روابط پویا میان نوسانات بازده، عدم تعادل تجاری، و حجم معاملات در بازار آتی
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|15881||2008||8 صفحه PDF||سفارش دهید|
نسخه انگلیسی مقاله همین الان قابل دانلود است.
هزینه ترجمه مقاله بر اساس تعداد کلمات مقاله انگلیسی محاسبه می شود.
این مقاله تقریباً شامل 4281 کلمه می باشد.
هزینه ترجمه مقاله توسط مترجمان با تجربه، طبق جدول زیر محاسبه می شود:
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Mathematics and Computers in Simulation, Volume 79, Issue 3, 1 December 2008, Pages 429–436
Trading imbalances reflect the quality of market information and may contain more information than the number of trades or trading volume. In order to better understand how trading imbalances play a role different from traditional variables (i.e., number of trades and trading volume) in explaining volatility, we use intraday data to examine the dynamic relations among return volatility, trading imbalances, and traditional variables for E-mini S&P 500 futures and Japanese Yen futures contracts, respectively. The Granger-causality tests indicate strong feedback effects between volatility and trading variables, confirming the information-based and hedging-based trading. We also compare the results of the traditional volumes and trading imbalances through variance decomposition and impulse responses analysis. It is shown that the sequential arrival of private information through trading imbalance is more important in explaining return volatility than the traditional variables, which are a proxy for the public information.
New information releases usually prompt heavy trading and volatility shocks. Many studies, including Clark , Tauchen and Pitts , Jones et al. , Andersen , Xu et al. , and Ludvigson and Ng , have examined the volatility–volume relation that is related to the information-based hypothesis. Since trade occurs at the time of public information releases, the number of trades or the trading volume used in the literature in the analysis of volume–volatility relation can be viewed as a proxy for the public information. In an asymmetric information environment, informed traders conduct trades that are closely related to private information. If more informed traders are confident of the information they possess, their orders will cluster on one side of trading and will cause a greater trading imbalance, thereby inducing a drastic change in asset prices. Trade imbalances reflect the quality of information, which is private, and hence affects the pricing dynamics. That is, trading imbalance, which can be used as a proxy for private information incorporating net of buy and seller orders, may be different from the number of trades or the trading volume, a proxy for the public information on asset pricing. The analysis of the relations between trading imbalance and volatility is limited in the literature, with the exception of Chan and Fong  and Wu and Xu .3 In this paper, we use a vector autoregressive (VAR) model to conduct Granger causality tests on the relations between asset volatility and different trading variables for the E-mini S&P 500 futures and Japanese Yen futures markets. Particularly, we examine whether futures trading is driven by information-based or by hedging-based trading. We also use the variance decomposition and impulse response functions to shed light on the speed of information adjustment in futures markets. The paper is of interest for several reasons. First, unlike previous studies which often examine volatility–volume relation through trading volume or the number of trades (Sarwar , Fung and Patterson  and , Jones et al. , and Darrat et al. ), we include another important factor, trading imbalance, to examine how trading imbalance plays a role in volatility behavior together with volume. This analysis helps us understand better the volume–volatility relation. Second, we analyze the futures market instead of the spot market because traders (especially informed traders) may be more attracted to the futures markets than the spot markets because of their low transaction costs and fast price discovery. In particular, the minute-by-minute data of the futures markets may capture releases of more private information through the trading imbalance variables than the daily spot data, and thereby allows us an examination of the speed of adjustment for shocks in the futures markets. Third, most studies examining volatility–volume relations are based on information-based trading, presuming the unidirectional link from trades to return volatility. Different from the spot market, futures market not only provides speculative (information) function for investors, it also has a hedging function. That is, greater volatility in the futures market (reflecting the greater price uncertainty of the cash market) triggers an increased use of futures contracts or more futures trading. This result would support the hypothesis of the hedging-based trading hypothesis because traders use the futures market to hedge the spot market volatility. On the other hand, if traders base information to trade in the futures markets and the volatility increases as a result of the trading activity, this relation supports the hypothesis of information-based trading. Granger-causality tests on the volatility-trade relations would enable us to understand better the hedging-based and information-based hypotheses in the futures markets. The paper is organized as follows. In the next section, we describe the data and methodologies. Details of our proposed trading imbalance metrics and the empirical VAR models (including Granger-causality test, variance decomposition and impulse response analysis) are explained. Empirical results are presented and discussed in Section 3. Section 4 summarizes and concludes the paper.
نتیجه گیری انگلیسی
Unlike previous studies which have often examined volatility–volume relations through the trading volume or the number of trades, we used the trading imbalance, which is a proxy for private information incorporating net of buy and seller orders, to examine the volatility–volume relation. We examined the dynamic relations among return volatility, trading imbalances (in the tick number and the tick volume metrics), and the traditional variables (the number of trades and the trading volume) for the intraday E-mini S&P 500 futures and Japanese Yen futures contracts. We used 5-minute futures transactions data for the analysis. The much longer time span used in this study (five years for the Yen futures and seven years for the S&P futures) compared with previous studies makes our results more robust and reliable. We used the Granger causality tests, variance decomposition, and impulse response functions to examine the volume–volatility relations. The results indicate strong feedback effects between volatility and trading variables in the Granger causality tests. The result indicates that E-mini S&P 500 futures and JapaneseYen futures support the hypotheses for the information-based and hedging-based trading. The clear lead–lag pattern also implies that sequential arrival of information hypothesis is more relevant to explain the volatility–volume relation. Comparing trading imbalances and traditional variables in explaining volatility, we found that the trading imbalance has a higher correlation with volatility and explains the error variance of volatility more than the traditional variables in the variance decomposition analysis. This paper also documented that volatility and trading imbalance adjust shocks rapidly, often less than two lags (i.e., 10 min), but the traditional trading variables after shocks converge relatively more slowly. Using intraday data of the futures market to examine the volatility–volume relations is more appropriate and meaningful because some information impact may have disappeared if daily data are used. Overall, an important result of this paper is that the trading imbalance plays a more important role in explaining volatility than the volume variable or the number of trades. Thus, future research should do well not to ignore this important factor in examining the volatility–volume relation.