Author: Aymard N'Zi KassiPublisher:
ISBN:
Category :
Languages : en
Pages : 94
Book Description
Essays in Modeling of Daily Returns and Realized Volatility
Author: Aymard N'Zi KassiThree Essays on Realized Volatility Models for High-Frequency Data
Author: Ji ShenThree Essays in Financial Econometrics
Author: Gang XuPublisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
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.
Essays on Volatility Risk, Asset Returns and Consumption-based Asset Pricing
Author: Young Il KimPublisher:
ISBN:
Category : Assets (Accounting)
Languages : en
Pages : 176
Book Description
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.
Topics in Modeling Volatility Based on High-frequency Data
Author: Constantin RothPublisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
In the first chapter, I compare the forecasting accuracy of different high-frequency based volatility models. The empirical analysis shows that the HEAVY and the Realized GARCH generally outperform the rest of the models. The inclusion of overnight returns considerably improves volatility forecasts for stocks across all models. Furthermore, the analysis shows that models based on realized volatility benefit much less from allowing leverage effects than do models based on daily returns. In the second chapter, the cause for this observation is investigated more deeply. I explain it by documenting that realized volatility tends to be higher on down-days than on up-days and that a similar asymmetry cannot be found in squared daily returns. I show that leverage effects are present already at high return-frequencies and that these are capable of generating asymmetries in realized variance but not in squared returns. In the third chapter, a conservative test based on the adaptive lasso is applied to investigate the optimal lag structure for modeling realized volatility dynamics. The empirical analysis shows that the optimal significant lag structure is time-varying and subject to drastic regime shifts. The accuracy of the HAR model can be explained by the observation that in many cases the relevant information for prediction is included in the first 22 lags. In the fourth chapter, a wild multiplicative bootstrap is introduced for M- and GMM estimators of time series. In Monte Carlo simulations, the wild bootstrap always outperforms inference which is based on standard asymptotic theory. Moreover, in most cases the accuracy of the wild bootstrap is also higher and more stable than that of the block bootstrap whose accuracy depends heavily on the choice of the block size.
Essays in Nonlinear Time Series Econometrics
Author: Niels HaldrupPublisher: OUP Oxford
ISBN: 0191669547
Category : Business & Economics
Languages : en
Pages : 393
Book Description
This edited collection concerns nonlinear economic relations that involve time. It is divided into four broad themes that all reflect the work and methodology of Professor Timo Teräsvirta, one of the leading scholars in the field of nonlinear time series econometrics. The themes are: Testing for linearity and functional form, specification testing and estimation of nonlinear time series models in the form of smooth transition models, model selection and econometric methodology, and finally applications within the area of financial econometrics. All these research fields include contributions that represent state of the art in econometrics such as testing for neglected nonlinearity in neural network models, time-varying GARCH and smooth transition models, STAR models and common factors in volatility modeling, semi-automatic general to specific model selection for nonlinear dynamic models, high-dimensional data analysis for parametric and semi-parametric regression models with dependent data, commodity price modeling, financial analysts earnings forecasts based on asymmetric loss function, local Gaussian correlation and dependence for asymmetric return dependence, and the use of bootstrap aggregation to improve forecast accuracy. Each chapter represents original scholarly work, and reflects the intellectual impact that Timo Teräsvirta has had and will continue to have, on the profession.
Modelling and forecasting stock return volatility and the term structure of interest rates
Author: Michiel de PooterPublisher: Rozenberg Publishers
ISBN: 9051709153
Category :
Languages : en
Pages : 286
Book Description
This dissertation consists of a collection of studies on two areas in quantitative finance: asset return volatility and the term structure of interest rates. The first part of this dissertation offers contributions to the literature on how to test for sudden changes in unconditional volatility, on modelling realized volatility and on the choice of optimal sampling frequencies for intraday returns. The emphasis in the second part of this dissertation is on the term structure of interest rates.
Essays on the Volatility of Macroeconomic and Financial Time Series
Author: Wei-Choun YuPublisher:
ISBN:
Category : Financial instruments
Languages : en
Pages : 144
Book Description
Essays on Investment Fluctuation and Market Volatility
Author: Chaoqun LaiPublisher:
ISBN:
Category : Electronic dissertations
Languages : en
Pages :
Book Description
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.
Essays in Honor of Subal Kumbhakar
Author: Christopher F. ParmeterPublisher: Emerald Group Publishing
ISBN: 1837978751
Category : Business & Economics
Languages : en
Pages : 401
Book Description
It is the editor’s distinct privilege to gather this collection of papers that honors Subhal Kumbhakar’s many accomplishments, drawing further attention to the various areas of scholarship that he has touched.