Three Essays on the Econometric Analysis of High Frequency Financial Data PDF Download
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Author: Roel C. A. Oomen
Publisher:
ISBN:
Category : Macroeconomics
Languages : en
Pages : 101
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Author: Roel C. A. Oomen
Publisher:
ISBN:
Category : Macroeconomics
Languages : en
Pages : 101
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Book Description
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 398
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Author:
Publisher:
ISBN: 9789036104357
Category :
Languages : en
Pages : 182
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"It has long been demonstrated that continuous-time methods are powerful tools in financial modeling. Yet only in recent years, their counterparts in empirical analysis-high frequency econometrics-began to emerge with the availability of intra-day data and relevant statistical tools. This dissertation contributes to the development of this emerging area in two directions. On the one hand, it develops new econometric tools to identify different types of interdependence structure among asset state processes. Chapter 2 examines the co-movement of asset price and its volatility, known as leverage effect. Different from previous work, this chapter allows price and volatility processes to have both continuous and discontinuous stochastic components that may contribute to the overall leverage effect. The second type is about the interdependence between price process and its jump intensity, known as self-excitation. Chapter 3 extends the definition of self-excitation in jumps accordingly, proposes statistical tests to detect its presence in a discretely observed path at high frequency, and derives the tests' asymptotic properties. On the other hand, Finance theory implies a set of constraints on the dynamics of an option price process and that of its underlying processes. Yet empirical option pricing models may either implicitly ignore some theoretical constraints or impose a possibly misspecified parametric structure on it. Chapter 4 fill this gap, by proposing a statistical procedure that utilizes information from the time series of the underlying processes to test the specification of a given option pricing model. "--Samenvatting auteur.
Author: Rodrigo Hizmeri
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Author: Nikolaus Hautsch
Publisher: Springer Science & Business Media
ISBN: 364221925X
Category : Business & Economics
Languages : en
Pages : 381
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Book Description
The availability of financial data recorded on high-frequency level has inspired a research area which over the last decade emerged to a major area in econometrics and statistics. The growing popularity of high-frequency econometrics is driven by technological progress in trading systems and an increasing importance of intraday trading, liquidity risk, optimal order placement as well as high-frequency volatility. This book provides a state-of-the art overview on the major approaches in high-frequency econometrics, including univariate and multivariate autoregressive conditional mean approaches for different types of high-frequency variables, intensity-based approaches for financial point processes and dynamic factor models. It discusses implementation details, provides insights into properties of high-frequency data as well as institutional settings and presents applications to volatility and liquidity estimation, order book modelling and market microstructure analysis.
Author: Shouwei Liu
Publisher:
ISBN:
Category : Options (Finance)
Languages : en
Pages : 126
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"My dissertation consists of three essays which contribute new theoretical and em- pirical results to Volatility Estimation and Market Microstructure theory as well as Risk Management. Chapter 2 extends the ACD-ICV method proposed by Tse and Yang (2012) for the estimation of intraday volatility of stocks to estimate monthly volatility. We compare the ACD-ICV estimates against the realized volatility (RV) and the generalized autoregressive conditional heteroskedasticity (GARCH) estimates. Our Monte Carlo experiments and empirical results on stock data of the New York Stock Exchange show that the ACD-ICV method performs very well against the other two methods. As a 30-day volatility predictor, the Chicago Board Options Exchange volatility index (VIX) predicts the ACD-ICV volatility estimates better than the RV estimates. While the RV method appears to dominate the literature, the GARCH method based on aggregating daily conditional variance over a month performs well against the RV method..."--Author's abstract.
Author: Yi Xue
Publisher:
ISBN:
Category : Econometrics
Languages : en
Pages : 0
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This thesis consists of three essays that study three interdependent topics: microstructure foundation of volatility clustering, inefficiency of information diffusion and jump detection in high frequency financial time series data. Volatility clustering, with autocorrelations of the hyperbolic decay rate, is unquestionably one of the most important stylized facts of financial time series. The first essay forms Chapter 1 which presents a market microstructure model that is able to generate volatility clustering with hyperbolic autocorrelations through traders with multiple trading frequencies using Bayesian information updating in an incomplete market. The model illustrates that signal extraction, which is induced by multiple trading frequency, can increase the persistence of the volatility of returns. Furthermore, it is shown that the local temporal memory of the underlying time series of returns and their volatility varies greatly with the number of traders in the market. The second essay, Chapter 2, presents a market microstructure model showing that an increasing number of information hierarchies among informed competitive traders leads to a slower information diffusion rate and informational inefficiency. The model illustrates that informed traders may prefer trading with each other rather than with noise traders in the presence of the information hierarchies. Furthermore, it is shown that momentum can be generated from the trend following behavior pattern of noise traders. I propose a new nonparametric test based on wavelets to detect jump arrivals in high frequency financial time series data, in the third essay, Chapter 3. It is demonstrated that the test is robust for different specifications of price processes and the presence of market microstructure noise and it has good size and power. Further, I examine the multi-scale jump dynamics in U.S. equity markets and the findings are as follows. First, the jump dynamics of equities are entirely different across different time scales. Second, although arrival densities of positive jumps and negative jumps are symmetric across different time scales, the magnitude of jumps is distributed asymmetrically at high frequencies. Third, only twenty percent of jumps occur in the trading session from 9:30AM to 4:00PM, suggesting that jumps are largely determined by news rather than liquidity shocks.
Author: Christian Nguenang Kapnang
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Institutional changes in markets regulation in recent years have enhanced the multiplication of markets and the cross listing of assets simultaneously in many places. The prices for a security on those interrelated markets are strongly linked by arbitrage activities. This is also the case for one security and its derivatives: Cash and futures, CDS and Credit spread, spot and options. In those multiple markets settings, it is interesting for regulators, investors and academia to understand and measure how each market contributes to the dynamic of the common fundamental value. At the same time, improvement in ITC fueled trading activity and generated High frequency data. My thesis develops new frameworks, with respect to the data frequency, to measure the contribution of each market to the formation of prices (Price discovery) and to the formation of volatility (Volatility discovery). In the first chapter, I show that existing metrics of price discovery lead to misleading conclusions when using High-frequency data. Due to uninformative microstructure noises, they confuse speed and noise dimension of information processing. I then propose robust-to-noise metrics, that are good at detecting “which market is fast”, and produce tighten bounds. Using Monte Carlo simulations and Dow Jones stocks traded on NYSE and NASDAQ, I show that the data are in line with my theoretical conclusions. In the second chapter, I propose a new way to define price adjustment by building an Impulse Response measuring the permanent impact of market's innovation and I give its asymptotic distribution. The framework innovates in providing testable results for price discovery measures based on innovation variance. I later present an equilibrium model of different maturities futures markets and show that it supports my metric: As the theory suggests, the measure selects the market with the higher number of participants as dominating the price discovery. An application on some metals of the London Metal Exchange shows that 3-month futures contract dominates the spot and the 15-month in price formation. The third chapter builds a continuous time comprehensive framework for Price discovery measures with High Frequency data, as the literature exists only in a discrete time. It also has advantages on the literature in that it explicitly deals with non-informative microstructure noises and accommodates a stochastic volatility. We derive a measure of price discovery evaluating the permanent impact of a shock on a market's innovation. Empirics show that it has good properties. In the fourth chapter, I develop a framework to study the contribution to the volatility of common volatility. This allows answering questions such as: Does volatility of futures markets dominate volatility of the Cash market in the formation of permanent volatility? I build a VECM with Autoregressive Stochastic Volatility estimated by MCMC method and Bayesian inference. I show that not only prices are cointegrated, their conditional volatilities also share a permanent factor at the daily and intraday level. I derive measures of market's contribution to Volatility discovery. In the application on metals and EuroStoxx50 futures, I find that for most of the securities, while price discovery happens on the cash market, the volatility discovery happens in the Futures market. Lastly, I build a framework that exploits High frequency data and avoid computational burden of MCMC. I show that Realized Volatilities are driven by a common component and I compute contribution of NYSE and NASDAQ to permanent volatility of some Dow Jones stocks. I obtain that volatility of the volume is the best determinant of volatility discovery, but low figures suggest others important factors.
Author: Caiqin Wang
Publisher:
ISBN:
Category :
Languages : en
Pages : 108
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Author: Gang Xu
Publisher:
ISBN:
Category :
Languages : en
Pages :
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This thesis documents the research and findings in the following three related areas of financial econometrics: The first essay examines whether volatility contains information to predict the likelihood of a price jump during the next trading day. It is motivated by the theoretical model of Bansal & Shaliastovich (2008) who develop a long-run learning model, arguing that market volatility should be able to predict the likelihood of jumps. I use S&P 500 futures prices and extensions of the GARCH jump model of Maheu & McCurdy (2004) to relate jump probabilities to conditional volatility. Since volatility is a latent variable, which can be measured using different variables, I consider predictions based upon squared daily return, at-the-money implied volatility, model-free im- plied volatility and high-frequency realized volatility. I find evidence that volatility can predict jump likelihood and the best predictive variable is the model-free implied volatility: which is constructed using cross-section of option prices. Therefore, this thesis contributes to the current literature by documenting the information efficiency of option prices when predicting the future likelihood of jumps. In addition. I also develop a new approach based on Poisson regression which compares the jump intensity obtained from the GARCH jump model with the intraday jump numbers counted using the method of Andersen et al. (2007b). I find the two measures of jumps match fairly well with each other in the period from 1990 to 1997. However, any such relationship seems to disappear in the later period from 1998 to 2004. The second essay is motivated by the affine jump-diffusion model of Duffie et al. (2000), which allows jump intensity to be an affine function of state variables. I examine whether volatility can predict the intensity of price jumps in stochastic volatility jump models, estimated using Markov Chain Monte Carlo simulation. Comparing implied volatility with high-frequency realized volatility, I find allowing the jump intensity to be an affine function of model-free implied volatility yields the best model, based on either the Deviance Information Criterion or on diagnostic tests. Further comparison are made for candidate AR(l) process which specify the stochastic volatility. I find a jump model with the log variance an AR( 1) process performs better than a jump model with Ornstein-Uhlenbeck stochastic volatility. In a Monte Carlo simulation, I find the Deviance Information Criterion is a reliable criterion to differentiate between competing equity price dynamics when there are price jumps and volatility is stochastic. In addition to examining univariate equity return models, in the third essay I also develop a bivariate equity return model which simultaneously captures time-varying correlation and volatility spillovers in the international equity markets. This model is calibrated using the weekly equity index returns from the US. UK, Germany, India and Brazil stock markets and it is compared with simplier model specifications. I find evidence that supports time varying correlation between equity markets in both developed and developing economics. How- ever, the volatility spillovers mainly exist from US equity returns to equity returns in other economies. This thesis concludes with a short discussion of its limitations and future research directions.
