Preliminary Please do not cite. The Reserve Bank of Australia as an Informed Foreign-Exchange Trader Presented at the Workshop on Central Bank Intervention Free University of Belgium Brussels, Belgium June 23, 2006 Patrick Higgins Research Department Federal Reserve Bank of Cleveland P.O. Box 6387 Cleveland, OH 44101-1387 Telephone: 216 579 2023 patrick.c.higgins@clev.frb.org Owen Humpage Research Department Federal Reserve Bank of Cleveland P.O. Box 6387 Cleveland, OH 44101-1387 Telephone: 216 579 2019 owen.f.humpage@clev.frb.org Abstract Using standard criteria, we show that Reserve Bank of Australia (RBA) foreignexchange interventions between 1983 and 2004 were successful as forecasts of day-today exchange-rate changes 57% of the time, significantly less than the 61% that we anticipated. RBA intervention had value only as a forecast that the recent movements in the exchange rate would moderate but not reverse over the day of the intervention. The amount of an intervention sometimes increases and sometimes decreases the probability of success, but the influence is generally very small. Shifting between purchases and sales, which the RBA frequently seems to do, generally reduces the probability of success. These results contrast with the results of investigations of RBA intervention that rely on various types of regression techniques. Keywords: Intervention, foreign-exchange rates, Reserve Bank of Australia. JEL classification: F3, G15. The views expressed here are those of the authors and not necessarily those of the Board of Governors of the Federal Reserve System, or the Federal Reserve Bank of Cleveland.
1 The Reserve Bank of Australia as an Informed Foreign-Exchange Trader 1. Introduction Over the past 22 years of the Australian dollar s float, the Reserve Bank of Australia (RBA) has frequently bought and sold foreign exchange (see figure 1). Many of these transactions are foreign-exchange interventions, intended to affect movements in the U.S. dollar-australian dollar exchange rate (US$/AU$). Others are customer transactions, but because the RBA can time its customer transactions to minimize or maximize their impact on the market, they seem to constitute a type of passive intervention, as described in Adams and Henderson (1983). 1 In this paper, we examine whether or not these RBA interventions had value as forecasts of day-to-day exchange-rate changes. To do so, we construct a set of success criteria that relates specific near-term exchange-rate movements with same-day official transactions. Then, following Henriksson and Merton (1981) and Merton (1981), we test if the number of observed successes significantly exceeds the amount that would randomly occur given the martingale nature of exchange-rate changes. Transactions that prove successful significantly more often than random have positive forecast value, implying that private market participants could benefit from observing and reacting to RBA operations. Similarly, interventions that are successful significantly less often than random have negative forecast value, implying ironically that private market participants might profit from taking a position opposite that of the central bank. The RBA automatically sterilizes its foreign exchange-market transactions (Rankin 1998). 2 Because sterilized interventions do not affect fundamental macroeconomic determinants of exchange rates, they do not afford the RBA a
2 mechanism for systematically influencing exchange rates independent of its monetary policy. The existing empirical research does show, however, that intervention can sometimes provoke the desired exchange-rate response. Sarno and Taylor (2001), Almenkinders (1995) and Edison (1993) survey the literature. These empirical studies generally do not specify a mechanism through which sterilized operations might affect exchange rates, but economists have offered three: the portfolio-balance channel, the inventory-adjustment mechanism and the expectations (or signaling) channel. A portfolio-balance channel relies on changes in the currency composition of outstanding asset stocks to produce an exchange-rate change, but except for Dominguez and Frankel (1993) this mechanism does not enjoy empirical support. The inventory-adjustment channel suggests that market makers will temporarily alter their quotes following large official transactions to adjust their inventories and to avoid maintaining an uncovered position for long (see Evans and Lyons 2001, Lyons 2001). The expectations channel suggests that when information is costly and asymmetrically distributed, official transactions may sometimes affect traders expectations by signaling new information to the market (Baillie, et al. 2000). Our approach is consistent with the expectations channel. We conceptualize the RBA as transacting, like any other market participant, on private information and revealing that information to the market through its trades. The RBA may have priority knowledge of impending monetary-policy changes or just an informed interpretation of current events. If the RBA routinely provides information useful for price discovery, knowledge that the RBA is in the market will cause other participants to alter their expectations about future exchange-rates. The spot rate will respond accordingly.
3 We count only 57% of RBA interventions as successful under our criteria, far fewer than the 61% that we anticipate by chance alone. With one exception, we conclude, therefore, that RBA interventions generally have negative forecast value. The one exception is that RBA transactions seem to predict that recent movements in the exchange rate will moderate but not reverse on the day of an intervention. These successes, however, amount to only 18% of the interventions. We also find that the size of an intervention has small but mixed effects under our different success criteria and that the tendency of the RBA to shift between purchases and sales of foreign exchange reduces the success rate. These results suggest that intervention does not provide the RBA with an instrument for systematically affecting spot exchange rates independent of monetary policy. Other studies have suggested that RBA intervention has limited or, at best, mixed effects on the US$/AU$. 3 Rogers and Siklos (2003) found that between 1989 and 1998, the RBA reacted to implied volatility and kurtosis (uncertainty) as derived from foreigncurrency-futures options. They find, however, that RBA interventions reduced implied volatility somewhat but had no effect on kurtosis. Edison et al. (2003) found that RBA interventions had quite modest effects on the US$/AU$ between January 1984 and December 2001. Using an event study methodology, they found that the RBA had some success at moderating the decline in the US$/AU$ between 1997 and 2001, but using a GARCH model they also found RBA interventions tended to increase overall exchangerate volatility. McKenzie (2004), interacting probit and GARCH techniques, argues that RBA intervention seems to alter apparently increase the conditional variance of the Australian dollar between December 12, 1983, and December 31, 1997. Endogeneity,
4 however, prevents an unequivocal interpretation of the relevant coefficients in this model. Noomen et al. (2003) investigate RBA intervention in a FIGARCH (long-memory) model. They get inconsistently signed contemporaneous coefficients in the conditional mean equation and positive coefficients in the conditional variance equations, but endogeneity again seems to be a serious problem. Two recent studies, which use the new RBA data (see section 2), get results that seem at odds with ours. Kearns and Rigoban (2005) investigate Australian intervention between July 1, 1986, and November 30, 1993, in a multi-equation model that they estimate using simulated general method of moments. The model controls carefully for endogeneity. They find that an RBA US$100 million purchase of Australian dollars appreciates the Australian dollar by 1.3% to 1.8% with almost all of the impact occurring on the day of intervention. Kim and Pham (forthcoming) estimate an EGARCH model, with careful attention to endogeneity, and find that large RBA intervention in particular and cumulative RBA interventions between July 1986 and December 2003 moderated US$/AU$ movements and that interventions undertaken for a number of days moderate volatility. Our paper proceeds as follows: The next section presents our success criteria, explains our timing convention, and describes our data set. Section 3 presents the counts, provides the unconditional probabilities of success and evaluates the RBA s forecast value ala Henriksson and Merton. In section 4, we develop a series of probit models and estimate the probability of success conditional on various attributes of the intervention process. Section 5 concludes, suggests why our results differ from those using regression techniques, and explains future extensions of this paper.
5 2. Success Criteria In this section, we evaluate the effectiveness of RBA interventions using two specific success criteria and an aggregate standard that incorporates the first two. We count the number of successes consistent with each criterion and, following Henriksson and Merton (1981) and Merton (1981), test whether the observed number of successes exceeds the number that could randomly occur. The tests assume that RBA interventions do not directly affect underlying exchange-rate fundamentals. As noted, the RBA sterilizes its foreign-exchange operations. In recent years, the RBA has conducted some domestic-market operations through foreign-currency swaps, but contends that these do not affect spot exchange rates (see Rankin 1998). Although Australian monetary policy focuses on domestic objectives, the RBA has, on a few occasions, adjusted monetary policy to influence the exchange rate while intervening. In July 1986 and in January 1987, the RBA tightened monetary policy in response to a decline in the exchange rate while simultaneously undertaking heavy sales of foreign exchange. 4 We check for possible interactions between monetary policy and intervention during these months in section 4. All in all, our assumption that RBA interventions have no direct effect on underlying macroeconomic fundamentals seems valid, but if these interventions did directly affect the relative supplies and demands of currencies, the influence would seem to bias our results toward a higher success count. We do not generally know what criteria the RBA uses to evaluate its interventions, and our success criteria do not encompass all possibilities. Nevertheless, the success criteria that we define below are reasonable, frequently mentioned in
6 intervention literature, consistent with interpretations of other empirical work, and readily verifiable. (With respect to Australian intervention, see especially Kim and Sheen 2002, Edison et al. 2003, Kearns and Rigobon 2005). In accordance with the Henriksson and Merton procedure, which we discuss in section 3 below, we define each success criterion for purchases and sales of foreign exchange separately. 2.1. Appreciate or depreciate (SC1). The first set of success criteria presumes that when the RBA buys or sells foreign exchange, it expects the Australian dollar to immediately depreciate or appreciate, as the case may be, against the U.S. dollar. Accordingly, our first success criterion (SC1) for official sales of foreign exchange is: 1) SC 1 (sell) 1 if I < Δ > = t 0, and St 0 otherwise. 0, and The corresponding criterion for official purchases of foreign exchange is: 2) SC1 (buy) 1 if I > Δ < = t 0, and St 0, and 0 otherwise. In all of our success criteria, I t refers to an intervention over day t, with negative and positive values indicating sales or purchases of foreign exchange, respectively. The official intervention data are in millions of Australian dollars and almost all transactions are against U.S. dollars. The official RBA data are on a net basis with dealers and include forward transactions. The median absolute value of an intervention is AU$30 million. Most transactions are purchases of foreign exchange. Figure 2 provides the frequency distribution and some basic statistics for RBA purchases and sales of foreign exchange.
7 The RBA recently revised these data. The revisions were fairly extensive, involving 10% of the observations for which we have overlapping data sets (December 12, 1983, through December 31, 2002). The revisions included adjustments to the timing and the amounts of specific transactions and adds many previously unreported transactions as well. The largest change equaled AU$241 million, which, as figure 2 shows, is quite large. The correlation between the old and new series remains very high (0.99). The RBA intervenes primarily in the Sydney market, but sometimes also transacts in other markets around the globe. Consequently, we measure Δ St = St+1 S t to bracket It in all possible markets. Here S t is the U.S. dollar price of Australian dollars at the 9 a.m. opening of the Sydney market on day t. We deleted 140 holiday observations from our data set because on these days the data included an exchange-rate quote from 4:00 p.m. on the previous day and often repeated this same quote for more than one day. We also lost five observations to variable construction, leaving a data file with 5,313 observations between December 20, 1983, and December 31, 2004. 2.4. Moderate exchange-rate movements (SC2). Empirical estimates of intervention reaction functions typically report that monetary authorities attempt to smooth exchange-rate movements (see Edison 1993, Almekinders 1995). Our second success criterion (SC2) is compatible with this behavior. We assume that the RBA takes a position in the foreign-exchange market when it expects that a recent appreciation or depreciation has proceeded too quickly, will subsequently slow, but will not reverse. Accordingly,
8 5) SC2 (sell) 1 if It < 0, and ΔS = 0 otherwise, t 0, and ΔS t > ΔS t 1 and and 6) SC2 (buy) 1 if It > 0, and ΔS = 0 otherwise. t 0, and ΔS t < ΔS t 1 and 2.5. General success criteria (SC3). Since we are never sure whether the RBA follows one or both of the previous success criteria, the following set of general success criteria is the union of the previous two: 9) SC3 (sell) 1 if It < 0, and ΔS = 0 otherwise. t > 0, or ΔS t > ΔS t 1, and 10) SC3 (buy) 1 if It > 0, and ΔS = 0 otherwise. t < 0, or ΔS t < ΔS t 1, and 2.6. Timing convention. Our timing convention considers only a single day. The chance that we might fail to count an intervention successful if the appropriate exchange-rate movement occurs beyond closing on day t seems remote. Chang and Taylor (1998), Chueng and Chinn (2001), and Dominguez (2003), among others, suggest that exchange markets begin to respond to intervention within minutes or hours, not days. Similarly, a majority of central banks in Neely s (2001) survey contended that exchange rates reflect the full effects of intervention within hours. Alternatively, we may count an intervention successful even though the exchange-rate movement that led to that conclusion subsequently disappears. This
9 occurrence is more problematic. Opening the event window, however, quickly causes overlap among interventions, making inferences about individual successes impossible. Consequently, we keep the event window narrow. Because exchange-rate changes approximate martingale processes, we interpret successful interventions as highly persistent, if not permanent, shocks even though interventions often appear to wear off in a day or two. A successful intervention will send the exchange rate on an alternate path, but one still consistent with existing and unchanged market fundamentals. Our methodology cannot answer questions about the duration of exchange-rate shocks. 3. Testing for Forecast Value Given the martingale nature of exchange-rate changes, one would expect to observe a fairly high number of intervention successes merely by chance. To have forecast value, the frequency with which a particular exchange-rate pattern and an intervention coincide a success must significantly exceed the frequency with which that exchange-rate pattern occurs irrespective of any intervention. We evaluate the probability of observing a specific number of successes under the assumption that the success counts are hypergeometric random variables. In doing so, we follow a procedure that Henriksson and Merton (1981) and Merton (1981) developed to test the forecast capabilities of private investment managers. 5 The hypergeometric distribution does not require individual events to be independent and does not depend on the presumed probability of an individual success. In addition, the moments of the hypergeometric distribution are defined in a manner that compares days of intervention against the entire sample, rather than against days of no intervention. Our null hypothesis compares actual
10 and expected successes. A low p-value indicates positive forecast value, and a very high p-value indicates negative forecast value. The results in table 1 indicate that the RBA has positive forecast value with respect to moderating movements in exchange rates, but has negative forecast value with respect to the other success criteria. The first column in table 1 lists the specific success criteria. The next column shows the total number of interventions. During our sample of 5,313 business days between December 20, 1983, and December 30, 2004, the RBA intervened on 2,191 occasions, consisting of 1,657 foreign-exchange purchases and 534 foreign-exchange sales. The third column counts the number of successes for each of the corresponding success criteria, and the fourth column expresses that count as a percentage of the total number of intervention purchases or sales. Under success criterion 1 (SC1), for example, out of 1,657 official foreign-exchange purchases, 668 or 40.3% were successful, and out of 534 official foreign exchange sales, 177 or 33.2% were successful. The two columns labeled virtual success, respectively, count the number of business days over which we observed exchange-rate movements consistent with the corresponding success criterion, whether or not the RBA intervened, and express that count as a percentage of the total number of observations. These values are important for calculating the hypergeometric distribution. The Australian dollar, for example, depreciated against the U.S. dollar on 2,513 business days, or 47.3% of the total days in our sample. A depreciation is consistent with SC1 to buy foreign exchange. Likewise, the Australian dollar appreciated on 2,738, or 51.5%, of the business days in our sample. An appreciation is consistent with SC1 to sell foreign exchange.
11 The last three columns in table 1 refer to the hypergeometric distribution. Columns seven and eight show the expected number of successes and their standard deviation, assuming that the success count is a hypergeometric random variable. In our sample, we expect to observe 783.7 successes under SC1 to buy foreign exchange with a standard deviation of 16.9 successes. Similarly, we expect to find 274.7 successes under SC1 to sell foreign exchange with a standard deviation of 11.0 successes. The last column of table 1 shows the p-value associated with observing a greater number of successes under the respective criterion than we actually observed. Under both versions of SC1, the observed number of successes falls far short of the expected number and as a consequence, the p-values approximate 1.0. This suggests that the RBA interventions have negative forecast value with respect to SC1. Market participants could have made money on average by selling foreign exchange when the RBA was buying, and buying foreign exchange when the RBA was selling. In stark contrast to the results for SC1, the RBA has strong forecast value with respect to the second specific success criterion. The number of successes under criterion SC2 (for both purchases and sales of foreign exchange) exceeds the expected number by more than two standard deviations, resulting in a p-value virtually equal to zero in both cases. RBA interventions have value as a forecast that the recent movement in the Australian dollar will moderate but not reverse over the current day. Because SC1 and SC2 are mutually exclusive, it is not at all that surprising to find opposite results when one of the two criteria is significant. What may matter more for policy analysis is the general success count, SC3. The RBA, however, also lacks forecast value with respect to SC3, which equals the union of the previous two standards. As
12 table 1 indicates SC1 dominates SC2 in the sense that we expect to observe appreciations or depreciations in the data much more often roughly four times as frequently than we expect to find a smoothing-type exchange-rate movement. 4. Predicting Success The frequencies in table 1 correspond to unconditional probabilities. As table 1 shows, the unconditional probabilities of a success under our three success criteria SC1, SC2, and SC3 are only 39%, 18%, and 57%, respectively. Success, however, may depend on specific attributes of the intervention process and environment. To test this conjecture, we run three sets of probit regressions, each using one of our success criteria as the bivariate dependent variable. We combine purchases and sales of foreign exchange into a single variable for each of three success criteria and run the regressions only on the 2,191 observations with intervention. Each probit regression includes a constant term along with a single independent variable, but we report only the coefficient on the independent variable and its t-statistic in table 2. Our eleven independent variables appear in the first column of table 2. The first four variables in the table refer to specific aspects on how the RBA conducted its intervention. We include the amount of an intervention on the assumption that larger operations affect market expectations more forcefully than smaller interventions. We also expect that infrequent operations, those with long lapses of time since the previous intervention, will have a stronger expectations effect. The RBA intervenes frequently. The median lapse time in our sample is just two days (also see figure 1). In only 31 cases is the lapse time equal to, or greater than, 15 days. The longest period, between November 13, 1993, and July 28, 1995, consisted of 429 consecutive business days
13 without intervention. In contrast, consecutive interventions may not convey new information to the market. The RBA typically has intervened in long strings of consecutive interventions. Slightly more than one-third of the interventions follow at least 5 consecutive days of interventions. Nearly 17% follow at least 10 consecutive days with intervention, and 9% follow at least 15 consecutive days of intervention. The longest string of interventions is 47 consecutive days from March 15, 1988, to May 23, 1988. A shift in the type of intervention can also affect market expectations. The RBA frequently shifted 209 times from purchases to sales and vice versa in our sample. Often the shift came within a series of same-type consecutive interventions followed soon after with a shift back. Between April 26, 1984, and May 4, 1984, for example, the RBA intervened on seven consecutive days, alternating between purchases and sales of foreign exchange on each day. Rankin (1998) defines five distinct time periods of RBA intervention. Variables 5 through 8 refer to these periods, with the last period extending up through 2004. 6 As figure 1 indicates, these periods differ in terms of the amount, frequency, direction, and incidence of shifts between purchases and sales. Consequently, the dummies for these time periods combine attributes of our first four regressors. These periods also differ in the terms of exchange-rate movements and other unobservable events. Variables 9 through 12 account for activities during the two months in which the RBA adjusted monetary policy to offset exchange-rate movements while simultaneously intervening in the foreign-exchange market. Variables 9 and 10 are straight dummy variables for the time periods. Variables 11 and 12 ask if sales of foreign exchange
14 undertaken while the RBA was tightening monetary policy increased the chances of a success. The probit results in table 2 show that the likelihood of success is sensitive to the dollar amount of interventions and shifts between purchases and sales of foreign exchange. The amount of intervention reduces the likelihood of success under SC1 and SC3, but increases the likelihood of success under SC2. Shifts between purchases and sales reduce the likelihood of success under SC1 and SC3, but have no effect on SC2. As already noted, SC1 and SC2 are mutually exclusive, and success under SC1 accounts for nearly 70% of SC3. Consequently, we might expect SC1 and SC3 to behave similarly and SC1 and SC2 often to behave oppositely. The likelihood of success also changes over the specific time periods that Rankin (1998) defined. SC1 success, for example, is less likely in the third time period (October 1, 1991, to November 30, 1993) and is more likely in the fifth time period (July 3, 1995, to December 31, 2004) than overall. Similarly, SC2 success is more likely in the third time period (October 1, 1991, to November 30, 1993) and is less likely in the fifth time period (July 3, 1995, to December 31, 2004) than overall. General success (SC3) is less likely in the first time period (December 19, 1983, to June 30, 1986) than overall. The other variables listed in table 2 do not have a significant impact on the likelihood of success. We next investigate the joint significance of the independent variables listed in table 2 and report the best combinations for each of the three success criteria in table 3. We made these determinations by running probit regressions with the most promising regressors and by watching how the likelihood-ratio tests responded as we added or
15 deleted variables. With respect to SC1 and SC3, once we include the amount of intervention and the dummy variable for shifts between intervention purchases and sales, the time period dummies had no additional explanatory power. For SC2, the best model included the amount of intervention and the third time period (October 1, 1991, to November 30, 1993). We next use the statistical models in table 3 to predict the probability of success for various combinations of conditioning factors under each success criterion. Table 4 presents our results. Generally, we find that the amount of intervention does not affect the probability of success very much and to the degree that it does, it is inconsistent across success criteria in its effects. We also find that shifting from purchases to sales greatly reduces the probability of success. The RBA, for example, has slightly better than a 41.3% probability of scoring a success under criterion SC1 if it conducts a median-sized transaction (AU$30 million) that is the same (buy or sell) as its previous intervention. This is better than the 38.6% unconditional probability of success. Increasing the amount of intervention, however, decreases the probability of success, and as the amount rises above AU$80 million, the probability of success falls below the unconditional probability of success. Shifting between purchases and sales, however, always reduces the probability of success for various transaction sizes well below 38.6%. Increasing the amount of intervention raises the probability of success under SC2, although the marginal effect is very small. Moreover, interventions outside of the third time period (October 1, 1991, to November 30, 1993) must exceed AU$100 million before the conditional probability of success rises above the unconditional probability of
16 success. Of the 2,191 interventions, only 311 or 14% were equal to, or greater than, AU$100 million. In time period three, however, the conditional probability of SC2 success was substantially greater that the unconditional probability of success for all of the intervention sizes. During the third time period, the Australian dollar was generally depreciating against the U.S. dollar, and most RBA foreign-exchange transactions consisted of foreign-exchange sales. The amount of intervention is inversely related to the probability of success under the general success criterion (SC3). For transactions consistently of one type and smaller than AU$113 million, the conditional probability of success exceeds the unconditional probability of success. If the transaction shifts between purchases and sales, the probability of success falls well below the unconditional probability of success. 5. Conclusions Since the beginning of the Australian dollar float in December 1983, the RBA has frequently transacted in foreign-exchange market. These operations consist of both active and passive types of intervention. Using three standard criteria, we counted the number of successes in our sample and tested whether that count significantly exceeded the number that we might randomly find given the martingale nature of exchange rates. A high success count would imply that the RBA has private information that is useful for price discovery and that RBA interventions successfully convey that information to the market. We find, however, that at best the RBA has only a limited expectational influence on the market. More specifically, we conclude that: Only 57% of RBA s interventions were successful according to our general criterion. Similar, studies of Swedish, Japanese, and
17 U.S. interventions using the same technique generally found that fewer than two-thirds of the interventions were successful (see Chaboud and Humpage 2005, Ragartz and Humpage 2005, and Humpage 1999, 2000). 7 RBA interventions have value only as a forecast that recent appreciations or depreciations as the case may be will proceed over the day of the intervention at a slower pace. Although only 18% of the total interventions conformed to this success criterion, the percentage exceeded the 12% that we anticipated. With respect both to the general success criterion (SC3), which conforms with many descriptions of leaning against the wind, and to a simple appreciation-or-depreciation success criterion (SC1), the success counts were always more than two standard deviations below the expected number, implying that the RBA had negative forecast value under both criteria. We find that the amount of intervention has little overall effect on the likelihood of success, but the effect differs with the various success criteria. We find that shifting between purchases and sales of foreign exchange can reduce the likelihood of overall success. These results seem inconsistent with studies using regression techniques that find a positive and statistically significant relationship between intervention and day-to-day exchange-rate movements. Our results suggest that relatively few observations drive the
18 statistically significant regression results. We are working to understand this apparent inconsistency. 6. References Adam, D. and Henderson, D. 1983. Definition and Measurement of Exchange market Intervention. Board of Governors of the Federal Reserve System, Staff Studies No. 126. Almekinders, G. J. 1995. Foreign Exchange Intervention, Theory and Evidence, Hants, United Kingdom: Edward Elgar Publishing. Andrew, R. and Broadbent, J. 1994. Reserve Bank Operations in the Foreign Exchange Market: Effectiveness and Profitability. Reserve Bank of Australia Research Discussion Paper 9406. Baillie, R., Humpage, O. and Osterberg W. 2000. Intervention from an Information Perspective. Journal of International Financial Markets, Institutions and Money, 10: 3-4. Becker, C., and Sinclair, M. 2004. Profitability of Reserve Bank Foreign Exchange Operations: Twenty Years after the Float. Reserve Bank of Australia Research Discussion Paper 2004-2006 (September). Bonser-Neal, C., Roley, V. V. and Sellon, G. H., Jr. 1998. Monetary Policy Actions, Intervention, and Exchange Rates: A Reexamination of the Empirical Relationships Using Federal Funds Rate Target Data. Journal of Business 71 (2): 147-177. Chaboud, A. and Humpage, O. 2005. An Assessment of the Impact of Japanese Foreign Exchange Interventions: 1991 2004. Board of Governors of the Federal Reserve System International Finance Discussion Papers, No. 824 (January). Chang Y. and Taylor, S. J. 1998. Intraday Effects of Foreign Exchange Intervention by the Bank of Japan. Journal of International Money and Finance. 17 (1): 191-210. Cheung, Y. and Chinn, M. D. 2001. Currency Traders and Exchange Rate Dynamics: A Survey of the U.S. Market. Journal of International Money and Finance. 20: 439-471. Dominguez, K. M. 2003. The Market Microstructure of Central Bank Intervention. Journal of International Economics, 59: 25-45.
19 Dominguez, K. M. and Frankel, J. A. 1993. Does Foreign Exchange Intervention Matter? The Portfolio Effect. American Economic Review, 83: 1356-1369. Edison, H. 1993. The Effectiveness of Central Bank Intervention: A Survey of the Literature after 1982. Princeton University, Special Papers in International Economics, No. 18. Edison, H., Cashin, P., and Liang, H. 2003. Foreign Exchange Intervention and the Australian Dollar: Has It Mattered? IMF Working Paper (May). Evans, M. and Lyons, R. 2001. Why Order Flow Explains Exchange Rates. U.C. Berkeley. Henriksson, R. D. and Merton, R. C. 1981. On Market Timing and Investment Performance. II. Statistical Procedures for Evaluating Forecasting Skills. Journal of Business 54: 513-533. Humpage, O. F. 1999. U.S. Intervention: Assessing the Probability of Success. Journal of Money Credit and Banking 31 (4):731 747. Humpage, O. F. 2000. The United States as an Informed Foreign-Exchange Speculator. Journal of International Financial Markets, Institutions, and Money 10: 287-302. Kearns, J. and Rigobon, R. 2005. Identifying the Efficacy of Central Bank Interventions: Evidence from Australia and Japan. Journal of International Economics 66: 31-48. Kim, S. and Sheen, J. 2002. The Determinants of Foreign Exchange Intervention by Central Banks: Evidence from Australia. Journal of International Money and Finance 21: 619-649. Kim, S. and Pham, C. M. D. Forthcoming. Is Foreign Exchange Intervention by Central Banks Bad News for Debt Markets?: A Case of Reserve Bank of Australia s Interventions 1986-2003. Journal of International Financial Markets, Institutions & Money. Leahy, M. P. 1995. The Profitability of U.S. Intervention in the Foreign Exchange Markets. Journal of International Money and Finance. 14 (6): 823-844. Lyons, R. 2001. The Microstructure Approach to Exchange Rates. The MIT Press: Cambridge, Mass. Mansfield, P. 1997. The Relationship Between the Trading Activities of the Reserve Bank of Australia and Movements in the Value of the Australian Dollar. International Review of Financial Analysis, June 49 61.
20 Merton, R. C. 1981. On Market Timing and Investment Performance. I. An Equilibrium Theory of Value for Market Forecasts. Journal of Business 54: 363-406. McKenzie, M. 2004. An Empirical Examination of the Relationship between Central Bank Intervention and Exchange Rate Volatility: Some Australian Evidence. Australian Economic Papers, March 59 74. McKenzie, M. 2002. The Economics of Exchange Rate Volatility Asymmetry. International Journal of Finance and Economics, July 247-260. Neely, C. J. 2001. The Practice of Central Bank Intervention: Looking under the Hood. Central Banking, XI(2): 24-37. Noomen, A. A. Amed, G. and Abdelwed, T. 2003. The Reserve Bank of Australia Intervention: Exchange Rate Volatility from a FIGARCH model. (August 21,) 1-10. Ragnartz, J. and Humpage, O. 2005. Swedish Intervention and the Krona Float, 1993-2002. Federal Reserve Bank of Cleveland Working Paper 05-14. Rankin, R. 1998. The Exchange Rate and the Reserve Bank s Role in the Foreign Exchange Market. http://www.rba.gov.au/education/exchange_rate/html. Rogers, J.M. and Siklos, P.L. 2003, Foreign Exchange Market Intervention in Two Small Open Economies: the Canadian and Australian Experience. Journal of International Money and Finance 22: 393-416. Sarno, L. and Taylor, M. 2001. Official Intervention in the Foreign Exchange Market: Is It Effective and, If So, How Does It Work? Journal of Economic Literature, 39: 839-868.
21 7. Endnotes 1 Most of these customer transactions are for the Commonwealth government. The RBA can finance its customer transactions out of its foreign-exchange reserves, which allows it to strategically time associated transactions in the market (see Rankin 1998). 2 At least since January 1990, the RBA has operated with a cash-rate target. This requires the RBA to offset any transaction including foreign-exchange operations that affects bank liquidity in a manner inconsistent with the target. 3 Kim and Sheen (2002) present an interesting model of the RBA intervention reaction function in terms of deviations from a 150-day trend, manageable volatility, interest rate differentials that might produce overshooting, and profitability of past intervention. Estimating a reaction function is not a focus of our paper. 4 Bonser-Neal, et al., (1998) and Humpage (1999) found that when U.S. monetary policy changes took place along with U.S. interventions, the change in monetary policy, not the intervention, accounted for any exchange-rate response. 5 Leahy (1995) applies this procedure to an analysis of U.S. intervention profits. Humpage (1999, 2000) investigates the success of U.S. interventions. Chaboud and Humpage (2005) consider Japanese intervention. Ragnartz and Humpage (2005) investigate Swedish interventions. 6 The RBA did not intervene during Rankin s four time period (December 1, 1993 through July 2, 1995), so we do not consider. 7 An exception is U.S. intervention against Japanese yen between February 18, 1987, and February 23, 1990, the Louvre Accord Period. Seventy-five percent of these interventions were successful under the general success criterion.
US$/AU$ 1.00 0.95 Figure 1: Reserve Bank of Australia Intervention and Daily US$/AU$ Rate at 9am in Sydney I II III IV V Millions of AU$ 1250 1000 0.90 0.85 0.80 + purchases of forex. - sales of forex. 750 500 250 0.75 0 0.70-250 0.65-500 0.60-750 0.55-1000 0.50-1250 0.45 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005-1500 Source: Reserve Bank of Australia
Number 200 180 Figure 2: Reserve Bank of Australia Interventions; Basic Statistics and Histogram Purchases of forex. Sales of forex 160 140 120 100 Mean: 56 Median: 30 High: 1256 Low: 1 Total Purchases: 1657 Total Sales: 535 80 60 40 20 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 150 >150 Millions of AU$ Source: Reserve Bank of Australia
TABLE 1: SUCCESS COUNTS December 20, 1983, to December, 30, 2004, 5313 observations Hypergeometric Distribution Interventions Expected Standard Total Successes Virtual Successes Successes Deviation p-value Success Criteria: # # % # % # # Apprec./Deprec.: SC1 buy forex: 1657 668 40.3 2513 47.3 783.7 16.9 1.000 SC1 sell forex: 534 177 33.2 2738 51.5 274.7 11.0 1.000 SC1 total: 2191 845 38.6 Moderate Movements: SC2 buy forex: 1657 285 17.2 686 12.9 213.9 11.3 0.000 SC2 sell forex: 534 110 20.6 564 10.6 56.6 6.8 0.000 SC2 total: 2191 395 18.0 General: SC3 buy forex: 1657 953 57.5 3199 60.2 997.7 16.5 0.996 SC3 sell forex: 534 287 53.8 3302 62.1 331.3 10.6 1.000 SC3 total: 2191 1240 56.6
TABLE 2: INDIVIDUAL DETERMINANTS OF THE LIKELIHOOD OF SUCCESS Bivariate dependent variable: SC1 SC2 SC3 Unconditional probablity of success: 38.6% 18.0% 56.6% Independent Variables Coefficient Coefficient Coefficient Constant only t-statistic t-statistic t-statistic 1 amount of intervention (abs. value) -0.001 0.001-0.001-3.98 2.42-1.95 2 time since last intervention (days) -0.004 0.002-0.001-1.21 0.93-0.48 3 consecutive interventions (days) 0.007-0.006 0.003 1.65-1.22 0.66 4 shift buy to sell or sell to buy (dummy) -0.361-0.076-0.377-3.71-0.70-4.12 5 12/19/1983 to 6/30/1986 (dummy) -0.144-0.020-0.150-1.86-0.22-1.98 6 7/1/1986 to 9/30/1991 (dummy) -0.001 0.034 0.022-0.01 0.54 0.41 7 10/1/1991 to 11/30/1993 (dummy) -0.278 0.364 0.018-2.34 3.01 0.16 8 7/3/1995 to 12/31/2004 (dummy) 0.139-0.125 0.053 2.49-1.92 0.96 9 July 1986 (dummy) -0.426 0.489 0.014-1.41 1.72 0.05 10 January 1987 (dummy) 0.111-0.154 0.014 0.33-0.37 0.04 11 sales in July 1986 (dummy) -0.387 0.394-0.041-1.27 1.33-0.14 12 sales in January 1987 (dummy) 0.407 * -0.052 1.07-0.14 Notes: Sample: 2191 interventions between December 19, 1983, and December 31, 2004 Probits included a constant term. RBA interventions are in AU$-millions. * No interventions sales correspond with SC2.
TABLE 3: MULTIVARIATE MODELS OF THE LIKELIHOOD OF SUCCESS Bivariate dependent variable: SC1 SC2 SC3 Unconditional probablity of success: 38.6% 18.0% 56.6% Independent Variables: Coefficient Coefficient Coefficient t-statistic t-statistic t-statistic Intercept -0.174-0.969 0.242-5.07-26.46 7.310 1 amount of intervention (abs. value) -0.002 0.001-0.001-4.30 1.79-2.327 2 shift buy to sell or sell to buy (dummy) -0.401-0.397-4.10-4.309 3 10/1/1991 to 11/30/1993 (dummy) 0.314 2.531 Log Likelihood -1443.30-1027.80-1488.32 Likelihood Ratio Test 35.20 11.92 22.51 Degrees of Freedom 2 2 2 Critical chi square at 95% 5.99 5.99 5.99
Table 4 Estimated Probabilities of Success (Percentages) Success Criterion, SC1 Intervention Amounts ($A-millions): 30 56.1 80 150 1256 shift buy to sell or sell to buy (dummy = 0): 41.3 39.8 38.4 34.5 1.9 shift buy to sell or sell to buy (dummy = 1): 26.8 25.5 24.3 21.1 0.7 Unconditional Probability of success: 38.6 Success Criterion, SC2 Intervention Amounts ($A-millions): 30 56.1 80 150 1256 10/1/1991 to 11/30/1993 (dummy = 0): 17.1 17.4 17.8 18.8 39.6 10/1/1991 to 11/30/1993 (dummy = 1): 26.2 26.7 27.1 28.4 52.0 Unconditional Probability of success: 18.0 Success Criterion, SC3 Intervention Amounts ($A-millions): 30 56.1 80 150 1256 shift buy to sell or sell to buy (dummy = 0): 58.8 58.1 57.5 55.6 27.2 shift buy to sell or sell to buy (dummy = 1): 43.1 42.4 41.7 39.9 15.8 Unconditional Probability of success: 56.6