the volume of international trade?

What are the motivations to stabilize exchange rates in general?

What is the positive and negative impact of exchange rate on the volume of trade?

1.4 Anticipated Research Results

The effects of changes in income and relative prices on trade quantities are well-known. The expected sign of coefficient of foreign country's income in exports model is positive. Also the expected sign of coefficient of home country's income in imports model is positive. Implying that income has its expected effect; with increased income demand is increasing.

The same is true for the other coefficients. If the CAD depreciates, then it causes increase in Canada's exports to USA and decrease in its imports, so expected sign for the related coefficients are in respect positive and negative.

A negative sign for and is expected since this study assumes that traders are risk-averse and hedging is expensive or impossible, increased exchange-rate volatility will lead to a decline in the risk-adjusted anticipated profits from foreign trade.

1.5 Scope of the Study

1.5.1 Scope of Topic research

This research studies for the Impacts of exchange rate volatility on bilateral exports volume, using data from Canada and USA for analysis purpose.

1.6 Statement of Hypothesis

Regarding the effects of exchange-rate volatility, it has been argued that the higher volatility of exchange rates will impede trade flows by creating uncertainty about the profits to be made from international trade transactions. This is because most trade contracts are not for immediate delivery of goods; and, since they are denominated in terms of the currency of either the exporter or the importer, unpredictable changes in exchange rates affect realized profits and, hence, the volume of trade. So in this study a negative relationship between exchange rate volatility and volume of exports is expected.

But recent theoretical developments suggest that there are situations in which the volatility of exchange rate could be expected to have negative or positive effects on trade volume. So it will not be unexpected if the results show the positive relationship between the import volume and volatility of exchange rate.

1.7 Definitions of Terms

1.7.1 Exchange Rate Risk

Exchange rate risk management is an integral part of every firm's decisions about foreign currency exposure (Allayannis, Ihrig, and Weston, 2001).

1.7.2 Volume of Trade

The number of contracts traded during a specified period of time. It may be quoted as the number of contracts traded or as the total of physical units, such as bales or bushels, pounds or dozens [2] .

1.7.3 Exchange rate volatility

Traditionally, it is the average of conditional or unanticipated exchange rate changes. Would say it refers to the rate at which a country's currency in terms of others adjusts to changes in market conditions or policies as given by the government or a central monetary authority. If the exchange rate moves up and down rapidly over short time periods, it has high volatility. If the price almost never changes, it has low volatility.

Chapter 2 - Literature Review

2.0 Introduction

The exchange rate regime is an important macro variable that influences the whole economy therefore attracts keen attention of researchers. Since the breakdown of the Bretton Woods system of fixed exchange rates, a substantial body of theoretical and empirical literature has investigated the link between exchange rate volatility and international trade flows as this information contributes to the understanding of the transmission mechanism of exchange rate fluctuations on the economy. The general presumption is that an increase in exchange rate volatility will have an adverse effect on trade flows and consequently, the overall health of the world economy. However, neither theoretical models nor empirical studies provide us with a definitive answer (Baum and Caglayan, 2006, p.2). Using model of this research, nominal exchange rate and relative price volatility are exploreed. Theoretical models of sticky-prices and asset markets dating back to Dornbusch (1976) explicitly address nominal exchange rate and relative price behavior. These models impute behavior to the real exchange rate in the short run and the long run owing to differences in the speed of adjustment between nominal exchange rates and relative prices. So, it seems useful to decompose real exchange rate volatility into its separate components. The Mundell-Fleming model with sticky prices also provides a link between nominal exchange rates and real exchange rates. By investigating the components of real exchange rate volatility separately, this study is distinguished work from many others. Engel and Morley (2001), Mark and Sul (1999) and Cheung, Lai and Bergman (2004) - who study the components of the real exchange rate - are exceptions.

Here, also a simple variance decomposition of the real exchange is conducted, after controlling for real and nominal factors. The decomposition of the residual variance allows us to calculate the contributions of unexplained nominal exchange rate volatility, unexplained relative price volatility, and their covariance to the residual portion of real exchange rate volatility.

This analysis produces several noteworthy results. Three main ï¬ndings emerge. With the inclusion of nominal factors, the used model substantially reduces the real exchange rate volatility spread between developing and developed countries and helps explain Hausmann, Hwang and Rodrik (2006) ï¬nding. This research also finds that the evidence that nominal factors matter in both the short and long run. Nominal factors can have long-lived (at 5 years) effects on the volatility of the real exchange rate. This ï¬nding is consistent with the range of half-life estimates reported for real exchange rate mean reversion. Another result is that for developing countries, a much larger share of real exchange rate volatility stems from relative price volatility than for industrialized countries. The ï¬nding persists in both the short run and the long run.

2.1 Review of the Related Literature

Gaston and Trefler (1997) found that the high interest rates associated with the anti-inflation policy of the early 1990s had a greater impact on the Canadian labour market than the trade liberalization due to CUSTA [3] . Beaulieu (2000) presents evidence that the CUSTA tariff reductions have no effect on average annual earnings in the manufacturing industries for either skilled or less skilled workers. He attributes this finding to the fact that Canadian real wages didn't vary much after trade liberalization, so that there is not much variation to attribute to trade liberalization.

A study by Pozo (1992) examines the effect of exchange rate volatility on trade volume in the early years of the last century. She used two proxies to measure exchange rate volatility; the rolling standard deviation and generalized autoregressive conditional hetroskedestic process. Pozo investigates the volume of British exports to the U.S and finds an increase in volatility by any measure would have a decrease in the volume of trade. During the period of the flexible exchange rate regime adopted by Sri Lanka, Weliwita, Ekanayaka and Tsujii (1999) investigate the impact of exchange rate volatility on Sri Lanka's exports to six developed countries. They find Sri Lanka's exports to be negatively affected by bilateral real exchange rate volatility with these developed countries.

Lee (2002) reviews a number of statistical studies suggesting that the buoyant U.S. economy and the depreciation of the Canadian dollar were mainly responsible for a dramatic increase in Canadian exports to the United States in the 1990s. The CUSTA and NAFTA Agreements [4] , in contrast, are estimated to have increased Canadian exports to the United States by only around 9 percent.

Table 2.1: Relative Price Volatility for Canada-U.S. City Pairs

before and after CUSTA (from Engel and Rogers 1998)

CPI Category

1978-89

1994-97

% Change

1. All item

1.82

1.65

-9.30%

2. Food at home

2.42

2.39

-1.20%

3. Food away from home

2.00

1.70

-15.00%

4. Alcoholic beverages

2.61

2.21

-15.30%

5. Shelter

2.68

1.72

-35.80%

6. Fuel and other utilities

5.06

5.05

-0.20%

7. Household furnishings and operations

2.23

2.37

6.30%

8. Men's and boys' apparel

4.48

5.06

12.90%

9. Women's and girls' apparel

7.86

8.58

9.20%

10. Footwear

4.76

5.82

22.30%

11. Private transportation

2.61

2.03

-22.20%

12. Public transportation

6.75

6.46

-4.30%

13. Medical care

2.60

1.81

-30.40%

14. Personal care

2.51

2.71

8.00%

15. Entertainment

2.31

2.46

6.50%

Pooled (2-15)

3.63

3.54

-2.50%

Source: Table 2.B from Engel and Rogers (1998). Relative price volatility if measured as the mean values for all city-province pairs of the standard deviation over the indicated period of the two-month difference in the natural log of the relative CPI ratio.

The results in Engel and Rogers (1998) were partly driven by the changes in the variability of the Canada-U.S. relative prices that they calculated for 1978-88 and 1994-97. These results are reproduced in Table 2.1 and show the greatest percentage declines in cross-border relative price volatility for the following categories: fuel and utilities, medical care, private transportation, shelter, and alcoholic beverages. Given the non-tradable nature of several of these categories, it is difficult to ascribe the declines in price volatility to trade liberalization. By contrast, cross border price variability actually increased for categories such as clothing and footwear where liberalization would be expected to have the largest impact on trade liberalization. While Engel et al. (1998) make no attempt to differentiate among goods based on tradability; their finding of greatest price convergence for less tradable goods reinforces their regression-based conclusion that price convergence was not accelerated by CUSTA.

The same conflicting evidence for the relationship between exchange rate volatility and trade exists with regard to developing countries. While some studies such as by Caballero and Corbo (1989), Pick, Daniel H. (1990), Arize, Osang, and Slottje (2005), Grier and Smallwood (2007), find a significant negative relationship, the study by Bahmani-Oskooee (1991) shows that the significant negative relationship is just for some of the developing countries and not for the others, and Pickard, J. C. (2003) finds weak negative relationship between the two. McKenzie (1999) uses both aggregate data and disaggregates data of Australia to show that the impact of exchange rate volatility does differ between traded good sectors.

Byrne, Darby and Mac Donald (2006) consider the impact of exchange rate volatility on the volume of bilateral US trade (both exports and imports) using sectoral data. Amongst the novelties in their approach are the use of sectoral industrial price indices, rather than an aggregate price index, and the construction of the sectoral groupings, which is based on economic and econometric criteria. They find that separating trade into differentiated goods and homogeneous goods results in the most appropriate sectoral division, and they also report evidence to suggest that exchange rate volatility has a robust and significantly negative effect across sectors, although it is strongest for exports of differentiated goods.

Rauch (1999) emphasizes the importance of search costs involved in matching buyers and sellers for differentiated goods: trade of this kind is facilitated by knowledge of particular markets or networks since the characteristics of some manufactured products are not readily known (e.g. performance and reliability). Given these search costs this also means that it is not easy for firms to switch foreign suppliers or find new buyers in response to changes in the exchange rate. This will consequently affect profitability, with negative effects in instances where individuals dislike increased risk. In contrast, homogeneous, or intermediate, goods are typically traded on exchanges; product characteristics do not vary between suppliers, can be substituted quickly and are therefore not regarded as having search costs. There will be considerable indifference between homogeneous goods sourced from different suppliers.

One of the earliest empirical studies of the relationship between trade and exchange rate volatility is that of Hooper and Kohlhagen (1978), who find limited evidence from the impact of volatility on bilateral trade prices and no evidence on bilateral trade volumes. Bini-Smaghi (1991) argues that sectoral studies may have greater potential since they do not constrain income and price elasticities to be equal across sectors as in aggregate studies. There have been a relatively small number of recent papers that empirically test the impact of exchange rate volatility on trade using sectoral disaggregate data. The sectoral studies that have been conducted have not been especially supportive of a negative relationship between trade and exchange rate uncertainty and this may be attributable to the relatively small samples used and poor price proxies employed. The only general conclusion that would seem to result from this literature is that differences do exist across sectors. Clark, Tamirisa, Wei, Sadikov and Zeng (2004), as part of their comprehensive study on trade and volatility, use disaggregate data divided into homogeneous and differentiated goods. They adopt a gravity framework and find evidence that there is a significant effect of exchange rate volatility on differentiated goods: if volatility is increased by one standard deviation around its mean, trade is reduced by 7-9%. Although this result is not robust to time effects, the authors believe these may actually model the volatility effects themselves since they are time specific. Recently, Tenreyro (2004) adopts a similar approach to Rose (2000) by using gravity equations for aggregate country data and considers the impact of uncertainty on trade. She reports evidence of a negative effect from intra-year volatility on trade. However once instrumental variables are used this effect all but disappears, which suggests there is a substantial endogeneity issue in the sample of countries that she uses.

2.2 Exchange Rate

In a fixed exchange rate scenario market forces will still act to change the real exchange rate. Therefore, the government will have to intervene to keep the nominal peg. As established earlier, the use of foreign reserves is one such method. In a floating exchange rate regime, movements in the exchange should not affect reserves as much. This results because the exchange rate is expected to absorb the macroeconomic shocks. Even if a country wished to keep a managed float, the exchange rate under this type of regime is allowed to vary within certain parameters, so adjustment would not occur quite as often and therefore fewer reserves would be necessary.

The exchange rate is said to have devaluated when the exchange rate goes up. Reserves are held to influence the exchange rate of a currency and prevent devaluations. This is done by purchasing and selling the country's own currency to affect its demand and supply; thus, helping maintain a stable value in the international markets. Essentially, more of the domestic currency is needed to buy a unit of the foreign currency. In order to counteract this devaluation of the currency, the central currency will have to buy some of its own currency in the open market. Reserves would be used to buy the domestic currency, thus depleting reserves. Most models of the real exchange rate can be categorized according to which specific relative price serves as the object of focus. If the relative price of non-tradable is the key, then the resulting models - in a small country context - have been termed "dependent economy" (Salter, 1959, and Swan, 1960). The real exchange rate plays a crucial role in models of the open economy. How should the real exchange rate be defined, how does it behave over time, and what determines it at various time horizons are all questions that have been posed over the years. They have taken on heightened importance in recent years, as the scope of international transactions has expanded and more and more economic activity is either directly or indirectly affected by economic activity in other countries [5] . The modeling of the real exchange rate determinants can be broken up into two main categories. The first category includes models of the nominal exchange rate which, by virtue of the assumption of sticky prices, become models of the real exchange rate. First and foremost among these are sticky price monetary models that incorporate exchange rate overshooting, such as Dornbusch (1976) and Frankel (1997).

In the long run, purchasing power parity holds, so that these models are only short run models. The second category includes models that focus on the determinants of the long run real exchange rate. By far dominant in this category are those that center on the relative price of non-tradables. These include the specifications based on the approaches of Balassa (1964) and Samuelson (1964) that model the relative price of nontradables as a function of sectoral productivity differentials, including Hsieh (1982), Canzoneri, Cumby and Diba (1999), and Chinn (1999, 2000). They also include those models that search more broadly and include demand side determinants of the relative price, such as DeGregorio and Wolf (1994). Engel (1999) has cast doubt upon the relevance of the relative nontradables price. He demonstrates that for the G-7 economies, the variability of the tradable components of the CPI, is comparable to the variability of the inter-country relative price of non-tradables in terms of tradables in the home country even at horizons of 15 years.

Some methodological approaches do not fall neatly into one or the other category. The analysis by Mark and Choi (1997) is one instance. They compared the usefulness of monetary and real factors in predicting exchange rate changes over long horizons, and found - surprisingly - that monetary factors had persistent effects on the real exchange rate. Using a different methodology, namely a Structural VAR model, Clarida and Gali (1995) find that monetary and demand side factors dominate in the determination of exchange rates. Also relying upon a structural (permanent-transitory) decomposition involving the real exchange rate and the current account, Lee and Chinn (2006) find that positive permanent shocks (interpreted as productivity innovations) tend to appreciate the currency and (at least for the U.S.) have an impact comparable in magnitude to those of temporary shocks [6] .

2.3 Impact of Exchange Rate Fluctuations on Trade

Baron (1976) shows how an increase in exchange rate volatility may not necessarily lead to an adverse effect on the level of trade when hedging opportunities exist. Furthermore, some authors have shown that an increase in exchange rate volatility may be beneficial for trade (De Grauwe, 1988; Franke, 1988). However, De Grauwe (1988) shows that, when exporters are sufficiently risk-averse, a positive relationship may still arise. The most commonly held belief is that greater exchange rate volatility generates uncertainty thereby increasing the level of riskiness of trading activity and this will eventually depress trade. A vast majority of economic literature, however, contains highly ambiguous and inconsistent theoretical and empirical results on this issue (Todani and Munyama, 2005, p.3).

Several theoretical studies (Ethier (1973), Clark (1973), Baron (1976), Cushman (1986), Peree and Steinherr (1989) found that an increase in exchange rate volatility will have adverse effects on the volume of international trade (Baum et al. 2006, p.3). Ethier (1973) shows that if traders were uncertain as to how the exchange rate affects their firm's revenue, the volume of trade will be reduced. Clark (1973) notes that while risk-aversion among traders, might depress the volume of a country's exports, perfect forward markets might reduce this effect.

At the same time advocates of greater exchange rate stability across the major currencies argue that a significant part of exchange rate volatility is created in the exchange rate market itself. If exchange rate fluctuations are due to non-fundamental reasons in the sense that they are primarily driven by investor psychology, there might still exists a good case for exchange rate stability. If the exchange rate breeds its own shock then it may also be a source of welfare reduction (Straub and Tchakarov, 2004, p.5).

2.4 Exchange Rate Risk

A common definition of exchange rate risk relates to the effect of unexpected exchange rate changes on the value of the firm (Madura, 1989). In particular, it is defined as the possible direct loss (as a result of an not hedged exposure) or indirect loss in the firm's cash flows, assets and liabilities, net profit and, in turn, its stock market value from an exchange rate move. To manage the exchange rate risk inherent in multinational firms' operations, a firm needs to determine the specific type of current risk exposure, the hedging strategy and the available instruments to deal with these currency risks.

The issue of exchange rate risk is common in international economics, and this is best understood and exemplified by the constant debates about the level and scope of its damaging effects. The tendency for nominal exchange rates to move so volatilely and unpredictably has been blamed for limiting gains from international trade and lowering welfare (Straub and Tchakarov, 2004, p.5).

Exchange rate risk management is an integral part in every firm's decisions about foreign currency exposure (Allayannis, Ihrig, and Weston, 2001). Currency risk hedging strategies entail eliminating or reducing this risk, and require understanding of both the ways that the exchange rate risk could affect the operations of economic agents and techniques to deal with the consequent risk implications (Barton, Shenkir, and Walker, 2002). Selecting the appropriate hedging strategy is often a daunting task due to the complexities involved in measuring accurately current risk exposure and deciding on the appropriate degree of risk exposure that ought to be covered. The need for currency risk management started to arise after the break down of the Bretton Woods system and the end of the U.S. dollar peg to gold in 1973 (Papaioannou, 2001).

The issue of currency risk management for non-financial firms is independent from their core business and is usually dealt by their corporate treasuries. Most multinational firms have also risk committees to oversee the treasury's strategy in managing the exchange rate (and interest rate) risk (Lam, 2003). This shows the importance that firms put on risk management issues and techniques. Conversely, international investors usually, but not always, manage their exchange rate risk independently from the underlying assets and/or liabilities. Since their currency exposure is related to translation risks on assets and liabilities denominated in foreign currencies, they tend to consider currencies as a separate asset class requiring a currency overlay mandate (Allen, 2003).

Multinational firms are participants in currency markets by virtue of their international operations. To measure the impact of exchange rate movements on a firm that is engaged in foreign-currency denominated transactions, i.e., the implied value-at-risk (VAR) from exchange rate moves, we need to identify the type of risks that the firm is exposed to and the amount of risk encountered (Hakala and Wystup, 2002). The three main types of exchange rate risk that we consider in this paper are (Shapiro, 1996; Madura, 1989):

Transaction risk, which is basically cash flow risk and deals with the effect of exchange rate, moves on transactional account exposure related to receivables (export contracts), payables (import contracts) or repatriation of dividends. An exchange rate change in the currency of denomination of any such contract will result in a direct transaction exchange rate risk to the firm;

Translation risk, which is basically balance sheet exchange rate risk and relates exchange rate moves to the valuation of a foreign subsidiary and, in turn, to the consolidation of a foreign subsidiary to the parent company's balance sheet. Translation risk for a foreign subsidiary is usually measured by the exposure of net assets (assets less liabilities) to potential exchange rate moves. In consolidating financial statements, the translation could be done either at the end-of-the-period exchange rate or at the average exchange rate of the period, depending on the accounting regulations affecting the parent company. Thus, while income statements are usually translated at the average exchange rate over the period, balance sheet exposures of foreign subsidiaries are often translated at the prevailing current exchange rate at the time of consolidation; and

Economic risk, which reflects basically the risk to the firm's present value of future operating cash flows from exchange rate movements. In essence, economic risk concerns the effect of exchange rate changes on revenues (domestic sales and exports) and operating expenses (cost of domestic inputs and imports). Economic risk is usually applied to the present value of future cash flow operations of a firm's parent company and foreign subsidiaries. Identification of the various types of currency risk, along with their measurement, is essential to develop a strategy for managing currency risk.

2.5 Measurement of Exchange Rate Risk

After defining the types of exchange rate risk that a firm is exposed to, a crucial aspect of a firm's exchange rate risk management decisions is the measurement of these risks. Measuring currency risk may prove difficult, at least with regards to translation and economic risk (Holton, 2003). At present, a widely-used method is the value-at-risk (VAR) model. In a general way, value at risk is defined as the maximum loss for a given exposure over a given time horizon with z% confidence. The VAR methodology can be used to measure a variety of types of risk, helping firms in their risk management. However, the VAR does not define what happens to the exposure for the (100 - z) % point of confidence, i.e., the worst case scenario. Since the VAR model does not define the maximum loss with 100% confidence, firms often set operational limits, such as nominal amounts or stop loss orders, in addition to VAR limits, to reach the highest possible coverage (Papaioannou and Gatzonas, 2002).

2.6 Exchange Rate Volatility, Trade and Exports

The relationship between exchange rate volatility and trade is well established. The basic idea is the following: if commodity traders are risk averse (or even risk neutral), higher exchange rate uncertainty may lead to a reduction in the volume of trade because they may not want to risk their expected profits from trade (Brodsky, 1984). As long as there is uncertainty, economic agents will demand a higher price to cover their exposure to currency risk, and this, in turn, will decrease the volume of trade. Now, since most of the international transactions take place in some of the G-3 currencies, increased exchange rate uncertainty among them may have an effect which is equivalent to a higher uncertainty on the bilateral exchange rate. Therefore, higher G-3 currency volatility may also lead to a lower volume of trade.

However, this is just the direct effect, and there may be other (perhaps more important) indirect effects of G-3 exchange rate volatility on trade. Suppose a country chooses to peg its exchange rate to one of the main world currencies. If there is instability among the G-3 exchange rates, rapid movements in the real exchange rate among these countries may have an indirect effect on the competitiveness of all the countries that are pegged (explicitly or implicitly) to one of the main currencies. Of course, the effect on trade of movements on G-3 parities depends on whether the anchor currency is appreciating or depreciating vis-à-vis the rest of the world [7] .

A strand of the literature has evolved which points to the impact of macroeconomic volatility on growth via innovative activities. Important contributions to the literature can be organized according to the channels through which volatility affects growth.

First, volatility affects investment. Simple one-sector AK models, as discussed by Mendoza (1997), with stochastic productivity and CES utility relate the effect of volatility on growth to the relative weight the representative consumer assigns to the income or substitution effect. Both papers show that when individuals have a sufficiently high coefficient of relative risk aversion, increased volatility will raise growth, and vice versa. In AK models with stochastic technologies in several sectors the impact on volatility on growth can be shown to depend on a set of parameters. Barlevy (2004) considers an AK model in which adjustment costs cause the investment function to be concave. He demonstrates that volatility lowers growth through the volatility of investment even for a constant volume of investment, whenever the investment function is strictly concave. Because of diminishing returns to investment, the positive effect of above average investment is smaller than the negative effect of below average investment. A similar mechanism is at work in Aghion et al. (2006). Alternative models of the influence of volatility on growth are based on human capital accumulation. Martin and Rogers (1997) present a model in which human capital is accumulated through learning by doing. Workers fail to internalize fully the positive externality of learning by doing on future wages. Therefore, their labor supply is inefficiently low and policies eliminating volatility can raise growth rates. Positive effects of fluctuations can emerge when, instead of being complementary to production activities, human capital accumulation is a substitute as in Aghion and Saint-Paul (1998).

Second, volatility may affect research expenditure. In a recent contribution Aghion, Bacchetta, Ranciere and Rogoff (2006) model the effect of exchange rate volatility in a small open economy. Assuming that credit is needed to overcome liquidity shocks which otherwise impede innovative activity implies that the probability of innovation is a linear function of the real exchange rate. As long as this probability is strictly concave in its arguments, the same mechanism employed in Barlevy (2004) ensures that lower volatility raises the expected growth rate via an increase in innovative activities. The empirical estimates show that there is actually a negative relationship between real exchange rate volatility and growth. However, the innovation channel is not estimated explicitly and the transmission from volatility to growth is treated as a black box. As discussed above, higher growth rates may be due to a positive investment effect of lower real exchange rate volatility. In addition, it is plausible to assume that real exchange rate effects on investment and innovation depend on the openness of an economy because different degrees of openness and hence, relative weight of traded to non-traded goods produced in an economy implies different sensitivity to real exchange rate shocks.

Moreover, exchange rate volatility and, therefore, uncertainty of exchange rate changes also affect economic growth via the trade channel. With increased competition among firms operating in monopolistic markets across countries, the uncertainty of exchange rates drives a wedge between the values of revenues earned by firms located in different markets (Krupp and Davidson, 1996). Hence, in the short run, stability of exchange rates is crucial to export oriented firms as they affect their profitability [8] .

Theoretical evidence concerning the impact of exchange rate stability on growth is mixed. The theoretical arguments in favor of flexible exchange rates are mainly of macroeconomic nature, as flexible exchange rates allow for an easier adjustment in response to asymmetric country specific real shocks. From a microeconomic perspective low exchange rate volatility can be associated with lower transaction costs for international trade and capital flows thereby contributing to higher growth. There are also macroeconomic benefits of fixed exchange rates as they contribute to macroeconomic stability and help to avoid "beggar-thy-neighbor" depreciations in highly integrated economic regions [9] .

2.6 Conclusion and Gap Analysis

There is no consensus in the literature on the factors affecting exchange rates and their volatility. This absence of agreement reflects basic difficulties in modeling and predicting exchange rates. Much of the existing work focuses on the levels of exchange rates (in statistical terms, the mean or first moment), but also has implications for exchange rate volatility. In the literature, three principal views have emerged:

First, at least over short time horizons and for countries without high inflation, exchange rate models that include macroeconomic fundamentals do not perform better than a random walk in out-of-sample forecasting [10] . Exchange rate volatility is simply the standard deviation of the error term.

Second, macroeconomic fundamentals play an important role in explaining the behavior of exchange rates. Some authors hold that these fundamentals are important only in the long run but have little to offer in explaining short-run movements, while others believe that macroeconomic fundamentals have explanatory power both in the long run and the short run [11] .

Third, neither macroeconomic fundamentals nor the random walk model adequately account for exchange rate behavior at short horizons. Rather, short-run exchange rate movements are attributed to market microstructure factors, including inventory management and information aggregation by foreign exchange dealers. Specifically, the microstructure approach suggests that non-dealers learn about fundamentals affecting the exchange rate, and this knowledge is reflected in the orders they place with dealers. Dealers in turn learn about fundamentals from order flow. The outcome of this two-stage learning process results in the formation of a price, (Lyons, 2001) [12] .

Many of the studies that have assessed the effects of exchange-rate uncertainty have modeled the quantity of exports or imports as a function of the importing country's income, a measure of relative price, and a proxy for volatility. The former two variables capture income and substitution effects. The relative price of competing domestic goods to traded goods is usually expressed as the trading country's real exchange rate. In this study the model which is employed to capture the export demand function may take the following form that exports or imports are a function of income of importing country, some measure of relative price and a proxy for risk [13] :

(1)

where denotes the volume of imports or exports of trading country (Canada), is a scale variable which captures importing country's economy situation and GDP will be used as this measure, is the relative prices and is measured by the Canadian and U.S. Consumer Price Indices. This study uses the (CPI-based) real exchange rate as the measure of the relative price level. is the logarithm of a moving-sample standard deviation as a proxy to measure the risk and is the disturbance term.

So Canadian models of imports and exports are as follows:

The theoretical literature concerning the effects of exchange-rate volatility on trade typically reveals no unambiguous response in the level of trade to a change in exchange-rate volatility (McKenzie, 1999; Clark et al., 2004). A conclusion that emerges from the literature is that differing analytic results can arise from differences in assumptions with regard to such factors as the degree of risk aversion of, and the availability of hedging opportunities, and/or the presence of other types of business risk to economic agents involved (or potentially involved) in international trade (Sauer and Bohara (2001) and Hondroyiannis, Swamy, Tavlas and Ulan (2008)). Consequently, the direction and extent of any relationship between exchange-rate volatility and trade is an empirical question (e.g., Sauer and Bohara, 2001, p.133). A common feature that characterizes earlier (i.e., pre-late 1990s) analytic assessments of the relationship between exchange-rate volatility and trade is that the countries under consideration were almost exclusively industrial countries. There are several potential reasons, however, that there may be differences between the relationship between short-term exchange-rate volatility and trade of industrial countries and developing countries.

Chapter 3 - Research Methodology

3.0 Introduction

In 1973, following the flattening of post-war Bretton Woods system of fixed exchange rates, the relative rates of currencies in open market started to vary. All of these movements raised uncertainness and risk for traders; and subsequently this particular uncertainty could affect the volume of global trade.

Issues which have obtained significant consideration recently stand out as the effect of exchange rate risk on the volume of trade. Numerous experts of economics agree the fact that existing floating rate of exchange provides considerable fluctuations both in the nominal as well as in the actual exchange rate. Therefore has ended in a reduction in international trade transactions.

A variety of theoretical paperwork and articles have already been written and published to describe and investigate the impact of higher exchange-rate volatility on trade as the start of the existing float, and much more are already released analysing such ideas through empirical observation. These types of studies have used different methods and of course found different results, however no general opinion has long been achieved concerning how to model, as well as how to appropriately determine, exchange-rate volatility.

3.1 Types of Research

This type of research applies research for the sector through theoretical studies and library and internet records and documents of an organization. This is a quantitative research as described further below.

3.1.1 Theoretical Methods

The quantity of exports or imports in some of such studies of which have analysed the effect of exchange-rate volatility, have been modelled as a function of the importing country's income, a measure for the relative prices and also volatility with a proxy to show it . These two previous variables are captured income and substitution effects.

The trading country's real exchange rate normally is shown by the relative price of competing domestic goods to price of foreign goods. Through this research the model will have the following form that is shown the demand function of export in which exports or alternatively imports are a function of income of importing country, a measure of relative price and a risk proxy:

(1)

where denotes the volume of imports or exports of trading country (Canada), is a scale variable which captures importing country's economy situation and GDP will be used as this measure, is the relative prices and is measured by the Canadian and U.S. Consumer Price Indices. This research follows Bahmani-Oskooee (2002) and employs the (CPI-based) real exchange rate as the measure for the relative price level. is the logarithm of a moving-sample standard deviation and is the disturbance term.

So Canadian models of imports and exports are as follows:

where denotes exports of Canada and refers to imports of Canada from US

3.1.2 Empirical Methods

The monthly time series data over period of 1980-2008 will be used in this study. The source of gathering the data for Canada is official website of "Statistics Canada" and those for USA is "U.S. Census Bureau".

Most time-series variables such as those included in above mentioned models contain one or more unit roots that make them non stationary. So each individual time series will be tested for first-order integration using augmented Dickey and Fuller (1981) and Johansen (1991) tests.

The technique of finding cointegration which is introduced by Engle and Granger (1987) is required to be used to identify if a long-run equilibrium relationship between the variables is exist in this research. The essential concept of cointegration is that two or more variables might be regarded as denoting a long-run equilibrium relationship if they move together closely in the long-run, despite of the fact that they may drift away in the short-run. This long-run is called a cointegrating vector. A regression including all the variables of a cointegrating vector will have a stationary error term, regardless of the fact that none of the variables, on its own, is stationary if there is a long-run relationship among the variables. Therefore test of cointegration is necessary to establish long-run relations.

On condition that each one of four variables in this research are non stationary and integrated of a similar order, the model will be estimated by using the multivariate cointegration methodology proposed by Johansen (1991).

The software which will be used for this study is "E.Views 6".

3.2 General and Specific Objectives

The purpose or general objective of this study is to investigate the effect of exchange-rate volatility on bilateral trade flows between Canada and United States.

The specific objectives are:

Determining the volatility of real exchange rate which is used as a proxy for exchange-rate uncertainty.

Estimate the elasticity of fluctuations of real exchange rate of CAD to USD on bilateral trade flows of Canada and USA.

3.3 Statement of Hypothesis

3.3 Desired Methods for Data Analysis and Hypothesis Testing

The way to measure the volatility of exchange rate including graphical analysis and descriptive statistics of data, seasonality checking and probably adjustment, data stationary test, correlogram, unit root tests (ADF and Phillips-Perron tests. empirical testing consisting of model estimation, significance and diagnostic tests of the model, Chow tests, Wald test and co-integration test (long run relationship) and interpretation of the model.

3.3.1 Choosing the Lag Length for the ADF Test

An important practical issue for the implementation of the ADF test is the specification of the lag length p. If p is too small then the remaining serial correlation in the errors will bias the test. If p is too large then the power of the test will suffer. Ng and Perron (1995) suggest the following data dependent lag length selection procedure that results in stable size of the test and minimal power loss. First, set an upper bound Ïmax for p. Next, estimate the ADF test regression with Ï= Ïmax. If the absolute value of the t-statistic for testing the significance of the last lagged difference is greater than 1.6 then set p = Ïmax and perform the unit root test. Otherwise, reduce the lag length by one and repeat the process.

A useful rule of thumb for determining Ïmax, suggested by Schwert (1989), is

Untitled

where [x] denotes the integer part of x. This choice allows Ïmax to grow with the sample so that the ADF test regressions:

4-3

and

4-4

are valid if the errors follow an ARMA process with unknown order.

Chapter 4 - Empirical Results

4.0 Introduction

This chapter covers the overview of the data used in this paper, as well as the analysis and the observation from the analysis results.

4.1 Data

To investigate the impact of exchange rate volatility and other economic variables on the export and import, this paper uses monthly data from the period 1980 to 2008. This data set starts in January 1980 and ends in September 2008. The exchange rate data are end-of-period exchange rates and data for exchange rate was collected and certified by the Federal Reserve Bank of New York. GDP, bilateral Export and import were collected by the IMF's International Financial Statistics, OECD's main economic indicators and WDI (World Development Index). All data are measured in constant 2000. The consumer price indices also use 2000 as base year. For summary statistics, see Table 4.1 and Figure 4.1 to Figure 4.6.

Table 4.1: Monthly Data from January 1980 to September 2008

Variables

Observations

Mean

Std. Dev.

Min

Max

GDP Canada

348

24.62826

0.237139

24.21064

25.0553

GDP USA

348

29.62750

0.339531

29.05654

30.0991

Imports

348

23.44196

0.397593

22.71524

23.9659

Exports

348

23.51100

0.412647

22.86920

24.0447

Exchange Rate

348

1.29686

0.139444

0.97610

1.5936

Relative Price

348

1.02960

0.042636

0.94180

1.1077

GDP Canada, GDP USA, M, X are on log. The actual data are in USA dollars. Mean of GDP Canada is 24.62. Min-Max is 24.21-25.05, and Std. Dev. is 0.237. Mean of GDP USA is 29.62. Min-Max is 29.05-30.09 and Std. Dev. is 0.339. Mean of imports is 23.44. Min-Max is 22.71-23.96, and Std. Dev. is 0.397. Mean of exports is 23.51. Min-Max is 22.86-24.04, and Std. Dev. is 0.412. Mean of the exchange rate is 1.29. Min-Max is 0.97-1.59, and Std. Dev. is 0.139. Mean of the relative price is 1.02.

According to Table 4.1, max of mean is 29.62(GDP USA), and min of mean is 23.44 (imports). Min of Std. Dev. is 0.23 (GDP Canada) and max of Std. Dev. is 0.41 (exports).

Figure 4.1: Canada GDP from January 1980 to September 2008

Figure 4.1 shows the increasing rate of the Canada GDP from 24.2 (1980) to 25.1 (2009). And, there is the linear growth in the Canada GDP.

Figure 4.2: USA GDP from January 1980 to September 2008

Figure 4.2 shows the increasing rate of the USA GDP from 29 (1980) to 30.1 (2009). Approximately, there is the linear growth in the USA GDP.

Figure 4.3: Canada Imports from USA from January 1980 to September 2008

Figure 4.3 shows the increasing rate of the Canada imports from 22.8 (1980) to 24 (2009). And, there is the exponential growth in the imports.

Figure 4.4: Canada Exports to USA from January 1980 to September 2008

Figure 4.4 shows the increasing rate of the Canada export from 22.8 (1980) to 24 (2009). And, there is the exponential growth in the export.

Figure 4.5: Canada Exchange Rate from January 1980 to September 2008

Figure 4.6: Canada Relative Price from January 1980 to September 2008

These graphs show that GDP of Canada and United States has been increasing in the last 29 years (1980-2008). Total bilateral exports and imports increased considerably during 1980 - 2008 as shown in Figure 4.3 and 4.4. But these variables were declined considerably in 1992 and 2002. Exchange rate has not stable trend and fall and rise in period 1980-2008. Also relative price was increased continuously to 1992 and then was decreased from 1992 to 2008.

In this step, this research applies Hodrick - Prescott Filter for Exchange Rate Volatility Estimation. This work is done using E.Views 6. We know that this filter gives us two series. First is variable's long run path and second is its volatility. For prevent of non positive amounts of volatility absolute amounts of these terms have been established.

The high-pass filter of Hodrick and Prescott (1997), which minimises the sum of squared deviations between the observed series and the unobserved low frequency component that is to be recovered, while penalising variation in the low frequency component. More formally, the HP filter estimates the non-linear trend as

where Argmin(t) is the tth observation of the series that minimises the objective function. The first term in (4) penalises deviations of the observed series from the low-frequency trend, while the second term penalises instability in the trend using its second derivative and a smoothing parameter, Î». When Î»=0, the trend is equal to the original series, while as Î» approaches infinity, it converges towards a linear trend. The HP filter therefore represents a trade-off between the goodness-of-fit of the low frequency trend component and its smoothness. While there is no 'correct' value for the smoothing parameter, Î», a common choice in the literature for macroeconomic data is 100 multiplied by the squared frequency of the data.

These figures presented as below.

Figure 4.7: Canada Exchange Rate Volatility from January 1980 to September 2008

Figure 4.8: Canada Long Run Path of Exchange Rate from January 1980 to September 2008

4.2 Unit-Root Test and Statistics (define ADF)

Paper starts by testing the hypothesis that each series contains a unit root. For this aim, augmented Dickey-Fuller test is applied that procedure by trying two different tests in which I include trend and intercept in the first test and only an intercept in the second test. Eight lags are entered for each series, and then all insignificant lags using t-statistics are eliminated. These tests are performed on the levels and on the first differences as shown in Table 4.2.

Table 4.2: Tests for Stationary

Variable

T-statistics Levels

First Difference

Lags

Classification

Augmented Dickey-Fuller Test

Test Assumptions: Intercept*

GDP Canada

-2.16

-6.248

I(1)

GDP USA

-1.052

-4.643

I(1)

Imports

-1.076

-10.445

I(1)

Exports

-0.457

-5.833

I(1)

Volatility

-4.351

-14.456

I(0)

Relative Price

-6.248

-15.237

I(0)

Test Assumptions: Intercept and Trend**

GDP Canada

-2.991

-7.290

I(1)

GDP USA

-3.994

-5.317

I(1)

Imports

-3.127

-14.138

I(1)

Exports

-1.334

-6.907

I(1)

Volatility

-7343

-13.508

I(0)

Relative Price

-8.207

-16.073

I(0)

* 5% critical value =-2.888

** 5% critical value = -3.445

H0 and H1 are as below:

H0: Variable has not a unit root

H1: Variable has a unit root

Four variables are not stationary in levels. These variables are GDP, Imports and Exports of Canada and USA's GDP. However, Volatility and Relative Price are stationary in levels. Concerning the co integration test, which requires a certain stochastic structure for each time series, paper focuses on first-order non stationary integrated process I(1) which requires the first differences to be stationary.

4.3 Empirical Results

Since four of the series are stationary in levels, it is important to decide whether paper should uses the variables in levels or in first difference. Doan, Litterman and Sims (1984) suggest that differencing all variables is not desirable when applying VAR models. This suggestion agrees with Fuller (1976), who shows that differencing the data may not produce any gain in the asymptotic efficiency of the VAR, even if it is appropriate. He also claims that differencing a variable throws information away while producing no significant gain. Therefore, this paper considers levels for all variables.

The Relationship Between Exchange Rate Volatility Finance Essay

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