本文關鍵詞：超高頻期貨市場微觀結構噪音實證研究　出處：《西南財經大學》2013年碩士論文　論文類型：學位論文

  更多相關文章： 期貨市場 微觀結構 微觀結構噪音 超高頻數據 已實現波動率(RV) 二尺度已實現波動率(TSRV)

【摘要】：期貨市場是金融市場和國民經濟的重要組成部分,具有價格發(fā)現和套期保值兩大重要功能。不僅對股票市場、外匯市場等其他虛擬經濟有巨大的影響,也與現貨市場直接關聯(lián),調控著實體經濟的運行。隨著近年來國際貿易和經濟一體化的迅猛發(fā)展,期貨市場跨越國界配置資源的作用越發(fā)顯現,成為搶奪國際資源定價權和話語權的重要陣地。期貨市場的地位在不斷增強。 因此,理解和研究我國期貨市場的微觀結構及其運作方式,對于揭示我國期貨產品價格形成規(guī)律,理解期貨市場波動率有重要的意義。認識市場微觀構件如何影響市場價格變動趨勢,可以直接運用到實際期貨產品投資實踐,有助于我們評價期貨市場質量和效率,促使我國期貨市場交易制度、規(guī)則的完善。 市場微觀結構理論的出現和發(fā)展,為深入理解和研究期貨市場微觀結構提供了方法和手段。莫琳·奧哈拉將市場微觀結構的研究界定為“研究確定交易規(guī)則下資產交易的過程和結果”。強調從確定的交易機制之下的規(guī)則出發(fā),來分析價格決定過程。麥德哈文(Ananth Madhavan,2000)將市場微觀結構定義為“一個研究投資者潛在的交易需求轉化為最終交易價格和交易量過程的金融學領域”。關注交易機制本身對價格形成的影響,是市場微觀結構研究的一大特點。 微觀結構噪音的相關研究正是這樣的背景提出和發(fā)展的。根據市場微觀結構的相關理論,現實市場不同于完美信息市場,存在著交易成本、信息不對稱等市場摩擦,證券價格在交易過程會受到這些摩擦因素的影響而偏離完美市場下資產的均衡價格。在交易過程中導致證券價格偏離均衡價格的因素的總和即為微觀結構噪音。微觀結構噪音是產生于交易過程的一系列市場摩擦,包括買賣價差、價格的離散變化、非同步交易、信息不對稱、市場對大宗交易的逐漸反應、訂單流的策略成分、市場參與者的流動性需要、市場參與者存貨控制效應等。 高頻數據和超高頻數據記錄了市場所有的交易信息,其運用促進了微觀結構噪音的研究,也為深入研究我國期貨市場提供了重要的實證數據。 傳統(tǒng)的資產價格波動研究主要以低頻數據為主,采樣頻率較低容易造成的信息流失,難以準確的刻畫市場特征。高頻數據和超高頻數據包含了所有的交易信息,彌補了低頻研究的不足。但超高頻數據具有“采樣頻率高、不等的時間間隔、價格的離散變化、交易的周期模式、交易的多重性”等特性,使得低頻下的波動估計不再是無偏估計。事實上,當采樣頻率越高,基于GARCH,SV模型不能取得較好的估計效果,RV模型的估計量的漸進一致性受到的影響越嚴重。一個主要的原因就是存在著市場微觀結構噪音的干擾,上述波動估計量將不再收斂于積分波動率。Zhang等(2005)提出了二尺度已實現波動率(TSRV),充分利用全樣本觀測數據,將觀測價格的波動分解為來自真實價格的波動和微觀結構噪音的波動,得出了市場波動的漸近一致估計量。 對市場微觀噪音的研究,可以充分揭示具體交易規(guī)則之下,證券市場的價格行為多大程度上偏離了完美市場假設下的均衡價格行為�？梢耘懦^測價格存在的非信息干擾,回歸資產正確的價格。其次,應用微觀結構噪音的的分離框架,可以有效估計價格過程的波動率狀況,為尋求市場交易機會和構建投資組合提供幫助。應用噪音的研究,也能夠形成對市場流動性和市場有效性的評價,從而幫助構建一個高效率、高質量的證券期貨市場。 鑒于目前國內在期貨市場微觀結構方面的研究相對匱乏,對微觀結構噪音的實證研究更是幾乎處于空白狀態(tài)。本文以500ms高頻采樣數據和處理之后的得到的超高頻交易數據為樣本,研究期貨市場價格波動狀況,成交量變動規(guī)律,以及期貨日內交易的一些特征。同時將引入TSRV方法對期貨市場高頻采樣條件下的波動率進行估計,對微觀結構噪音做出分離并分析其構成因素。 本文的主要結構和安排如下： 第一章是緒論部分。簡述了期貨市場微觀結構噪音研究的研究背景和意義。第二章是相關理論和文獻綜述。第三章簡要介紹了我國期貨市場及其微觀結構特征。第四章是文章的實證數據描述以及數據處理方法的介紹,包括了期貨交易的日歷效應和日內分時特征。 第五章是本文的一個重點,介紹了微觀結構噪音估計的原理和本文選取的TSRV噪音估計方法。然后基于逐筆交易數據對不同期貨品種價格波動率和市場噪音水平進行了分離和估計。 第六章分析了微觀結構噪音的影響因素,并基于超高頻數據實證回歸分析了相關因素對噪音的貢獻。第七章是本文的結語,總結了全文的研究和得出的結論,并就研究中存在的一些問題進行了總結,指出了未來可能的研究方向。最后是本文的參考文獻,致謝和附錄。本文研究得出的一些圖表難以在正文中全部給出,可以在附錄中找到。 本文的研究主要得出了以下結論： 第一,滬深300股指期貨、天然橡膠、銅期貨日歷效應,各品種期貨不同合約之間價格存在高度相關性,表現出同向變動趨勢；股指成交量呈現當月交易活躍,其他時間交易較為清淡。臨近交割日成交量先上升后下降的,類似于“M”型變化的趨勢。天然橡膠期貨日間成交量受現貨市場的影響,存在著季節(jié)性變化的趨勢。銅期貨成交量與是否是主力合約有關。 第二,本文分析了滬深300股指期貨、天然橡膠、銅期貨3個期貨品種日內分時交易的特征。發(fā)現期貨日內交易價格存在跳躍的行為,具有典型的離散變動特點,不能視為一個連續(xù)的價格過程。股指、銅期貨的收益率在開盤和收盤附近具有類似“L”型或“倒L”型的特征,離開盤或收盤時點越近,收益率表現出較大幅度波動,隨著距這些時點的間隔增加,收益率波動減小相對較為平緩。成交量有類似收益率的變動趨勢。說明開盤和收盤附近存在較多的信息噪音交易。當市場處于一致的行情走勢時,市場信息分歧較低,交易行為表現一致變動,日內分時特征將不明顯。 第三,利用已實現波動率RV、二尺度已實現波動率TSRV對市場的波動率進行了估計和比較,并對微觀結構噪音進行了分離。發(fā)現以下結果：股指期貨RV大致在104級別,TSRV大致在10-5級別,微觀結構噪音在10-8和10-9兩個級別變動。TSRV對市場噪音干擾的排除更優(yōu),是比RV更好的波動率估計量。微觀結構噪音的變動趨勢與成交量變動有較大關系,當成交量較低時,市場流動性價差,微觀結構噪音相對較高,當交易變得活躍時,微觀結構噪音下降。天然橡膠期貨RV、TSRV都處于10-4水平,但顯然TSRV的值更小,而其微觀結構噪音水平在10-5,噪音水平與成交量有類似于股指期貨的變化,成反向變動關系。銅期貨RV、TSRV、微觀結構噪音分別處于10-4、10-5、10-6水平。同時,微觀結構噪音尖峰事件亦很好的解釋了銅期貨交易手續(xù)費調整和異常交易的現象。 在本文的最后實證分析了微觀結構噪音的影響因素,發(fā)現價差、交易規(guī)模對其有正的貢獻,日內交易次數對其有負的貢獻,TSRV對其無明顯貢獻,說明市場流動性與噪音有負相關關系。 本文的創(chuàng)新之處在于： 第一,首次運用500毫秒處理得出超高頻數據分析了股指期貨、天然橡膠、銅的日內分時交易特征。第二,首次引入了期貨市場資產價格波動率和微觀結構噪音的分離框架,并運用逐筆成交數據對股指期貨、天然橡膠、銅期貨三個品種的已實現波動率、二尺度已實現波動率、微觀結構噪音水平分別作出了估計。第三,驗證了TSRV模型在期貨市場應用的穩(wěn)健性,實證結果表明TSRV估計波動優(yōu)于RV估計量。從而為高頻條件波動率估計提出了一個參考指標,有助于更深刻地認識期貨市場波動特征,更準備的構建投資組合實現風險控制。第四,實證分析了期貨市場噪音的相關影響因素,驗證價差、交易規(guī)模對微觀結構噪音有正的影響,而日內交易頻率與噪音負相關。第五,運用微觀結構噪音尖峰事件,成功的解釋了如市場微觀結構調整(銅期貨交易費率)、市場異常交易情況。 本文的研究可以彌補目前在國內期貨市場微觀結構噪音定量研究上的空白,為以后的相關研究鋪石墊瓦、拋磚引玉。
[Abstract]:The futures market is an important part of the financial market and the national economy, with the price discovery and hedging two important functions. Not only on the stock market, has great impact on the foreign exchange market and other virtual economy, and also directly related to the spot market, regulate the operation of the real economy. In recent years with the rapid development of international trade and economic integration the allocation of resources across borders, the role of the futures market is more and more, has become an important position to snatch international resource pricing and voice. The position in the futures market is growing.
Therefore, the understanding and research of China's futures market micro structure and mode of operation, to reveal China's futures price formation law, understand the futures market volatility has important significance. Know how to influence the market micro component market price movements, can be directly applied to the actual product futures investment practice, helps us to evaluate the futures market the quality and efficiency, promote China's futures market trading system, the perfection of rules.
The emergence of market microstructure theory and development, methods and means are provided for the in-depth understanding and study of futures market microstructure. Maureen O Hara will study the microscopic structure of the market is defined as "the process and results of" asset transactions to determine the transaction rules. Under the stress from the trading mechanism to determine the rules of price analysis in the decision process. Neil (Ananth Madhavan, 2000) will define the market microstructure for transforming a potential transaction demand for investors of finance "the final transaction price and transaction volume process. Pay attention to the influence of trading on the price formation mechanism itself, is a major feature of the market microstructure research.
The related research of microstructure noise is the background and development. According to the theory of market microstructure, the real market is different from the perfect information market, there is a transaction cost, information asymmetry and other market frictions, the equilibrium price of stock price will be affected by the friction factors and deviate from the perfect market assets in the transaction process. In the trading process, resulting in a total stock price deviates from the equilibrium price of the factors is the microstructure noise. The microstructure noise is a series of market frictions in the trading process, including the sale price, discrete changes in price, non synchronous trading, information asymmetry, market gradually in response to the bulk of the transaction, component order flow the liquidity needs of market participants, market participants inventory control effect.
High frequency data and ultra-high frequency data have recorded all trading information in the market, and their application has promoted the research of microstructure noise, and provided important empirical data for further study of China's futures market.
Study on the fluctuation of asset prices mainly in the traditional low frequency data, the sampling frequency is low easy to cause the loss of information, it is difficult to accurately describe the characteristics of the market. The high frequency data and high frequency data contains all of the transaction information, to make up for the lack of the low frequency. But the ultra high frequency data with high sampling frequency, unequal time interval, discrete changes in price, the transaction cycle mode, the characteristics of trading ", so that multiple low frequency fluctuation of the estimator is no longer the unbiased estimation. In fact, when the sampling frequency is high, based on GARCH, the SV model can not get good estimates of the effect, influence the progressive model by RV estimator the more serious. One of the main reasons is the existence of market microstructure noise interference, the fluctuation estimator will no longer converge to the integral fluctuation rate of.Zhang (2005) proposed two scale realized volatility (TSRV), we make full use of the whole sample observation data to decompose the fluctuation of observed price into the fluctuation of real price and microstructure noise, and obtain the asymptotic consistent estimator of market volatility.
Research on market microstructure noise, can fully reveal the specific transaction under the rules, the behavior of stock market prices of the extent to which deviates from the equilibrium price behavior under the perfect market assumption. You can exclude non interference observation price exists, the price return of assets right. Second, the separation framework of microstructure noise, can effective estimation of price process volatility, the market is looking for trading opportunities and portfolio construction help. Research and application of noise, but also to the formation of evaluation on the market liquidity and market efficiency, and help from the construction of a high efficient, high quality of the securities and futures market.
In view of the current domestic research in the futures market micro structure, the relative lack of empirical research on the microstructure noise is almost in a blank state. After taking the 500ms high frequency sampling data and processing the ultra high frequency trading data, research status of price fluctuation of futures market, change the volume, and some characteristics of futures in the deal. While the introduction of TSRV method on the futures market under the condition of high frequency sampling is used to estimate the volatility, make the separation and analysis of its constituent factors of microstructure noise.
The main structure and arrangement of this article are as follows:
The first chapter is the introduction part. The study of microstructure noise in the futures market research background and significance. The second chapter is the related theory and literature review. The third chapter briefly introduces the characteristics of China's futures market and its micro structure. The fourth chapter is the empirical data description and data processing methods in this paper are introduced, including the calendar effect of futures trading and the days when feature.
The fifth chapter is the emphasis of this paper, this paper introduces the TSRV noise principle and microstructure noise estimation method to estimate the selection. Then the separation and estimation of different futures price volatility and market transaction data based on the level of noise.
The sixth chapter analyzes the influencing factors of microstructure noise, and the empirical regression analysis based on the ultra high frequency data with related factors on noise. The seventh chapter is the conclusion of this thesis, summarizes and draws the research conclusion, and summarizes the problems existing in the research, pointed out the direction of future studies finally is this article references acknowledgements and appendix. Some charts are drawn in this paper it is hard to give all in the text, can be found in the appendix.
The main conclusions of this paper are as follows:
First, the Shanghai and Shenzhen 300 stock index futures, natural rubber, copper futures calendar effect, the futures contract between different prices are highly correlated, showed the same change trend; the stock volume presents the active transactions, other time relatively light trading. Near the settlement volume increased after the first drop, similar to the "M" type change trend natural rubber futures day trading volume. Affected by the stock market, there is a seasonal variation trend. Copper futures trading volume and is the main contract.
Second, this paper analyzes the CSI 300 stock index futures, natural rubber, copper futures trading features 3 days of time. It is found that the trading prices of futures intraday skipping behavior, has the typical characteristics of discrete changes, can not be regarded as a continuous process. The price of stock index futures, the rate of return has characteristics similar to "L" or "inverted L" type in the vicinity of the opening and closing of the left disc or closing point closer, yield showed a relatively large fluctuation, with the distance from the point of the interval is increased, the return volatility decreases relatively smooth. Volume change trend similar yields. It shows that there are more near the opening and closing the information of noise trading. When the market is in consistent with the market trend, the differences of market information is low, trading behavior changes consistent, days when feature is not obvious.
Third, the realized volatility of RV two scale realized volatility TSRV market volatility was estimated and compared, and the microstructure noise were separated. Find the following results: RV stock index futures at approximately 104 level, TSRV is at the 10-5 level, the microscopic structure of the noise interference of noise in the market 10-8 and 10-9 two level changes of.TSRV better, is a rate estimator is better than RV fluctuations. Have a greater relationship of microstructure noise change trend and volume changes, when the volume is low, market liquidity spreads, microstructure noise is relatively high, when the transaction becomes active when the microstructure noise decreased. Natural rubber futures RV, TSRV at 10-4 level, but obviously TSRV is smaller, and the microscopic structure of the noise level in the 10-5, the noise level and volume changes similar to the stock index futures, the reverse change relationship between copper futures of RV. , TSRV, microstructure noise respectively in the 10-4,10-5,10-6 level. At the same time, the microstructure noise spike event was also a good explanation of the copper futures transaction fee adjustment and abnormal trading phenomenon.
At the end of this paper, we empirically analyze the influencing factors of microstructure noise. We find that the price difference and the scale of transaction have positive contributions to it. The number of intra day trading has a negative contribution to it, and TSRV has no significant contribution to it, which indicates that market liquidity is negatively correlated with noise.
The innovation of this article lies in:
First, for the first time by 500 milliseconds that the ultra high frequency data analysis of stock index futures, natural rubber, copper within transaction characteristics. Second, first introduced the framework of separation of asset price volatility and futures market microstructure noise, and the use of transaction transaction data of stock index futures, natural rubber, copper has achieved three varieties of two scale volatility, realized volatility, microstructure noise levels were estimated. Third, to verify the robustness of the TSRV model application in the futures market, the empirical results show that TSRV is superior to RV estimation of volatility estimator. Thus rate estimation is proposed as a reference index for high frequency fluctuations, contribute to a more profound to understand the fluctuation characteristics of the futures market, build a portfolio to achieve risk control. Fourth, empirical analysis of the factors related to the effects of noise, the futures market price verification, trading rules Die has a positive impact on the microstructure noise, and the noise trading frequency and negative correlation. Fifth days, the microstructure noise spike event, such as the successful interpretation of market microstructure adjustment (copper futures rate), market abnormal trading.
The research of this paper can make up the blank of the quantitative research on the micro structure noise in the domestic futures market.

【學位授予單位】：西南財經大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：F724.5;F224

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