Download or read book Essays on Multivariate Volatility Models written by Trung Thanh Le and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis is an empirical study of how multivariate models can be applied to analyze the dependence between emerging financial markets and the US financial market. This thesis comprises of 3 complete papers which will use this data set as follows. The first paper is an comparative research on estimations and evaluations of 54 individual volatility models which belong to 10 different model classes being the Riskmetrics models, the Constant model (CCC), the Orthogonal-GARCH model (O-GARCH), the Dynamic Conditional Correlation model (DCC), the Asymmetric DCC model (ADCC), the Consistent DCC model (CDCC) and the Student's t-DCC model (TDCC). All of these models were estimated and then ranked by using both in-sample and out of sample performances. This research is to emphasize the importance of model selection in modeling the volatility of financial time series from emerging financial markets. The second paper uses the TDCC model which performed relatively well among the 54 volatility of financial time series from emerging financial markets. The second paper uses the TDCC model which performed relatively well among the 54 volatility models to analyze the volatilities and correlations of the emerging markets. Specifically, the pair-wise conditional correlations between each of the emerging markets and the US market, generated by the TDCC model, were used to perform empirical tests for the contagion of the 3 recent financial crises which are the Dotcom crisis in 2000, the Sub-prime in 2007-2008 and the Global financial crisis in 2008-2009. The use of the TDCC model which assumes a Student's t-distribution is greatly meaningful for the empirical tests for contagion as it deals with the fat-tailed behaviours of the financial data. The third paper is the application of multivariate copula, which provides a connection between the univariate distributions and the multivariate distribution inside the DCC model, to analyze the emerging data. The flexibility of the copula model that separates the multivariate distribution assumption from those univariate series allows us to have an efficient examination of the dependence structure of emerging financial markets. Following success of the copula models in recent studies, our research, which is the first to use the copula model to analyze high-dimensional data, confirms a significant improvement of the copula from the standard DCC model.

Download or read book Essays on Multivariate Volatility and Dependence Models for Financial Time Series written by Diaa Noureldin and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Essays on Multivariate Stochastic Volatility Models written by Sebastian Trojan and published by . This book was released on 2015 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first essay describes a very general stochastic volatility (SV) model specification with leverage, heavy tails, skew and switching regimes, using realized volatility (RV) as an auxiliary time series to improve inference on latent volatility. The information content of the range and of implied volatility using the VIX index is also analyzed. Database is the S & P 500 index. Asymmetry in the observation error is modeled by the generalized hyperbolic skew Student-t distribution, whose heavy and light tail enable substantial skewness. Resulting number of regimes and dynamics differ dependent on the auxiliary volatility proxy and are investigated in-sample for the financial crash period 2008/09 in more detail. An out-of-sample study comparing predictive ability of various model variants for a calm and a volatile period yields insights about the gains on forecasting performance from different volatility proxies. Results indicate that including RV or the VIX pays off mostly in more volatile market conditions, whereas in calmer environments SV specifications using no auxiliary series outperform. The range as volatility proxy provides a superior in-sample fit, but its predictive performance is found to be weak. The second essay presents a high frequency stochastic volatility model. Price duration and associated absolute price change in event time are modeled contemporaneously to fully capture volatility on the tick level, combining the SV and stochastic conditional duration (SCD) model. Estimation is with IBM stock intraday data 2001/10 (decimalization completed), taking a minimum midprice threshold of a half tick. Persistent information flow is extracted, featuring a positively correlated innovation term and negative cross effects in the AR(1) persistence matrix. Additionally, regime switching in both duration and absolute price change is introduced to increase nonlinear capabilities of the model. Thereby, a separate price jump.

Download or read book Essays on Multivariate Stochastic Volatility Models Using Wishart Processes written by Yu-Cheng Ku and published by . This book was released on 2010 with total page 87 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Volatility and Time Series Econometrics written by Tim Bollerslev and published by OUP Oxford. This book was released on 2010-02-11 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robert Engle received the Nobel Prize for Economics in 2003 for his work in time series econometrics. This book contains 16 original research contributions by some the leading academic researchers in the fields of time series econometrics, forecasting, volatility modelling, financial econometrics and urban economics, along with historical perspectives related to field of time series econometrics more generally. Engle's Nobel Prize citation focuses on his path-breaking work on autoregressive conditional heteroskedasticity (ARCH) and the profound effect that this work has had on the field of financial econometrics. Several of the chapters focus on conditional heteroskedasticity, and develop the ideas of Engle's Nobel Prize winning work. Engle's work has had its most profound effect on the modelling of financial variables and several of the chapters use newly developed time series methods to study the behavior of financial variables. Each of the 16 chapters may be read in isolation, but they all importantly build on and relate to the seminal work by Nobel Laureate Robert F. Engle.

Download or read book Volatility and Time Series Econometrics written by Mark Watson and published by Oxford University Press. This book was released on 2010-02-11 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: A volume that celebrates and develops the work of Nobel Laureate Robert Engle, it includes original contributions from some of the world's leading econometricians that further Engle's work in time series economics

Download or read book Three Essays on Modeling Conditional Correlation written by Kevin Sheppard and published by . This book was released on 2004 with total page 384 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Essays on Volatility Models Using EMM Estimation written by Ying Gu and published by . This book was released on 2006 with total page 155 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Essays on Causality and Volatility in Econometrics with Financial Applications written by Hui Jun Zhang and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "This thesis makes contributions to the statistical analysis of causality and volatility in econometrics. It consists of five essays, theoretical and empirical. In the first one, we study how to characterize and measure multi-horizon second-order causality. The second and third essays propose linear estimation methods for univariate and multivariate weak GARCH models. In the fourth essay, we use multi-horizon causality measures to study the causal relationships between commodity prices and exchange rates with high-frequency data. In the fifth essay, we evaluate the historical evolution of volatility forecast skill.Given the increasingly important role of volatility forecasting in financial studies, a number of authors have proposed to extend the notion of Granger causality to study the dynamic cobehavior of volatilities. In the first essay, we propose a general theory of second-order causality between random vectors at different horizons, allowing for the presence of auxiliary variables, in terms of the predictability of conditional variance. We establish various properties of the causality structures so defined. Furthermore, we propose nonparametric and parametric measures of second-order causality at a given horizon. We suggest a simulation-based method to evaluate the measures in the context of stationary VAR-MGARCH. The asymptotic validity of bootstrap confidence intervals is demonstrated. Finally, we apply the proposed measures of second-order causality to study volatility spillover and contagion across financial markets in the U.S., the U.K. and Japan, for the period of 2000-2010.It is well known that the quasi-maximum likelihood (QML) estimator is consistent and asymptotically normal for (semi-)strong GARCH models. However, when estimating a weak GARCH model, the QML estimator can be inconsistent due to the misspecification of conditional variance. The nonlinear least squares (NLS) estimation is consistent and asymptotically normal for weak GARCH models, but requires a complicated nonlinear optimization. In the second essay, we suggest a linear estimation method, which is shown to be consistent and asymptotically normal for weak GARCH models. Simulation results for weak GARCH models indicate that, the linear estimation method outperforms both QML and NLS for parameter estimation, and is comparable to the NLS, and better than QML for out-of-sample forecasts.Similar issues show up when QML and NLS are used for weak multivariate GARCH (MGARCH) models. In the third essay, we propose a linear estimation method for weak MGARCH models. The asymptotic properties of this linear estimator are established. Simulations for weak MGARCH models show that our linear estimation method outperforms both QML and NLS for the parameter estimation, and the three methods perform similarly in out-of-sample forecasting experiments. Most importantly, the proposed linear estimation is much less computationally complex than QML and NLS. In the fourth essay, we study the causal relationship between commodity prices and exchange rates. Existing studies using quarterly data and noncausality tests only at horizon 1 do not indicate a clear direction of causality from commodity prices to exchange rates. In contrast, by considering multi-horizon causality measures using the high-frequency data (daily and 5-minute) from three typical commodity economies, we find that causality running from commodity prices to exchange rates is stronger than that in the opposite direction up to multiple horizons, after accounting for "dollar effects".In the fifth essay, we apply the concept of forecast skill to evaluate the historical evolution of volatility forecasting, using the data from S&P 500 composite index over the period of 1983-2009. We find that models of conditional volatility do yield improvements in forecasting, but the historical evolution of volatility forecast skill does not exhibit a clear upward trend." --

Download or read book Essays on Forecasting the Multivariate Variance covariance Matrix written by Robert O'Neill and published by . This book was released on 2011 with total page 138 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Essays in Honor of Cheng Hsiao written by Dek Terrell and published by Emerald Group Publishing. This book was released on 2020-04-15 with total page 472 pages. Available in PDF, EPUB and Kindle. Book excerpt: Including contributions spanning a variety of theoretical and applied topics in econometrics, this volume of Advances in Econometrics is published in honour of Cheng Hsiao.

Download or read book Essays in Volatility and Risk Modelling in Interest Rate Swaps written by A.S.M. Sohel Azad and published by . This book was released on 2011 with total page 434 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis focuses on the linkages between volatility of interest rate swaps (hereafter, IRS) and macroeconomic risk, cross-border linkages of swap markets from two-factor volatility models, and the influence of three additional risk factors on swap spreads. In order to investigate these, the thesis presents three empirical research essays that all revolve around a common theme: volatility and risk modelling in interest rate swaps. First research essay, presented in Chapter 3, explores whether and how the volatility of swap yield curves is related to macroeconomic risk. The methodology in this essay is based on a recent Spline-GARCH model, multivariate regression, principal component analysis and Granger causality. The empirical analysis is conducted on a sample of daily data for the period between 1987 and 2010 from three major swap markets namely, Japan, the UK and the US. The empirical analysis reveals two important findings. First, using "low-frequency" volatility extracted from aggregate volatility shocks of the three swap markets the analysis suggests that this low-frequency IRS volatility has strong and (mostly) positive association with most of the macroeconomic risk proxies. This relationship between the macroeconomic risks and IRS volatility varies slightly across the different swap maturities but is robust to alternative volatility specifications, namely C-GARCH model and model-free realized volatility. This finding is fairly consistent with the argument that the greater the macroeconomic risk the greater is the use of derivative instruments to hedge or speculate. Second, to explore the dynamic interaction including lead-lag relationship, the study finds that it is the low-frequency (IRS) volatility that Granger causes most of the macroeconomic risk proxies. This finding is, nonetheless, consistent with the argument that, as forward looking instrument, IRS has predictive power to forecast the changes in macroeconomic risk. Motivated by these findings, an empirical analysis is done on reverse regression in which macroeconomic risk proxies and their principal components are regressed on low-frequency volatility of swaps. The findings are encouraging for those who would like to use swaps in predicting macroeconomic risk. Second research essay, presented in Chapter 4, explores whether the observed relationship between macroeconomic risk proxies and volatility of swap market can be extended to investigate the cross-border linkages of swap markets. The mixed and inconclusive evidence on volatility transmission and swap market integration motivated this essay to investigate this issue from different approach. In particular, using the decomposed volatilities (long-term and short-term), this essay examines the financial integration and volatility linkages of three major swap markets, namely Japan, the UK and the US. To facilitate empirical investigation, a step-by-step approach is proposed in measuring volatility transmission and financial linkages including dynamic correlations, contagion and causality of volatility components. These findings have important implications for portfolio risk diversifications in swaps.Third research essay, presented in Chapter 5, exploits the puzzle with regard to determinants and components of swap spreads. This essay argues that in addition to default risk and liquidity risk, three risk factors namely, business cycle risk, market skewness risk and correlation risk contain significant information in determining the swap spreads. Using the GMM approach, this essay provides empirical support of risk premia related to these three risk factors in addition to default and liquidity risk premia. The results are robust to sub-sample analysis.

Download or read book Essays on Stochastic Volatility and Jumps written by Diep Ngoc Duong and published by . This book was released on 2013 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation comprises three essays on financial economics and econometrics. The first essay outlines and expands upon further testing results from Bhardwaj, Corradi and Swanson (BCS: 2008) and Corradi and Swanson (2011). In particular, specification tests in the spirit of the conditional Kolmogorov test of Andrews (1997) that rely on block bootstrap resampling methods are first discussed. We then broaden our discussion from single process specification testing to multiple process model selection by discussing how to construct predictive densities and how to compare the accuracy of predictive densities derived from alternative (possibly misspecified) diffusion models. In particular, we generalize simulation steps outlined in Cai and Swanson (2011) to multifactor models where the number of latent variables is larger than three. In the second essay, we begin by discussing important developments in volatility modeling, with a focus on time varying and stochastic volatility as well as the "model free" estimation of volatility via the use of so-called realized volatility, and variants thereof called realized measures. In an empirical investigation, we use realized measures to investigate the role of "small" and large" jumps in the realized variation of stock price returns and show that jumps do matter in the relative contribution to the total variation of the process, when examining individual stock returns, as well as market indices. The third essay examines the predictive content of a variety of realized measures of jump power variations, all formed on the basis of power transformations of instantaneous returns. Our prediction involves estimating members of the linear and nonlinear extended Heterogeneous Autoregressive of the Realized Volatility (HAR-RV) class of models, using S & P 500 futures data as well as stocks in the Dow 30, for the period 1993-2009. Our findings suggest that past "large" jump power variations help less in the prediction of future realized volatility, than past "small" jump power variations. Our empirical findings also suggest that past realized signed jump power variations, which have not previously been examined in this literature, are strongly correlated with future volatility.

Download or read book Three Essays on Realized Volatility Models for High Frequency Data written by Ji Shen and published by . This book was released on 2017 with total page 105 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Three Essays on Continuous time Stochastic Volatility Models written by Lu Feng and published by . This book was released on 1996 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Essays on Stochastic Volatility Models with Jump Clustering written by Jian Chen and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Essays on Financial Econometrics written by Juri Marcucci and published by . This book was released on 2005 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation contains three self-contained chapters dealing with volatility modeling and forecasting. In the first chapter we compare a set of standard GARCH models with a group of Markov Regime-Switching GARCH (MRS-GARCH) in terms of their ability to forecast the US stock market volatility at horizons that range from one day to one month. The empirical analysis demonstrates that MRS-GARCH models do really outperform all standard GARCH models in forecasting volatility at horizons shorter than one week. In particular, all tests reject the presence of a better model than the MRS-GARCH with normal innovations. However, at forecast horizons longer than one week, standard asymmetric GARCH models tend to be superior. In chapter 2 a new model to analyze the comovements in the volatilities of a portfolio is proposed. The Pure Variance Common Features model is a factor model for the conditional variances of a portfolio of assets, designed to isolate a small number of variance features that drive all assets' volatilities. It decomposes the conditional variance into a short-run idiosyncratic component (a low-order ARCH process) and a long-run component (the variance factors). An empirical example provides evidence that models with very few variance features perform well in capturing the long-run common volatilities of the equity components of the Dow Jones. In the third and last chapter we compare standard univariate models and multivariate factor models in terms of their ability to forecast the realized variances of a group of major international stock exchanges. Our results show that those models adopting equally weighted regional factors outperform all the others. In addition, models that use factors obtained from canonical correlation analysis tend to outperform all the others that employ different multivariate techniques, therefore confirming their predicting power.