Essays on Modeling of Volatility, Duration and Volume in High-frequency Data PDF Download
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Author: Haiqing Zheng
Publisher:
ISBN:
Category :
Languages : en
Pages : 134
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Author: Haiqing Zheng
Publisher:
ISBN:
Category :
Languages : en
Pages : 134
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Author: Ji Shen
Publisher:
ISBN:
Category :
Languages : en
Pages : 105
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Author: Pujin Liu
Publisher:
ISBN:
Category :
Languages : en
Pages : 286
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The thesis consists of three essays dealing with the modeling of volatility in financial markets, trade durations, and Value-at-Risk (VaR). The first essay models nonlinearities in the return series to estimate time-varying volatility by incorporating both regime changes and jumps. Two types of regime-switching GARCH-jump models with autoregressive jump intensity are presented. The first model follows the traditional Markov regime-switching model proposed in Hamilton (1989). As the unknown regimes in the Markov model lead to difficulty in forecasting, a threshold GARCH-jump model, in which regimes are known after observing the threshold variable in the previous period, is also proposed. The second essay models the intraday durations between two adjacent trade transactions by considering the impact of unaccounted struc- tural changes on parameter estimates. Monte Carlo simulations show that the observed high persistence in trade durations can be spurious and caused by unaccounted structural changes in the data generating process. The third essay investigates the use of realized moments in VaR forecasting, which is an important issue in risk management. Many VaR models rely only on the mean and volatility and ignore higher moments of returns, which leads to un- derestimation of VaR due to the unaccounted fat-tail property of the return series. Applying the Cornish-Fisher expansion to incorporate realized higher moments constructed from high frequency data, the proposed realized moment models outperform the realized volatility model and the traditional RiskMet- rics model, especially during the financial crisis period (2008-09).
Author: Ilze Kalnina
Publisher:
ISBN:
Category : Academic theses
Languages : en
Pages : 0
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Author: Yucheng Sun
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Author: Yacine Aït-Sahalia
Publisher: Princeton University Press
ISBN: 0691161437
Category : Business & Economics
Languages : en
Pages : 683
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Book Description
A comprehensive introduction to the statistical and econometric methods for analyzing high-frequency financial data High-frequency trading is an algorithm-based computerized trading practice that allows firms to trade stocks in milliseconds. Over the last fifteen years, the use of statistical and econometric methods for analyzing high-frequency financial data has grown exponentially. This growth has been driven by the increasing availability of such data, the technological advancements that make high-frequency trading strategies possible, and the need of practitioners to analyze these data. This comprehensive book introduces readers to these emerging methods and tools of analysis. Yacine Aït-Sahalia and Jean Jacod cover the mathematical foundations of stochastic processes, describe the primary characteristics of high-frequency financial data, and present the asymptotic concepts that their analysis relies on. Aït-Sahalia and Jacod also deal with estimation of the volatility portion of the model, including methods that are robust to market microstructure noise, and address estimation and testing questions involving the jump part of the model. As they demonstrate, the practical importance and relevance of jumps in financial data are universally recognized, but only recently have econometric methods become available to rigorously analyze jump processes. Aït-Sahalia and Jacod approach high-frequency econometrics with a distinct focus on the financial side of matters while maintaining technical rigor, which makes this book invaluable to researchers and practitioners alike.
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: Constantin A. Roth
Publisher:
ISBN:
Category :
Languages : en
Pages :
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In the first chapter, I compare the forecasting accuracy of different high-frequency based volatility models. The empirical analysis shows that the HEAVY and the Realized GARCH generally outperform the rest of the models. The inclusion of overnight returns considerably improves volatility forecasts for stocks across all models. Furthermore, the analysis shows that models based on realized volatility benefit much less from allowing leverage effects than do models based on daily returns. In the second chapter, the cause for this observation is investigated more deeply. I explain it by documenting that realized volatility tends to be higher on down-days than on up-days and that a similar asymmetry cannot be found in squared daily returns. I show that leverage effects are present already at high return-frequencies and that these are capable of generating asymmetries in realized variance but not in squared returns. In the third chapter, a conservative test based on the adaptive lasso is applied to investigate the optimal lag structure for modeling realized volatility dynamics. The empirical analysis shows that the optimal significant lag structure is time-varying and subject to drastic regime shifts. The accuracy of the HAR model can be explained by the observation that in many cases the relevant information for prediction is included in the first 22 lags. In the fourth chapter, a wild multiplicative bootstrap is introduced for M- and GMM estimators of time series. In Monte Carlo simulations, the wild bootstrap always outperforms inference which is based on standard asymptotic theory. Moreover, in most cases the accuracy of the wild bootstrap is also higher and more stable than that of the block bootstrap whose accuracy depends heavily on the choice of the block size.
Author: Serge Luther Nyawa Womo
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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This thesis is about financial risks and high frequency data, with a particular focus on financial systemic risk, the risk of high dimensional portfolios and market microstructure noise. It is organized on three chapters. The first chapter provides a continuous time reduced-form model for the propagation of negative idiosyncratic shocks within a financial system. Using common factors and mutually exciting jumps both in price and volatility, we distinguish between sources of systemic failure such as macro risk drivers, connectedness and contagion. The estimation procedure relies on the GMM approach and takes advantage of high frequency data. We use models' parameters to define weighted, directed networks for shock transmission, and we provide new measures for the financial system fragility. We construct paths for the propagation of shocks, firstly within a number of key US banks and insurance companies, and secondly within the nine largest S&P sectors during the period 2000-2014. We find that beyond common factors, systemic dependency has two related but distinct channels: price and volatility jumps. In the second chapter, we develop a new factor-based estimator of the realized covolatility matrix, applicable in situations when the number of assets is large and the high-frequency data are contaminated with microstructure noises. Our estimator relies on the assumption of a factor structure for the noise component, separate from the latent systematic risk factors that characterize the cross-sectional variation in the frictionless returns. The new estimator provides theoretically more efficient and finite-sample more accurate estimates of large-scale integrated covolatility, correlation, and inverse covolatility matrices than other recently developed realized estimation procedures. These theoretical and simulation-based findings are further corroborated by an empirical application related to portfolio allocation and risk minimization involving several hundred individual stocks. The last chapter presents a factor-based methodology to estimate microstructure noise characteristics and frictionless prices under a high dimensional setup. We rely on factor assumptions both in latent returns and microstructure noise. The methodology is able to estimate rotations of common factors, loading coefficients and volatilities in microstructure noise for a huge number of stocks. Using stocks included in the S&P500 during the period spanning January 2007 to December 2011, we estimate microstructure noise common factors and compare them to some market-wide liquidity measures computed from real financial variables. We obtain that: the first factor is correlated to the average spread and the average number of shares outstanding; the second and third factors are related to the spread; the fourth and fifth factors are significantly linked to the closing log-price. In addition, volatilities of microstructure noise factors are widely explained by the average spread, the average volume, the average number of trades and the average trade size.
Author: Constantin Roth
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
In the first chapter, I compare the forecasting accuracy of different high-frequency based volatility models. The empirical analysis shows that the HEAVY and the Realized GARCH generally outperform the rest of the models. The inclusion of overnight returns considerably improves volatility forecasts for stocks across all models. Furthermore, the analysis shows that models based on realized volatility benefit much less from allowing leverage effects than do models based on daily returns. In the second chapter, the cause for this observation is investigated more deeply. I explain it by documenting that realized volatility tends to be higher on down-days than on up-days and that a similar asymmetry cannot be found in squared daily returns. I show that leverage effects are present already at high return-frequencies and that these are capable of generating asymmetries in realized variance but not in squared returns. In the third chapter, a conservative test based on the adaptive lasso is applied to investigate the optimal lag structure for modeling realized volatility dynamics. The empirical analysis shows that the optimal significant lag structure is time-varying and subject to drastic regime shifts. The accuracy of the HAR model can be explained by the observation that in many cases the relevant information for prediction is included in the first 22 lags. In the fourth chapter, a wild multiplicative bootstrap is introduced for M- and GMM estimators of time series. In Monte Carlo simulations, the wild bootstrap always outperforms inference which is based on standard asymptotic theory. Moreover, in most cases the accuracy of the wild bootstrap is also higher and more stable than that of the block bootstrap whose accuracy depends heavily on the choice of the block size.
