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A Monte Carlo study of new time series statistical tests and their application to the modeling of price dynamics in futures markets.

機(jī)譯：蒙特卡洛研究新的時(shí)間序列統(tǒng)計(jì)檢驗(yàn)及其在期貨市場(chǎng)價(jià)格動(dòng)態(tài)建模中的應(yīng)用。

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摘要

Modeling price dynamics in financial markets has become an important research area in financial economics. In the past empirical studies of financial price movements were based on methods that were incapable of detecting or modeling nonlinear serial dependence that characterizes financial market data. Recently, advances in the study of nonlinear dynamics in the physical sciences have motivated researchers to apply nonlinear time-series models to the study of financial and economic data. This dissertation investigates three statistical tests which can detect nonlinear serial dependence, and applies these tests and two nonlinear time-series models to futures markets.;Based on findings of Monte Carlo investigation, the three tests and two nonlinear time-series models are applied to the study of price dynamics in futures markets. The futures studied are the S&P 500, Crude Oil, Japanese Yen, Deutsche Mark, and Eurodollar futures. The results show that the price changes of all five futures have nonlinear serial dependence, and that they can be modeled by nonlinear time-series models, either GARCH, or TAR, or combined TAR-GARCH model.;The main conclusions to emerge from the findings of this dissertation are as follows. The three tests are reliable for detecting serial dependence, including nonlinear serial dependence. The tests work well when sample size equals 1000 or larger and the sample's departure from the null hypothesis is not too small. When analyzing futures prices, we have to acount for nonlinear serial dependence, use nonlinear models with conditional heteroskedasticity and conditional mean change.;In this dissertation, the finite sample properties of the BDS, TAR-F and Q
機(jī)譯：對(duì)金融市場(chǎng)中的價(jià)格動(dòng)態(tài)進(jìn)行建模已成為金融經(jīng)濟(jì)學(xué)的重要研究領(lǐng)域。過去，對(duì)金融價(jià)格變動(dòng)的經(jīng)驗(yàn)研究基于無法檢測(cè)或建模表征金融市場(chǎng)數(shù)據(jù)的非線性序列依賴性的方法。近年來，物理學(xué)中非線性動(dòng)力學(xué)研究的不斷發(fā)展促使研究人員將非線性時(shí)間序列模型應(yīng)用于金融和經(jīng)濟(jì)數(shù)據(jù)的研究。本文研究了三種可以檢測(cè)非線性序列相關(guān)性的統(tǒng)計(jì)檢驗(yàn)，并將這些檢驗(yàn)和兩個(gè)非線性時(shí)間序列模型應(yīng)用于期貨市場(chǎng)。基于蒙特卡洛研究的結(jié)果，將這三個(gè)檢驗(yàn)和兩個(gè)非線性時(shí)間序列模型應(yīng)用于期貨市場(chǎng)。期貨市場(chǎng)價(jià)格動(dòng)態(tài)研究。研究的期貨是標(biāo)準(zhǔn)普爾500，原油，日元，德國(guó)馬克和歐洲美元的期貨。結(jié)果表明，所有五種期貨的價(jià)格變化都具有非線性序列依賴性，可以通過非線性時(shí)間序列模型GARCH或TAR或組合TAR-GARCH模型進(jìn)行建模。本論文的發(fā)現(xiàn)如下。這三個(gè)測(cè)試對(duì)于檢測(cè)包括非線性序列依賴性在內(nèi)的序列依賴性是可靠的。當(dāng)樣本大小等于或大于1000且樣本與原假設(shè)的偏離不太小時(shí)，這些測(cè)試會(huì)很好地工作。在分析期貨價(jià)格時(shí)，我們必須考慮非線性序列依賴性，使用具有條件異方差和條件均值變化的非線性模型。本文研究了BDS，TAR-F和Q的有限樣本性質(zhì)。

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  • 作者

    Gao, Hong.;

  • 作者單位

    American University.;

  • 授予單位 American University.;
  • 學(xué)科 Statistics.;Economics Finance.;Economics General.
  • 學(xué)位 Ph.D.
  • 年度 1994
  • 頁碼 238 p.
  • 總頁數(shù) 238
  • 原文格式 PDF
  • 正文語種 eng
  • 中圖分類
  • 關(guān)鍵詞

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