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Author: Maria Pacurar
Publisher:
ISBN:
Category : Monte Carlo method
Languages : en
Pages : 0
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Author: Maria Pacurar
Publisher:
ISBN:
Category : Monte Carlo method
Languages : en
Pages : 0
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Author: Lidan Grossmass
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Category :
Languages : en
Pages : 0
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Author: Yingjie Dong
Publisher:
ISBN:
Category : Econometrics
Languages : en
Pages : 137
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"This dissertation consists of three essays on high-frequency financial data analysis. I consider intraday periodicity adjustment and its effect on intraday volatility estimation, the Business Time Sampling (BTS) scheme and the estimation of market microstructure noise using NYSE tick-by-tick transaction data. Chapter 2 studies two methods of adjusting for intraday periodicity of highfrequency financial data: the well-known Duration Adjustment (DA) method and the recently proposed Time Transformation (TT) method (Wu (2012)). I examine the effects of these adjustments on the estimation of intraday volatility using the Autoregressive Conditional Duration-Integrated Conditional Variance (ACD-ICV) method of Tse and Yang (2012). I find that daily volatility estimates are not sensitive to intraday periodicity adjustment. However, intraday volatility is found to have a weaker U-shaped volatility smile and a biased trough if intraday periodicity adjustment is not applied. In addition, adjustment taking account of trades with zero duration (multiple trades at the same time stamp) results in deeper intraday volatility smile..."--Author's abstract.
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Category :
Languages : en
Pages : 398
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Author: Frederi G. Viens
Publisher: John Wiley & Sons
ISBN: 0470876883
Category : Business & Economics
Languages : en
Pages : 468
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CUTTING-EDGE DEVELOPMENTS IN HIGH-FREQUENCY FINANCIAL ECONOMETRICS In recent years, the availability of high-frequency data and advances in computing have allowed financial practitioners to design systems that can handle and analyze this information. Handbook of Modeling High-Frequency Data in Finance addresses the many theoretical and practical questions raised by the nature and intrinsic properties of this data. A one-stop compilation of empirical and analytical research, this handbook explores data sampled with high-frequency finance in financial engineering, statistics, and the modern financial business arena. Every chapter uses real-world examples to present new, original, and relevant topics that relate to newly evolving discoveries in high-frequency finance, such as: Designing new methodology to discover elasticity and plasticity of price evolution Constructing microstructure simulation models Calculation of option prices in the presence of jumps and transaction costs Using boosting for financial analysis and trading The handbook motivates practitioners to apply high-frequency finance to real-world situations by including exclusive topics such as risk measurement and management, UHF data, microstructure, dynamic multi-period optimization, mortgage data models, hybrid Monte Carlo, retirement, trading systems and forecasting, pricing, and boosting. The diverse topics and viewpoints presented in each chapter ensure that readers are supplied with a wide treatment of practical methods. Handbook of Modeling High-Frequency Data in Finance is an essential reference for academics and practitioners in finance, business, and econometrics who work with high-frequency data in their everyday work. It also serves as a supplement for risk management and high-frequency finance courses at the upper-undergraduate and graduate levels.
Author: Yi Xue
Publisher:
ISBN:
Category : Econometrics
Languages : en
Pages : 294
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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: Florian Jacob
Publisher: Springer
ISBN: 3658093897
Category : Mathematics
Languages : en
Pages : 78
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By studying the ability of the Normal Tempered Stable (NTS) model to fit the statistical features of intraday data at a 5 min sampling frequency, Florian Jacobs extends the research on high frequency data as well as the appliance of tempered stable models. He examines the DAX30 returns using ARMA-GARCH NTS, ARMA-GARCH MNTS (Multivariate Normal Tempered Stable) and ARMA-FIGARCH (Fractionally Integrated GARCH) NTS. The models will be benchmarked through their goodness of fit and their VaR and AVaR, as well as in an historical Backtesting.
Author:
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ISBN:
Category :
Languages : en
Pages :
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Author: Ji Shen
Publisher:
ISBN:
Category :
Languages : en
Pages : 105
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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.
