بانک بزرگ، بانک کوچک: اجرای سیاست های پولی و استراتژی های مدیریت ذخیره بانک ها
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|27352||2011||23 صفحه PDF||سفارش دهید||12584 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Journal of Economics and Business, Volume 63, Issue 4, July–August 2011, Pages 306–328
This paper provides estimates of banks’ demand for excess reserve balances on a period average basis for the period from 2005 to mid-2008. Consistent with theoretical work, we find that the demand for excess depends critically on uncertainty of flows in and out of reserve accounts. We also document the variability of demand for excess reserve balances by institution size, evaluate different models for forecasting demand for excess on a period average basis, and report the forecasting performance of each of these models.
Traditionally, managing the supply of reserve balances has played a critical role in the implementation of U.S. monetary policy. The supply of excess reserve balances exploded in the fall of 2008 and has remained exceptionally high relative to historical levels. As the Federal Reserve considers its “exit strategy” and new tools to drain reserve balances, it is instructive to examine closely the demand for excess reserve balances in the U.S. before the financial crisis. This paper reviews and formalizes one of the tools used to guide the provision of reserve balances over the course of the maintenance period during normal times. While this tool was surely not the only one used to forecast demand, this paper shows that there existed some consistencies in the average demand for reserve balances over the maintenance period that are in line with theoretical models.1 During the period examined in this paper, the Federal Reserve implemented monetary policy by conducting overnight open market operations to align the supply of balances held by depository institutions (DIs) at Federal Reserve Banks with demand for those balances so that federal funds would trade around the target rate set by the Federal Open Market Committee (FOMC). Aside from open market operations, the supply of balances is determined largely by so-called autonomous factors, including the Treasury’s account balance at the Federal Reserve, currency in circulation, and check float. In the period studied, the demand for balances varied significantly from day to day. Staff at the open market operations desk at the Federal Reserve Bank of New York (the Desk) and at the Federal Reserve Board (the Board) estimated the expected demand for balances at the policy target each day. The daily estimates took into account market conditions, including expectations of high payment flows, the day of the two-week maintenance period, and information gathered from discussions with banks and brokers. However, although the Desk’s and DIs’ reserve management decisions were made on a daily basis, they were within the context of an operating framework based on a two-week reserve maintenance period. In particular, while DIs must maintain a positive account balance each day, they must also meet their requirements on average over the course of the two-week maintenance period. As a result, the Desk and DIs paid attention to period average levels of reserves as well. Most of the analysis of the demand for reserve balances was focused on the demand for excess balances, which are balances held over and above reserve balance requirements. In order to put the demand for excess reserves within a maintenance period construct, Board and Desk staff focused on the daily average demand for reserve balances over the maintenance period, or period average excess (PAE). This is simply the total amount of excess reserve balances held by DIs over the maintenance period divided by the number of days in the maintenance period.2 This calculation was important because, although reserve balances are calculated and selected by DIs on a daily basis, reserve requirements are imposed on a maintenance period basis. That is, a DI can hold reserve balances below its requirements on a daily basis, but its average reserve balance holdings over the maintenance must meet its required reserve level. Over the period of analysis in this paper, some DIs would need to make substantial adjustments to their daily reserve balance holdings over the last few days of the maintenance period in order to meet their reserve requirements. The adjustments could and did often have a significant impact on daily reserve demand. As a result, a useful tool for estimating daily reserve demand late in the maintenance period was the analysis of average reserve balances over the maintenance period relative to reserve requirements, or period average excess (PAE). In general, PAE ranged from about $1 billion to $2 billion.3 Over the two years from August 3, 2005 to August 5, 2007, PAE averaged $1.7 billion but varied substantially, ranging from $1.2 billion to $2.4 billion. Over the year of market turmoil that began in earnest in August, 2007, PAE averaged $1.8 billion, ranging from $800 million to $3.1 billion.4 There is a long and extensive theoretical literature discussing reserves management and the demand for excess reserve balances. Poole (1968), who developed one of the early models of reserve management, showed that much of the demand for balances was related to uncertainty in the level of balances held in Federal Reserve accounts. Other researchers, including Clouse and Dow, 1999 and Clouse and Dow, 2002 and Dow (2001), extended these ideas, and showed that the cost of discount window borrowing and the carry-over provision, or the ability to apply small reserve deficiencies or excesses to the following maintenance period, also were key drivers of reserve demand. Fewer papers explore the empirical evidence of demand for excess reserve balances, although there are several notable exceptions. Dyl and Hoffmeister (1985) reviewed the pattern of federal funds rates and volatility over the 1970s and early 1980s and noted increased volatility on the final day of the maintenance period. They linked this volatility to changes in reserves operating procedure and the resulting changes in the pattern for the demand for reserves. Evanoff (1990) provided estimates using bank-level data from the Seventh Federal Reserve District that suggest that banks respond to various institutional features of reserve management in ways that one would expect from theory. More recently, Bartolini, Bertola, and Prati (2001) empirically documented some of the salient features of reserve demand presented in this paper; however, their focus is on developing a theoretical model that explains the stylized facts, rather than developing a simpler model and estimating parameters of a function describing the demand for excess balances. This paper explores the demand for period average excess reserve balances using data from 2004 to 2008. It also presents a theoretical model consistent with the empirical results in Appendix A. Our analysis differs from previous research in two major ways. First, we find that it is crucial to take into account institution size and charter type when formulating period average excess forecasts. For example, large DIs managed their excess reserve balances very carefully and held minimal excess balances. In contrast, small DIs generally held a buffer stock of excess that varied relatively little either within the maintenance period or across maintenance periods. Earlier studies of reserve demand either ignore, or, due to data limitations, do not consider, demand for reserve balances by institution size or type. There are important implications of this empirical result as policymakers consider the potential impact of new or altered policies because larger DIs may react differently to policy changes than smaller ones do. In addition, the theoretical model shows how differences in payment flows across days of the maintenance period are critical for forecasting period average excess, and the empirical results support these conclusions. These differences in payment flows can then be linked back to large and small banks, where the former may have more volatile payment flows than the latter. Second, the data discussed in this paper and used in the estimates we present are some of the real-time inputs that have been used by Board staff in formulating their forecasts for the demand for excess balances at the Federal Reserve. In practice, the parameters were re-estimated through time and the results were only one of the tools used for analysis supporting open market operations. Still, reviewing this material is important, and while the data used here are proprietary, we believe these results will inform researchers outside of central banks of the important factors for implementing monetary policy, which we hope will stimulate more research in this area. Before reviewing the results, it is important to note that we forecast PAE at three points in the 14-day maintenance period: before its start (ex-ante), a little more than halfway through (day 9), and the morning of the 12th day (day 12). We choose these mid-period days rather than updating our forecast on each day of the period for two reasons. First, specification tests indicate that a model based on day 13 data is nearly indistinguishable from a day 12 model. Second, lockins, which occur when a DI has satisfied its reserve requirement before the end of the maintenance period, are a key variable. However, lockins are relatively rare and volatile prior to day 9, and so we limit our forecasting to the latter part of the maintenance period. Our results are as follows. Using a two-year sample that covers the period prior to the beginning of the market turmoil in August 2007, we find that the ex-ante method with the lowest variance has a mean absolute error of about $160 million. Our day 9 forecast, with a mean absolute error of about $120 million, is an improvement over the ex-ante forecast, due to incoming information. The day 12 forecast has the best performance overall, with a mean absolute error of just $80 million, considerably smaller than other forecast rules. When our models are extended to include the year from August 2007 to September 2008, the forecast errors are larger, consistent with the increased uncertainty and volatility of this period: Our ex-ante forecast has a mean absolute error of about $180 million; our mid-period forecast error is $140 million, and our late-period forecast error is $110 million. It should be noted at the outset that we do not use rates in our empirical models; our formulations are in terms of quantities of reserve balances.5 However, over the earlier part of the sample, funds traded slightly less than one basis point above the target on average, with an absolute average deviation from the target of about View the MathML source312 basis points. Over the market turmoil period, funds traded slightly less than 3 basis points above the target on average, with an absolute average deviation from the target of about 9 basis points. These observations provide support for our (implicit) assumption that our estimates are conditional on an effective funds rate at the target. As a result, the implicit assumption of funds trading at the target seems reasonable, and the model does a fairly good job at forecasting period average excess balances under the regime that was in place at the time.6 The remainder of the paper proceeds as follows. Section 2 reviews background on the supply and demand framework for reserve balances, a closer look at the demand for reserve balances, and the data used in this paper. Section 3 discusses our analytical framework and reviews our estimation results for the three forecasts. Section 4 discusses comparative forecast performance. Section 5 concludes. Appendix A presents a theoretical analysis of the demand for fed balances.
نتیجه گیری انگلیسی
This paper provides new estimates of banks’ demand for excess reserve balances on a period average basis for the period from August 2005 to September 2008. We find that the demand for excess depended critically on uncertainty of flows in and out of reserve accounts, as evidenced by the significance of the number of high payment flow days within a reserve maintenance period. We also document facts concerning the demand for excess during this period and how it differed according to the size of an institution, as well as evaluate different models for forecasting demand for excess on a period average basis and reporting the forecasting performance of each of these models. Although the reserves environment changed dramatically in late 2008, it remains useful to understand bank behavior during the earlier period. Moreover, implementation of tools to drain reserve balances such as reverse repurchase agreements and term deposit facilities that are now under consideration may once again make the analysis of period average excess demand for reserve balances important for effective monetary policy implementation, although it is likely that the payment of interest on reserve balances may cause the behavior of DIs to change somewhat.29