ساختار بازار و ریزساختار، در بازار آتی نرخ بهره بین المللی
|تعداد صفحات مقاله انگلیسی
|14 صفحه PDF
نسخه انگلیسی مقاله همین الان قابل دانلود است.
هزینه ترجمه مقاله بر اساس تعداد کلمات مقاله انگلیسی محاسبه می شود.
این مقاله تقریباً شامل 7752 کلمه می باشد.
هزینه ترجمه مقاله توسط مترجمان با تجربه، طبق جدول زیر محاسبه می شود:
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Research in International Business and Finance, Volume 24, Issue 3, September 2010, Pages 253–266
We examine the role of market structure in identifying microstructure features of the NYSE.Euronext-LIFFE STIR futures market by comparing the ability of two bid–ask spread component models to explain bid–ask spreads. These two models differ only in their assumptions about whether or not market makers are present. The period we analyze includes data from pit-based trading alongside electronic market data. We explore how market structure affects the way private information influences bid–ask spreads and return volatility. A second part of our study employs intraday correlation to investigate these links in greater depth, while a third part looks at how private information and trading noise contribute to price evolution.
Mainstream market microstructure models have long assumed that informed traders are the initiating party in a trade. However, a number of recent studies dispute this assumption. Oliven and Rietz (2004), Bloomfield et al. (2005), Goettler et al. (2006) and Kaniel and Liu (2006) all conclude that, given the choice, informed traders prefer to play the passive role of liquidity provider. The microstructure dynamics cannot be the same in that situation as they are when informed traders are the initiating party. This leads us to argue that understanding a market's structure is critical to understanding its microstructure. We compare an empirical bid–ask spread component model, which is consistent with the initiating informed trader assumption, with an alternative model which is built around the assumption of an informed liquidity provider. We also explore an empirical model which explores how private information influences bid–ask spreads and return volatility under different market structures. We explore this issue in the context of one of the world's largest and most heavily traded financial instruments, namely, short term interest rate (STIR) futures contracts. Our data come from the world's largest financial derivatives exchange, NYSE.Euronext-LIFFE. BIS (2000) lists total annual turnover (notional amounts) in STIR futures on LIFFE for the years 1997, 1998 and 1999 as $223.4tr, $241.4tr and $213.5tr, respectively, compared with a total domestic 1997 turnover of $7tr for the world's largest equity market, the NYSE. The STIR futures market is an important inter-bank market where banks trade to secure future interest rates for international deposits and loans. We concentrate on the 4 most liquid STIR futures contracts (Euromark, Euribor, Euroswiss and Short Sterling) between 1997 and 2000, which made up well over 90% of NYSE.Euronext-LIFFE STIR futures trading volume at that time. We use empirical models from Huang and Stoll (1997) and from McGroarty et al., 2007 and McGroarty et al., 2009. Huang and Stoll (1997) examine the composition of bid–ask spreads among NYSE specialists who are deemed to provide liquidity to informed and uninformed traders, i.e. it is assumed to conform to the conventional microstructure model. McGroarty et al. (2007) is also a bid–ask spread decomposition model but this is set in the order-driven, electronic, inter-dealer foreign exchange (FX) market, where informed traders are assumed to prefer to provide liquidity. A comparison of the results of these two models constitutes a test of market structure. Our third model, McGroarty et al. (2009), also analyzes the order-driven, electronic, inter-dealer FX market. It examines how private information and temporary buy–sell volume imbalances separately influence volatility and the bid–ask spread. Different market structures lead us to expect different relationships between these variables. Our analysis contributes to the existing literature in three ways. First, we characterize the structure of the STIR futures market, in its various states. Second, we present the components of STIR futures bid–ask spreads produced by the alternative approaches. Third, our identification of the sources of STIR futures price volatility is another new contribution.
نتیجه گیری انگلیسی
Our findings constitute compelling evidence in support of our assertion that correctly identifying the market structure is crucial to understanding the microstructure of the NYSE.Euronext-LIFFE STIR futures market. The essential differentiator for this market is not whether trades take place in a trading pit or remotely via computers, but rather whether market makers are present or not. Market makers are not a feature of either the trading pit STIR futures market or its electronic successor. Under open outcry, intermediaries called scalpers, broker deals between buyers and sellers, but they do not risk substantial amounts of capital in holding inventory for long periods and they are not obliged to quote simultaneous two-way prices. This means that scalpers are not exposed to the adverse selection risk that market makers are. Rather, scalpers’ risk arises primarily from deliberate speculation. Comparison of 2 alternative trade indicator models showed that only the model appropriate for a market without market makers can produce plausible results. Using the McGroarty et al. (2007) model, we reveal the components of NYSE.Euronext-LIFFE STIR futures bid–ask spreads. In 11 out of 12 cases, this model shows that private information is more important bid–ask spread component than the random buy–sell volume imbalance generated by noise trading. The trade indicator model can also be interpreted as the elemental components of return volatility. At the tick frequency level, the bounce of successive trades between bid side and ask side makes return volatility equivalent to the bid–ask spread. However, by aggregating our data to 5min intervals, we are able to distinguish clearly between the drivers of bid–ask spreads and of volatility. We identify the separate contributions that private information and random trading imbalances make to bid–ask spreads and to volatility. Our analysis showed that bid–ask spreads are influenced by the normal pattern of uninformed intraday trading but not by private information. This is consistent with market-maker-less markets where bid–ask spreads do not have an adverse selection risk component. On the other hand, we show that volatility is jointly determined by private information and by what Goettler et al. (2006) describe as “microstructure noise”. Some volatility, which is evident at high frequencies, and which arises from uninformed random imbalances can be attributed to market structure and can be thought of as excess volatility compared with what would arise under an alternative market structure. Market makers suppress random imbalance volatility by shading post-trade quoted prices in the opposite direction to the preceding trade, resulting in higher bid–ask spreads where market makers are present.