Three Essays on Using High Frequency Data in Estimating Financial Risks PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Three Essays on Using High Frequency Data in Estimating Financial Risks PDF full book. Access full book title Three Essays on Using High Frequency Data in Estimating Financial Risks by Lidan Grossmass. Download full books in PDF and EPUB format.
Author: Lidan Grossmass
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Get Book Here
Book Description
Author: Lidan Grossmass
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Get Book Here
Book Description
Author: Serge Luther Nyawa Womo
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Get Book Here
Book Description
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: Valeri Voev
Publisher:
ISBN:
Category :
Languages : en
Pages : 222
Get Book Here
Book Description
Author: Maria Pacurar
Publisher:
ISBN:
Category : Monte Carlo method
Languages : en
Pages : 0
Get Book Here
Book Description
Author: Yingjie Dong
Publisher:
ISBN:
Category : Econometrics
Languages : en
Pages : 137
Get Book Here
Book Description
"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.
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
Author: Ilya Archakov
Publisher:
ISBN:
Category :
Languages : en
Pages : 173
Get Book Here
Book Description
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.
Author: Florian Jacob
Publisher:
ISBN: 9783658093907
Category :
Languages : en
Pages :
Get Book Here
Book Description
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.
Author: Frederi G. Viens
Publisher: John Wiley & Sons
ISBN: 1118204565
Category : Business & Economics
Languages : en
Pages : 468
Get Book Here
Book Description
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: Shouwei Liu
Publisher:
ISBN:
Category : Options (Finance)
Languages : en
Pages : 126
Get Book Here
Book Description
"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.
