تجزیه و تحلیل فنی و مداخله بانک مرکزی
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|23033||2001||22 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Journal of International Money and Finance, Volume 20, Issue 7, December 2001, Pages 949–970
This paper extends genetic programming techniques to show that US foreign exchange intervention information improves technical trading rules' profitability for two of four exchange rates over part of the out-of-sample period. Rules trade contrary to intervention and are unusually profitable on days prior to intervention, indicating that intervention is intended to halt predictable trends. Intervention seems to be more successful in checking such trends in the out-of-sample (1981–98) period than in the in-sample (1975–80) period. Any improvement in performance results from more precise estimation of the relationship between current and past exchange rates, rather than from information about contemporaneous intervention.
There is now a considerable amount of evidence to suggest that technical trading rules can earn economically significant excess returns in the foreign exchange market (Dooley and Shafer, 1984, Levich and Thomas, 1993, Neely et al., 1997, Neely and Weller, 1999 and Sweeney, 1986). However, the reasons for the existence of these excess returns are still not well understood. One possible explanation is that the intervention activities of central banks in the market may account for at least part of the profitability of technical trading rules (Dooley and Shafer, 1984, LeBaron, 1999, Szakmary and Mathur, 1997 and Neely, 1998). The arguments advanced in favor of this hypothesis focus on the fact that central banks are not profit maximizers, but have other objectives that may make them willing to take losses on their trading. Thus, the stated goal of intervention by the Federal Reserve is to maintain orderly market conditions, and the unstated goals may include the achievement of macroeconomic objectives such as price stability or full employment.1 If the target for the exchange rate implied by these goals is inconsistent with the market's expectations of future movements in the exchange rate, there may be an opportunity for speculators to profit from the short-run fluctuations introduced (Bhattacharya and Weller, 1997). LeBaron (1999) investigated the relationship between intervention by the Federal Reserve and returns to a simple moving average trading rule. He used daily intervention data to show that most excess returns were generated on the day before intervention occurred. He found that removing returns on the days prior to US intervention reduced the trading rule excess returns to insignificance.2Szakmary and Mathur (1997) examined the link between monthly trading rule returns and monthly changes in the foreign exchange reserves — a proxy for intervention — of five central banks. They also found evidence of an association between intervention activity and trading rule returns. The fact that trading rule returns were abnormally high on the day before intervention tends to support the hypothesis that strong and predictable trends in the foreign exchange market cause intervention, rather than that intervention generates profits for technical traders. But it still leaves open the possibility that a sophisticated technical trader might be able to respond to the fact that intervention had occurred to modify his position and increase his profits. If this is the case, then observing intervention carries additional useful information about the future path of the exchange rate that is not contained in current and past rates. Although intervention by the Federal Reserve is not publicly announced at the time it occurs, there is evidence that foreign exchange traders quickly become aware of it.3 Thus we are interested in determining whether knowledge of central bank intervention can increase excess returns to trading rules in dollar exchange rate markets. We investigate this question using the methodology developed in Neely et al. (1997). This allows us to identify optimal ex ante trading rules that use information about whether intervention has occurred, and to compare their profitability to that of rules obtained without the use of such information. We find substantial differences between different time periods, suggesting that either the policies determining intervention or its effects on the market have not been stable over time. We also find some evidence that the use of in-sample intervention data improves the out-of-sample profitability of the trading rules for two currencies, the British pound and Swiss franc, over the period 1981–92. However, we show that this is a consequence of more precise estimation of the relationship between past and future exchange rates. We find no evidence for any currency to suggest that trading profits can be improved out of sample by using rules that condition on contemporaneous intervention information.
نتیجه گیری انگلیسی
The profitability of a trading rule is closely related to the predictability of the exchange rate one period ahead. However, it is important to recognize the differences between this investigation and one that uses standard statistical procedures to address the issue of predictability. The application of Granger causality tests to the data (not reported) provides strong evidence for all currencies except the JPY that returns and squared returns help predict intervention and also lends support to the hypothesis that intervention causes returns. These conclusions are based on the results from running two-variable vector autoregressions, including past exchange rates and magnitude of intervention. However, this evidence of Granger causality does not necessarily imply that a trading strategy that conditions on intervention will be more profitable than one that does not. There are several reasons for this. First, the linear predictive power attributed to intervention by the Granger causality tests may also be present as a non-linear component in the past exchange rate return series. The genetic program may already have incorporated the information into the trading rules trained only on exchange rate data. Second, the Granger causality tests use the magnitude of intervention, while we provide the genetic program only with information about the sign of intervention. Third, the linear relationship may not be economically significant; transactions costs may eliminate any excess returns. The volatility associated with exchange rate returns cautions us to be circumspect in drawing conclusions about the value of intervention information as an input to trading rules. Given this caveat, however, the weight of the evidence suggests that training with intervention information improved the performance of the GBP and CHF rules over the period 1981–92, although there is no such evidence for the major intervention currencies, DEM and JPY. For both DEM and JPY, providing intervention information led to some deterioration in performance for the median portfolio rule during the out-of-sample period 1981–92. This may be explained by the fact that intervention policy changed in some significant ways between in-sample and out-of-sample periods. For example, the DEM was by far the most used intervention currency in the 1975–80 period (Table 4) but the JPY was used nearly as often in the 1981–98 period. In addition, interventions were much more frequent, but much smaller in the former period. Experiments with null and simulated intervention (Table 7) show that the improved performance of the GBP and CHF rules comes about not because the intervention signal itself has predictive power out of sample, but because training rules with intervention data better identify the predictive component in the past exchange rate series. This suggests that the predictive relationship between past and future exchange rates has been more stable than the relationship between intervention and the exchange rate. Because we find no evidence for any currency that contemporaneous information about the occurrence of intervention improves trading rule performance, our findings do not support the view that intervention activity is a source of profit for technical traders in the foreign exchange market. On the contrary, our results indicate that the profitability of technical trading rules is a consequence of strong and persistent trends in exchange rates, which intervention is intended to reverse.