Download or read book Three Essays on Using High Frequency Data in Estimating Financial Risks written by Lidan Grossmass and published by . This book was released on 2013 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Three Essays on Financial Risks Using High Frequency Data written by Serge Luther Nyawa Womo and published by . This book was released on 2018 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Three Essays on Estimation and Dynamic Modelling of Multivariate Market Risks Using High Frequency Financial Data written by Valeri Voev and published by . This book was released on 2008 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Three Essays on High Frequency Financial Data and Their Use for Risk Management written by Maria Pacurar and published by . This book was released on 2006 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Essays on High frequency Financial Data Analysis written by Yingjie Dong and published by . This book was released on 2015 with total page 137 pages. Available in PDF, EPUB and Kindle. Book excerpt: "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.
Download or read book Three Essays on Improving Financial Risk Estimation Forecasting and Backtesting written by and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Essays in Applied Econometrics of High Frequency Financial Data written by Ilya Archakov and published by . This book was released on 2016 with total page 173 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the first chapter, co-authored with Peter Hansen and Asger Lunde, we suggest a novel approach to modeling and measuring systematic risk in equity markets. We develop a new modeling framework that treats an asset return as a dependent variable in a multiple regression model. The GARCH-type dynamics of conditional variances and correlations between the regression variables naturally imply a temporal variation of regression coefficients (betas). The model incorporates extra information from the realized (co-)variance measures extracted from high frequency data, which helps to better identify the latent covariance process and capture its changes more promptly. The suggested structure is consistent with the broad class of linear factor models in the asset pricing literature. We apply our framework to the famous three-factor Fama-French model at the daily frequency. Throughout the empirical analysis, we consider more than 800 individual stocks as well as style and sectoral exchange traded funds from the U.S. equity market. We document an appreciable cross-sectional and temporal variation of the model-implied risk loadings with the especially strong (though short-lived) distortion around the Financial Crisis episode. In addition, we find a significant heterogeneity in a relative explanatory power of the Fama-French factors across the different sectors of economy and detect a fluctuation of the risk premia estimates over time. The empirical evidence emphasizes the importance of taking into account dynamic aspects of the underlying covariance structure in asset pricing models. In the second chapter, written with Bo Laursen, we extend the popular dynamic Nelson-Siegel framework by introducing time-varying volatilities in the factor dynamics and incorporating the realized measures to improve the identification of the latent volatility state. The new model is able to effectively describe the conditional distribution dynamics of a term structure variable and can still be readily estimated with the Kalman filter. We apply our framework to model the crude oil futures prices. Using more than 150,000,000 transactions for the large panel of contracts we carefully construct the realized volatility measures corresponding to the latent Nelson-Siegel factors, estimate the model at daily frequency and evaluate it by forecasting the conditional density of futures prices. We document that the time-varying volatility specification suggested in our model strongly outperforms the constant volatility benchmark. In addition, the use of realized measures provides moderate, but systematic gains in density forecasting. In the third chapter, I investigate the rate at which information about the daily asset volatility level arrives with the transaction data in the course of the trading day. The contribution of this analysis is three-fold. First, I gauge how fast (after the market opening) the reasonable projection of the new daily volatility level can be constructed. Second, the framework provides a natural experimental field for the comparison of the small sample properties of different types of estimators as well as their (very) short-run forecasting capability. Finally, I outline an adaptive modeling framework for volatility dynamics that attaches time-varying weights to the different predictive signals in response to the changing stochastic environment. In the empirical analysis, I consider a sample of assets from the Dow Jones index. I find that the average precision of the ex-post daily volatility projections made after only 15 minutes of trading (at 9:45a.m. EST) amounts to 65% (in terms of predictive R2) and reaches up to 90% before noon. Moreover, in conjunction with the prior forecast, the first 15 minutes of trading are able to predict about 80% of the ex-post daily volatility. I document that the predictive content of the realized measures that use data at the transaction frequency is strongly superior as compared to the estimators that use sparsely sampled data, but the difference is getting negligible closer to the end of the trading day, as more observations are used to construct a projection. In the final chapter, joint with Peter Hansen, Guillaume Horel and Asger Lunde, we introduce a multivariate estimator of financial volatility that is based on the theory of Markov chains. The Markov chain framework takes advantage of the discreteness of high-frequency returns and suggests a natural decomposition of the observed price process into a martingale and a stationary components. The new estimator is robust to microstructural noise effects and is positive semidefinite by construction. We outline an approach to the estimation of high dimensional covariance matrices. This approach overcomes the curse of dimensionality caused by the tremendous number of observed price transitions (normally, exceeding 10,000 per trading day) that complicates a reliable estimation of the transition probability matrix for the multivariate Markov chain process. We study the finite sample properties of the estimator in a simulation study and apply it to high-frequency commodity prices. We find that the new estimator demonstrates a decent finite sample precision. The empirical estimates are largely in agreement with the benchmarks, but the Markov chain estimator is found to be particularly well with regards to estimating correlations.
Download or read book Risk Estimation on High Frequency Financial Data written by Florian Jacob and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: 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. Contents Multivariate Standard Normal Tempered Stable Distribution FIGARCH High Frequency Data and Risk Management Target Groups Researchers and students in the field of finance Practitioners in this area The Author Florian Jacob obtained his Master's Degree in Business Engineering from the Karlsruhe Institute of Technology focusing on the application of tempered stable distributions on financial data and financial engineering.
Download or read book Handbook of Modeling High Frequency Data in Finance written by Frederi G. Viens and published by John Wiley & Sons. This book was released on 2011-11-16 with total page 468 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Essays on High frequency Financial Econometrics written by Shouwei Liu and published by . This book was released on 2014 with total page 126 pages. Available in PDF, EPUB and Kindle. Book excerpt: "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.
Download or read book Three Essays in Monetary and Financial Economics written by Liang Ma and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation consists of three essays in the field of monetary and financial economics. Specifically, we use high-frequency financial data to study monetary policies with a focus on the information effect, namely, that some of the interest rate movements around central bank announcements are not policy-driven, but are results of the market becoming aware of the central bank's view about future economic prospects. Understanding the role played by the information effect will help us apprehend monetary policy implications in both normal times and extraordinary situations. Chapter 1 evaluates the impact of unconventional monetary policy in the newly developed instrumental variable structural Vector Autoregression (VAR) framework. In the current low interest rate environment, central banks must resort to using unconventional monetary policies, such as forward guidance and quantitative easing, to flight recessions. To empirically evaluate the effectiveness of these unconventional policies, we need to rely on the clean policy shock. A prominent concern is that the often used high-frequency interest rate surprises not only reflect unexpected policy changes, but also contain the information effect. We contribute to the literature by using a heteroskedasticity identification approach, taking advantage of changes in the relative dominance of economic shocks around different macroeconomic announcements. Analysis based on clean policy shocks suggests that the unconventional policies successfully aided the recovery in the U.S. More importantly, we show that the information effect, while it may introduce bias, is rather modest when it comes to estimating the real impact of unconventional monetary policies. Chapter 2 studies the stock return pattern after the U.S. Federal Open Market Committee (FOMC) announcement. This research is motivated by recent literature that documents stock returns drifts, both before and after FOMC announcements, according to policy rate surprises. Indeed, research has shown that the information contained in the central bank announcement is multifaceted: its current monetary policy stances (monetary policy news) and news about future economic prospects (non-monetary policy news). Our contribution is to combine these two strands of literature. To the best of our knowledge, no study has looked at stock market reactions to the non-monetary news stemming from policy announcements. We identify both good and bad news events using a combination of sign restriction with high-frequency financial prices. The novel finding is that following bad FOMC announcements, that is the market interpreted the Fed announcements as revealing negative information about the economy, we observe significant positive stock returns in a 20-day period. We call this the ``post-FOMC drift.'' Further analysis suggests that the drift is likely caused by relatively heightened risks associated with bad announcements, although the drift is consistent with market overreactions as well. Moreover, the post FOMC drift is a market-wide phenomenon and can be exploited in an easy-to-implement trading strategy with a historical record of earning 40\% of the annual equity premium. In Chapter 3, we explore the channels through which the FOMC announcements affect the financial market. While much of the existing literature measures the surprise components with only changes in policy rates (surrounding the announcement), we contribute to the existing literature by taking a broader view through examining unexpected changes in longer-term yields, corporate credit spreads, and inflation expectations (a proxy for growth prospects), using high-frequency financial data. Through a regression analysis, our findings show that these additional surprises provide orthogonal information and sharply increase the goodness of fit in explaining stock returns around FOMC announcements, with the inclusion of inflation expectations having the biggest contribution. The important role of inflation expectation suggests that the current literature, which uses stock prices together with nominal rates to disentangle the information contents of central bank announcements, may be too limited in the scope of information it uses.
Download or read book Essays on Multivariate Modelling of Financial Markets Using Copula and Sentiment Networks written by Anastasija Tetereva and published by . This book was released on 2018 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multivariate dependence structures play an important role in finance. The modelling and accurate prediction of multivariate financial time series is an important component of asset pricing and portfolio management. This doctoral thesis comprises three essays that address the question of multivariate dependencies using high-frequency data and innovative sources of information such as news analytics. These essays make complementary contributions to the field of financial econometrics and can be read independently of each other. The first essay focuses on the improvement of Value at Risk prediction based on highfrequency data. The novel concept of the realized hierarchical Archimedean copula is introduced. It is proposed estimating the structure and the parameters of the hierarchical Archimedean copula using the realized correlation matrix only. This approach allows one to estimate the multivariate distribution of daily returns based on intraday information. Moreover, the proposed estimator does not suffer from the curse of dimensionality. In this essay, the realized hierarchical Archimedean copula is applied to manage the risk of high-dimensional portfolios. The evidence of the superior forecasting power of our approach, compared to a set of existing models, is provided. The second essay investigates the role of news sentiment data in improving forecasts in financial econometrics. The objective of this paper is to answer the question regarding whether the class of stock-price-relevant news is wider than firm-specific announcements. For this purpose, causal links between news sentiments and excess returns are studied by means of an adaptive lasso. It is concluded that unexpected returns in the whole economy can be explained by news originating from the financial and energy sectors. In other words, the news spillover effects are dominating the direct effects of sectoral news. Therefore, including exogenous financial or energy sentim.
Download or read book Essays in Financial Econometrics Asset Pricing and Corporate Finance written by Markus Pelger and published by . This book was released on 2015 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt: My dissertation explores how tail risk and systematic risk affects various aspects of risk management and asset pricing. My research contributions are in econometric and statistical theory, in finance theory and empirical data analysis. In Chapter 1 I develop the statistical inferential theory for high-frequency factor modeling. In Chapter 2 I apply these methods in an extensive empirical study. In Chapter 3 I analyze the effect of jumps on asset pricing in arbitrage-free markets. Chapter 4 develops a general structural credit risk model with endogenous default and tail risk and analyzes the incentive effects of contingent capital. Chapter 5 derives various evaluation models for contingent capital with tail risk. Chapter 1 develops a statistical theory to estimate an unknown factor structure based on financial high-frequency data. I derive a new estimator for the number of factors and derive consistent and asymptotically mixed-normal estimators of the loadings and factors under the assumption of a large number of cross-sectional and high-frequency observations. The estimation approach can separate factors for normal "continuous" and rare jump risk. The estimators for the loadings and factors are based on the principal component analysis of the quadratic covariation matrix. The estimator for the number of factors uses a perturbed eigenvalue ratio statistic. The results are obtained under general conditions, that allow for a very rich class of stochastic processes and for serial and cross-sectional correlation in the idiosyncratic components. Chapter 2 is an empirical application of my high-frequency factor estimation techniques. Under a large dimensional approximate factor model for asset returns, I use high-frequency data for the S & P 500 firms to estimate the latent continuous and jump factors. I estimate four very persistent continuous systematic factors for 2007 to 2012 and three from 2003 to 2006. These four continuous factors can be approximated very well by a market, an oil, a finance and an electricity portfolio. The value, size and momentum factors play no significant role in explaining these factors. For the time period 2003 to 2006 the finance factor seems to disappear. There exists only one persistent jump factor, namely a market jump factor. Using implied volatilities from option price data, I analyze the systematic factor structure of the volatilities. There is only one persistent market volatility factor, while during the financial crisis an additional temporary banking volatility factor appears. Based on the estimated factors, I can decompose the leverage effect, i.e. the correlation of the asset return with its volatility, into a systematic and an idiosyncratic component. The negative leverage effect is mainly driven by the systematic component, while it can be non-existent for idiosyncratic risk. In Chapter 3 I analyze the effect of jumps on asset pricing in arbitrage-free markets and I show that jumps have to come as a surprise in an arbitrage-free market. I model asset prices in the most general sensible form as special semimartingales. This approach allows me to also include jumps in the asset price process. I show that the existence of an equivalent martingale measure, which is essentially equivalent to no-arbitrage, implies that the asset prices cannot exhibit predictable jumps. Hence, in arbitrage-free markets the occurrence and the size of any jump of the asset price cannot be known before it happens. In practical applications it is basically not possible to distinguish between predictable and unpredictable discontinuities in the price process. The empirical literature has typically assumed as an identification condition that there are no predictable jumps. My result shows that this identification condition follows from the existence of an equivalent martingale measure, and hence essentially comes for free in arbitrage-free markets. Chapter 4 is joint work with Behzad Nouri, Nan Chen and Paul Glasserman. Contingent capital in the form of debt that converts to equity as a bank approaches financial distress offers a potential solution to the problem of banks that are too big to fail. This chapter studies the design of contingent convertible bonds and their incentive effects in a structural model with endogenous default, debt rollover, and tail risk in the form of downward jumps in asset value. We show that once a firm issues contingent convertibles, the shareholders' optimal bankruptcy boundary can be at one of two levels: a lower level with a lower default risk or a higher level at which default precedes conversion. An increase in the firm's total debt load can move the firm from the first regime to the second, a phenomenon we call debt-induced collapse because it is accompanied by a sharp drop in equity value. We show that setting the contractual trigger for conversion sufficiently high avoids this hazard. With this condition in place, we investigate the effect of contingent capital and debt maturity on capital structure, debt overhang, and asset substitution. We also calibrate the model to past data on the largest U.S. bank holding companies to see what impact contingent convertible debt might have had under the conditions of the financial crisis. Chapter 5 develops and compares different modeling approaches for contingent capital with tail risk, debt rollover and endogenous default. In order to apply contingent convertible capital in practice it is desirable to base the conversion on observable market prices that can constantly adjust to new information in contrast to accounting triggers. I show how to use credit spreads and the risk premium of credit default swaps to construct the conversion trigger and to evaluate the contracts under this specification.
Download or read book Three Essays on High Frequency Financial Econometrics and Individual Trading Behavior written by and published by . This book was released on 2008 with total page 398 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Essays in Financial Econometrics written by Christian Nguenang Kapnang and published by . This book was released on 2018 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Challenges in Using High frequency Financial Data in Estimating and Forecasting Return Volatility written by Wenhao Cui and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The availability of high-frequency financial data in the last 20 years has led to a rich literature on its estimation and forecasting. Motivated by the challenges in utilizing high-frequency financial data, we decide to investigate the problem of estimating and forecasting return volatility, taking into account the presence of market microstructure noise, jump, and time endogeneity. With this target in mind, we solve the volatility estimation problem by combining several existing methods with our Laplace estimator of volatility. We also investigate the forecasting problem by employing linear regression models. Furthermore, we apply a standard data cleaning procedure to reduce the potential impact of outliers and errors. After trimming, we are able to draw a robust conclusion across a variety of different linear regression models. The process leads to a better understanding of utilizing high-frequency financial data and its application in volatility forecasting.
Download or read book Handbook of High Frequency Trading and Modeling in Finance written by Ionut Florescu and published by John Wiley & Sons. This book was released on 2016-04-05 with total page 414 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reflecting the fast pace and ever-evolving nature of the financial industry, the Handbook of High-Frequency Trading and Modeling in Finance details how high-frequency analysis presents new systematic approaches to implementing quantitative activities with high-frequency financial data. Introducing new and established mathematical foundations necessary to analyze realistic market models and scenarios, the handbook begins with a presentation of the dynamics and complexity of futures and derivatives markets as well as a portfolio optimization problem using quantum computers. Subsequently, the handbook addresses estimating complex model parameters using high-frequency data. Finally, the handbook focuses on the links between models used in financial markets and models used in other research areas such as geophysics, fossil records, and earthquake studies. The Handbook of High-Frequency Trading and Modeling in Finance also features: • Contributions by well-known experts within the academic, industrial, and regulatory fields • A well-structured outline on the various data analysis methodologies used to identify new trading opportunities • Newly emerging quantitative tools that address growing concerns relating to high-frequency data such as stochastic volatility and volatility tracking; stochastic jump processes for limit-order books and broader market indicators; and options markets • Practical applications using real-world data to help readers better understand the presented material The Handbook of High-Frequency Trading and Modeling in Finance is an excellent reference for professionals in the fields of business, applied statistics, econometrics, and financial engineering. The handbook is also a good supplement for graduate and MBA-level courses on quantitative finance, volatility, and financial econometrics. Ionut Florescu, PhD, is Research Associate Professor in Financial Engineering and Director of the Hanlon Financial Systems Laboratory at Stevens Institute of Technology. His research interests include stochastic volatility, stochastic partial differential equations, Monte Carlo Methods, and numerical methods for stochastic processes. Dr. Florescu is the author of Probability and Stochastic Processes, the coauthor of Handbook of Probability, and the coeditor of Handbook of Modeling High-Frequency Data in Finance, all published by Wiley. Maria C. Mariani, PhD, is Shigeko K. Chan Distinguished Professor in Mathematical Sciences and Chair of the Department of Mathematical Sciences at The University of Texas at El Paso. Her research interests include mathematical finance, applied mathematics, geophysics, nonlinear and stochastic partial differential equations and numerical methods. Dr. Mariani is the coeditor of Handbook of Modeling High-Frequency Data in Finance, also published by Wiley. H. Eugene Stanley, PhD, is William Fairfield Warren Distinguished Professor at Boston University. Stanley is one of the key founders of the new interdisciplinary field of econophysics, and has an ISI Hirsch index H=128 based on more than 1200 papers. In 2004 he was elected to the National Academy of Sciences. Frederi G. Viens, PhD, is Professor of Statistics and Mathematics and Director of the Computational Finance Program at Purdue University. He holds more than two dozen local, regional, and national awards and he travels extensively on a world-wide basis to deliver lectures on his research interests, which range from quantitative finance to climate science and agricultural economics. A Fellow of the Institute of Mathematics Statistics, Dr. Viens is the coeditor of Handbook of Modeling High-Frequency Data in Finance, also published by Wiley.
