Download or read book Essays on Financial Return and Volatility Modeling written by Jing Wu (Ph. D.) and published by . This book was released on 2012 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: My dissertation consists of three essays focusing on modeling financial asset return and volatility. The first essay proposes a threshold GARCH model to describe the regimeswitching in volatility dynamics of financial asset returns. In the threshold model the switching of regimes is triggered by an observable threshold variable, while volatility follows a GARCH process within each regime. This model can be viewed as a special case of the random coefficient GARCH model. We establish theoretical conditions, which ensure that the return process in the threshold model is strictly stationary, as well as conditions for the existence of finite variance and fourth moment. A simulation study is further conducted to examine the finite sample properties of the maximum likelihood estimator. The second essay extends our study of the threshold GARCH model to an empirical application. The empirical results support the use of the threshold variable to identify the regime shifts in the volatility process. Especially when VIX is used as the threshold, we are able to separate the clustering of volatile periods corresponding to various financial crises. According to 5 common measures on forecasting evaluation, the threshold GARCH model provides better volatility forecasts for stocks as well as currency exchange rates. The third essay examines the effect of time structure on the estimation performance of independent component analysis (ICA) models and provides guidance in applying the ICA model to time series data. We compare the performance of the basic ICA model to the time series ICA model in which the cross-autocovariances are used as a measure to achieve independence. We conduct a simulation study to evaluate the time series ICA model under different time structure assumptions about the underlying components that generate financial time series. Moreover, the empirical results support the use of the time series ICA model.
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 in Financial Econometrics written by Gang Xu and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis documents the research and findings in the following three related areas of financial econometrics: The first essay examines whether volatility contains information to predict the likelihood of a price jump during the next trading day. It is motivated by the theoretical model of Bansal & Shaliastovich (2008) who develop a long-run learning model, arguing that market volatility should be able to predict the likelihood of jumps. I use S&P 500 futures prices and extensions of the GARCH jump model of Maheu & McCurdy (2004) to relate jump probabilities to conditional volatility. Since volatility is a latent variable, which can be measured using different variables, I consider predictions based upon squared daily return, at-the-money implied volatility, model-free im- plied volatility and high-frequency realized volatility. I find evidence that volatility can predict jump likelihood and the best predictive variable is the model-free implied volatility: which is constructed using cross-section of option prices. Therefore, this thesis contributes to the current literature by documenting the information efficiency of option prices when predicting the future likelihood of jumps. In addition. I also develop a new approach based on Poisson regression which compares the jump intensity obtained from the GARCH jump model with the intraday jump numbers counted using the method of Andersen et al. (2007b). I find the two measures of jumps match fairly well with each other in the period from 1990 to 1997. However, any such relationship seems to disappear in the later period from 1998 to 2004. The second essay is motivated by the affine jump-diffusion model of Duffie et al. (2000), which allows jump intensity to be an affine function of state variables. I examine whether volatility can predict the intensity of price jumps in stochastic volatility jump models, estimated using Markov Chain Monte Carlo simulation. Comparing implied volatility with high-frequency realized volatility, I find allowing the jump intensity to be an affine function of model-free implied volatility yields the best model, based on either the Deviance Information Criterion or on diagnostic tests. Further comparison are made for candidate AR(l) process which specify the stochastic volatility. I find a jump model with the log variance an AR( 1) process performs better than a jump model with Ornstein-Uhlenbeck stochastic volatility. In a Monte Carlo simulation, I find the Deviance Information Criterion is a reliable criterion to differentiate between competing equity price dynamics when there are price jumps and volatility is stochastic. In addition to examining univariate equity return models, in the third essay I also develop a bivariate equity return model which simultaneously captures time-varying correlation and volatility spillovers in the international equity markets. This model is calibrated using the weekly equity index returns from the US. UK, Germany, India and Brazil stock markets and it is compared with simplier model specifications. I find evidence that supports time varying correlation between equity markets in both developed and developing economics. How- ever, the volatility spillovers mainly exist from US equity returns to equity returns in other economies. This thesis concludes with a short discussion of its limitations and future research directions.
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 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 Forecasting Volatility in the Financial Markets written by Stephen Satchell and published by Elsevier. This book was released on 2011-02-24 with total page 428 pages. Available in PDF, EPUB and Kindle. Book excerpt: Forecasting Volatility in the Financial Markets, Third Edition assumes that the reader has a firm grounding in the key principles and methods of understanding volatility measurement and builds on that knowledge to detail cutting-edge modelling and forecasting techniques. It provides a survey of ways to measure risk and define the different models of volatility and return. Editors John Knight and Stephen Satchell have brought together an impressive array of contributors who present research from their area of specialization related to volatility forecasting. Readers with an understanding of volatility measures and risk management strategies will benefit from this collection of up-to-date chapters on the latest techniques in forecasting volatility. Chapters new to this third edition:* What good is a volatility model? Engle and Patton* Applications for portfolio variety Dan diBartolomeo* A comparison of the properties of realized variance for the FTSE 100 and FTSE 250 equity indices Rob Cornish* Volatility modeling and forecasting in finance Xiao and Aydemir* An investigation of the relative performance of GARCH models versus simple rules in forecasting volatility Thomas A. Silvey Leading thinkers present newest research on volatility forecasting International authors cover a broad array of subjects related to volatility forecasting Assumes basic knowledge of volatility, financial mathematics, and modelling
Download or read book Forecasting Volatility in the Financial Markets written by Stephen Satchell and published by Elsevier. This book was released on 2002-08-22 with total page 417 pages. Available in PDF, EPUB and Kindle. Book excerpt: 'Forecasting Volatility in the Financial Markets' assumes that the reader has a firm grounding in the key principles and methods of understanding volatility measurement and builds on that knowledge to detail cutting edge modelling and forecasting techniques. It then uses a technical survey to explain the different ways to measure risk and define the different models of volatility and return.The editors have brought together a set of contributors that give the reader a firm grounding in relevant theory and research and an insight into the cutting edge techniques applied in this field of the financial markets.This book is of particular relevance to anyone who wants to understand dynamic areas of the financial markets.* Traders will profit by learning to arbitrage opportunities and modify their strategies to account for volatility.* Investment managers will be able to enhance their asset allocation strategies with an improved understanding of likely risks and returns.* Risk managers will understand how to improve their measurement systems and forecasts, enhancing their risk management models and controls.* Derivative specialists will gain an in-depth understanding of volatility that they can use to improve their pricing models.* Students and academics will find the collection of papers an invaluable overview of this field. This book is of particular relevance to those wanting to understand the dynamic areas of volatility modeling and forecasting of the financial marketsProvides the latest research and techniques for Traders, Investment Managers, Risk Managers and Derivative Specialists wishing to manage their downside risk exposure Current research on the key forecasting methods to use in risk management, including two new chapters
Download or read book Essays in Financial Economics written by Mohammadjavad Pakdel and published by . This book was released on 2016 with total page 74 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation consists of two self-contained essays. The first essay compares out-of-sample performance of asset allocation using forward-looking information and backward-looking information. The existing literature processes forward-looking and backward-looking information using different models and consequently different sets of assumptions. Therefore, one might wonder if superior performance of portfolios using these two sources of information should be attributed to superiority of sources of information or superiority of models underlying them. In contrast, this study uses the identical stochastic volatility model to process both forward-looking and backward-looking information. The empirical results of this study show that the investor will be significantly better off when using the forward-looking information in her asset allocation compared to using the backward-looking information. In the second essay, I investigate the relationship between idiosyncratic risk at industry level and stock prices. The Capital Asset Pricing Model (CAPM) predicts that idiosyncratic risk would not be priced by investors, since investors can avoid it through portfolio diversification. In contrast to CAPM's prediction, the authors of existing literature usually conclude that this type of risk is priced by investors at firm level. I hypothesized that risk at industry level, like risk at firm level, is priced by investors. Surprisingly, I found some evidence that net industry-level volatility innovations are contemporaneously positively correlated to respective industry excess returns in some industries. This positive relation is interpreted as lower prices for industries with higher idiosyncratic risk, in contrast to my assumption.
Download or read book Essays on Investment Fluctuation and Market Volatility written by Chaoqun Lai and published by . This book was released on 2008 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation includes two different groups of objects in macroeconomics and financial economics. In macroeconomics, the aggregate investment fluctuation and its relation to an individual firm's behavior have been extensively studied for the past three decades. Most studies on the interdependence behavior of firms' investment focus on the key issue of separating a firm's reaction to others' behavior from reaction to common shocks. However, few researchers have addressed the issue of isolating this endogenous effect from a statistical and econometrical approach. The first essay starts with a comprehensive review of the investment fluctuation and firms' interdependence behavior, followed by an econometric model of lumpy investments and an analysis of the binary choice behavior of firms' investments. The last part of the first essay investigates the unique characteristics of the Italian economy and discusses the economic policy implications of our research findings. We ask a similar question in the field of financial economics: Where does stock market volatility come from? The literature on the sources of such volatility is abundant. As a result of the availability of high-frequency financial data, attention has been increasingly directed at the modeling of intraday volatility of asset prices and returns. However, no empirical research of intraday volatility analysis has been applied at both a single stock level and industry level in the food industry. The second essay is aimed at filling this gap by modeling and testing intraday volatility of asset prices and returns. It starts with a modified High Frequency Multiplicative Components GARCH (Generalized Autoregressive Conditional Heteroscedasticity) model, which breaks daily volatility into three parts: daily volatility, deterministic intraday volatility, and stochastic intraday volatility. Then we apply this econometric model to a single firm as well as the whole food industry using the Trade and Quote Data and Center for Research in Security Prices data. This study finds that there is little connection between the intraday return and overnight return. There exists, however, strong evidence that the food recall announcements have negative impacts on asset returns of the associated publicly traded firms.
Download or read book Essays on Models for Financial Volatility written by Mihaela Oana Craioveanu and published by . This book was released on 2008 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
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 Financial Volatility and Correlation written by George Christodoulakis and published by . This book was released on 2001 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Long Memory in Economics written by Gilles Teyssière and published by Springer Science & Business Media. This book was released on 2006-09-22 with total page 394 pages. Available in PDF, EPUB and Kindle. Book excerpt: Assembles three different strands of long memory analysis: statistical literature on the properties of, and tests for, LRD processes; mathematical literature on the stochastic processes involved; and models from economic theory providing plausible micro foundations for the occurrence of long memory in economics.
Download or read book Nonlinear Economic Dynamics and Financial Modelling written by Roberto Dieci and published by Springer. This book was released on 2014-07-26 with total page 384 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book reflects the state of the art on nonlinear economic dynamics, financial market modelling and quantitative finance. It contains eighteen papers with topics ranging from disequilibrium macroeconomics, monetary dynamics, monopoly, financial market and limit order market models with boundedly rational heterogeneous agents to estimation, time series modelling and empirical analysis and from risk management of interest-rate products, futures price volatility and American option pricing with stochastic volatility to evaluation of risk and derivatives of electricity market. The book illustrates some of the most recent research tools in these areas and will be of interest to economists working in economic dynamics and financial market modelling, to mathematicians who are interested in applying complexity theory to economics and finance and to market practitioners and researchers in quantitative finance interested in limit order, futures and electricity market modelling, derivative pricing and risk management.
Download or read book Essays on Financial Asset Return Volatility written by Peilan Zhou and published by . This book was released on 2007 with total page 230 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Essays on Volatility Risk Asset Returns and Consumption based Asset Pricing written by Young Il Kim and published by . This book was released on 2008 with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: My dissertation addresses two main issues regarding asset returns: econometric modeling of asset returns in chapters 2 and 3 and puzzling features of the standard consumption-based asset pricing model (C-CAPM) in chapters 4 and 5. Chapter 2 develops a new theoretical derivation for the GARCH-skew-t model as a mixture distribution of normal and inverted-chi-square in order to represent the three important stylized facts of financial data: volatility clustering, skewness and thick-tails. The GARCH-skew-t is same as the GARCH-t model if the skewness parameter is shut-off. The GARCH-skew-t is applied to U.S. excess stock market returns, and the equity premium is computed based on the estimated model. It is shown that skewness and kurtosis can have significant effect on the equity premium and that with sufficiently negatively skewed distribution of the excess returns, a finite equity premium can be assured, contrary to the case of the Student t in which an infinite equity premium arises. Chapter 3 provides a new empirical guidance for modeling a skewed and thick-tailed error distribution along with GARCH effects based on the theoretical derivation for the GARCH-skew-t model and empirical findings on the Realized Volatility (RV) measure, constructed from the summation of higher frequency squared (demeaned) returns. Based on an 80-year sample of U.S. daily stock market returns, it is found that the distribution of monthly RV conditional on past returns is approximately the inverted-chi-square while monthly market returns, conditional on RV and past returns are normally distributed with RV in both mean and variance. These empirical findings serve as the building blocks underlying the GARCH-skew-t model. Thus, the findings provide a new empirical justification for the GARCH-skew-t modeling of equity returns. Moreover, the implied GARCH-skew-t model accurately represents the three important stylized facts for equity returns. Chapter 4 provides a possible solution to asset return puzzles such as high equity premium and low riskfree rate based on parameter uncertainty. It is shown that parameter uncertainty underlying the data generating process can lead to a negatively skewed and thick-tailed distribution that can explain most of the high equity premium and low riskfree rate even with the degree of risk aversion below 10 in the CRRA utility function. Chapter 5 investigates a possible link between stock market volatility and macroeconomic risk. This chapter studies why U.S. stock market volatility has not changed much during the "great moderation" era of the 1980s in contrast to the prediction made by the standard C-CAPM. A new model is developed such that aggregate consumption is decomposed into stock and non-stock source of income so that stock dividends are a small part of consumption. This new model predicts that the great moderation of macroeconomic risk must have originated from declining volatility of shocks to the relatively large non-stock factor of production while shocks to the relatively small stock assets have been persistently volatile during the moderation era. Furthermore, the model shows that the systematic risk of holding equity is positively associated with the stock share of total wealth.
