An Exploratory Study of Stock Price Behavior and Volatility Estimation Using High Frequency Data PDF Download
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Author: Guangren Xi
Publisher:
ISBN:
Category :
Languages : en
Pages : 150
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Book Description
Author: Guangren Xi
Publisher:
ISBN:
Category :
Languages : en
Pages : 150
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Book Description
Author: Xiaohua Zheng
Publisher:
ISBN:
Category : Electronic dissertations
Languages : en
Pages : 79
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Book Description
Measuring and modeling financial volatility are key steps for derivative pricing and risk management. In financial markets, there are two kinds of data: low-frequency financial data and high-frequency financial data. Most research has been done based on low-frequency data. In this dissertation we focus on high-frequency data. In theory, the sum of squares of log returns sampled at high frequency estimates their variance. For log price data following a diffusion process without noise, the realized volatility converges to its quadratic variation. When log price data contain market microstructure noise, the realized volatility explodes as the sampling interval converges to 0. In this dissertation, we generalize the fundamental Ito isometry and analyze the speed with which stochastic processes approach to their quadratic variations. We determine the difference between realized volatility and quadratic variation under mean square constraints for Brownian motion and general case. We improve the estimation for quadratic variation. The estimators found by us converge to quadratic variation at a higher rate.
Author: Duke Whang
Publisher:
ISBN:
Category :
Languages : en
Pages : 96
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Book Description
The availability of software tools, high frequency data, and recent advances in statistical inference all allow a greater study of continuous-time models of price and volatility processes. This research studies the structure of intraday stock volatility over a selected group of stocks from 2007 to 2011. We use nearly every valid transaction in the Trades and Quotes database to obtain a price series which is sampled every second. We calculate realized variation (RV), the sum of squared log returns, to estimate squared volatility. We partition the trading day at the level of 100-second time intervals, and we observe mean reversion in RV even at this time scale. We estimate a modified Heston model for RV in which statistical criteria are used to detect volatility jumps.
Author: Leonard Mushunje
Publisher:
ISBN:
Category : Economics
Languages : en
Pages : 0
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Book Description
Fundamental theorem behind financial markets is that stock prices are intrinsically complex and stochastic in nature. One of the complexities is the volatilities associated with stock prices. Price volatility is often detrimental to the return economics and thus investors should factor it in when making investment decisions, choices, and temporal or permanent moves. It is therefore crucial to make necessary and regular stock price volatility forecasts for the safety and economics of investors,Äô returns. These forecasts should be accurate and not misleading. Different traditional models and methods such as ARCH, GARCH have been intuitively implemented to make such forecasts, however they fail to effectively capture the short-term volatility forecasts. In this paper we investigate and implement a combination of numeric and probabilistic models towards short-term volatility and return forecasting for high frequency trades. The essence is that: one-day-ahead volatility forecasts were made with Gaussian Processes (GPs) applied to the outputs of a numerical market prediction (NMP) model. Firstly, the stock price data from NMP was corrected by a GP. Since it not easy to set price limits in a market due to its free nature, and randomness of the prices, a censored GP was used to model the relationship between the corrected stock prices and returns. To validate the proposed approach, forecasting errors were evaluated using the implied and estimated data.
Author: Yinfen Tang
Publisher:
ISBN:
Category :
Languages : en
Pages : 35
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Book Description
This paper introduces a solution that combines the Kalman and particle fi lters to the challenging problem of estimating integrated volatility using high-frequency data where the underlying prices are perturbed by a mixture of random noise and price discreteness. An explanation is presented of how the proposed combined filtering approach is able to correct for bias due to this mixed-type microstructure effect. Simulation and empirical studies on the tick-by-tick trade price data for four US stocks in the year 2009 show that our method has clear advantages over existing high-frequency volatility estimation methods.
Author: Bart Pieter Marie Frijns
Publisher:
ISBN: 9789090188256
Category :
Languages : en
Pages : 149
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Author: Rituparna Sen
Publisher:
ISBN:
Category :
Languages : en
Pages :
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An important component of the models for stock price process is volatility. It is necessary to estimate volatility in many practical applications like option pricing, portfolio selection and risk management. Now-a-days stock price data is available at very high frequency and the most common estimator of volatility using such data is the realized variance. However in the presence of microstructure noise, realized variance diverges to infinity. The paper proposes principal component analysis of functional data approach to separate the volatility of a process from microstructure noise. This approach can be used to detect days on which the stock price process has jumps and to measure the size of jumps. Thus we can separate the jump component from the daily integrated volatility in the quadratic variation process. This separation leads to better understanding and prediction of integrated volatility. We develop the theory and present simulation as well as real data examples.
Author: Yucheng Sun
Publisher:
ISBN:
Category :
Languages : en
Pages : 125
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Book Description
Based on high-frequency price data, this thesis focuses on estimating the realized covariance and the integrated volatility of asset prices, and applying volatility estimation to price jump detection. The first chapter uses the LASSO procedure to regularize some estimators of high dimensional realized covariance matrices. We establish theoretical properties of the regularized estimators that show its estimation precision and the probability that they correctly reveal the network structure of the assets. The second chapter proposes a novel estimator of the integrated volatility which is the quadratic variation of the continuous part in the price process. This estimator is obtained by truncating the two-scales realized variance estimator. We show its consistency in the presence of market microstructure noise and finite or infinite activity jumps in the price process. The third chapter employs this estimator to design a test to explore the existence of price jumps with noisy price data.
Author: Jianqing Fan
Publisher:
ISBN:
Category :
Languages : en
Pages : 36
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Book Description
The wide availability of high-frequency data for many financial instruments stimulates a upsurge interest in statistical research on the estimation of volatility. Jump-diffusion processes observed with market microstructure noise are frequently used to model high-frequency financial data. Yet, existing methods are developed for either noisy data from a continuous diffusion price model or data from a jump-diffusion price model without noise. We propose methods to cope with both jumps in the price and market microstructure noise in the observed data. They allow us to estimate both integrated volatility and jump variation from the data sampled from jump-diffusion price processes, contaminated with the market microstructure noise. Our approach is to first remove jumps from the data and then apply a noise-resistent method to estimated the integrated volatility. The asymptotic analysis and the simulation study reveal that the proposed wavelet methods can successfully remove the jumps in the price processes and the integrated volatility can be estimated as well as the case with no presence of jumps in the price processes. In addition, they have outstanding statistical efficiency. The methods are illustrated by applications to two high-frequency exchange rate data sets.
Author: Luc Bauwens
Publisher: John Wiley & Sons
ISBN: 1118272056
Category : Business & Economics
Languages : en
Pages : 566
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Book Description
A complete guide to the theory and practice of volatility models in financial engineering Volatility has become a hot topic in this era of instant communications, spawning a great deal of research in empirical finance and time series econometrics. Providing an overview of the most recent advances, Handbook of Volatility Models and Their Applications explores key concepts and topics essential for modeling the volatility of financial time series, both univariate and multivariate, parametric and non-parametric, high-frequency and low-frequency. Featuring contributions from international experts in the field, the book features numerous examples and applications from real-world projects and cutting-edge research, showing step by step how to use various methods accurately and efficiently when assessing volatility rates. Following a comprehensive introduction to the topic, readers are provided with three distinct sections that unify the statistical and practical aspects of volatility: Autoregressive Conditional Heteroskedasticity and Stochastic Volatility presents ARCH and stochastic volatility models, with a focus on recent research topics including mean, volatility, and skewness spillovers in equity markets Other Models and Methods presents alternative approaches, such as multiplicative error models, nonparametric and semi-parametric models, and copula-based models of (co)volatilities Realized Volatility explores issues of the measurement of volatility by realized variances and covariances, guiding readers on how to successfully model and forecast these measures Handbook of Volatility Models and Their Applications is an essential reference for academics and practitioners in finance, business, and econometrics who work with volatility models in their everyday work. The book also serves as a supplement for courses on risk management and volatility at the upper-undergraduate and graduate levels.
