Three Essays in the Financial Economics of Conditional Volatility PDF Download
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Author: J. Liu
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Category :
Languages : en
Pages :
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Author: J. Liu
Publisher:
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Languages : en
Pages :
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Author: Jingyi Liu (Ph.D.)
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Category :
Languages : en
Pages : 279
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Author: Gang Xu
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Category :
Languages : en
Pages :
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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.
Author: Diep Ngoc Duong
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Category : Econometrics
Languages : en
Pages : 184
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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.
Author: George Panayotov
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Category : Options (Finance)
Languages : en
Pages :
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Author: Christian Mücher
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Category :
Languages : en
Pages : 0
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Author: Edison Guozhu Yu
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Category :
Languages : en
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This dissertation consists of three essays. The first essay, entitled "Dynamic Market Participation and Endogenous Information Aggregation", studies information aggregation in financial markets with recurrent investor exit and entry. The paper considers a dynamic general equilibrium model of asset trading with private information and collateral constraints. Investors differ in their aversion to Knightian uncertainty: when uncertainty is high, some investors exit the market. Since exiting investors' information is not fully revealed by prices, conditional return volatility and risk premia both increase. I use data on institutional investors' holdings of individual stocks to show that investor exit rates indeed comove with return volatility and help forecast it. The model also implies that exit is more likely when wealth is more concentrated in the hands of less uncertainty averse investors. The model thus predicts more exit toward the end of a long boom, as seen in the data. Moreover, economies with looser collateral constraints should see more volatility due to exit and partial revelation. The second essay, entitled "The (Un)importance of Mobility in the Great Recession", is based on a paper co-authored with Siddharth Kothari and Itay Saporta-Eksten. Unemployment during and after the Great Recession has been persistently high. One concern is that the housing bust reduced mobility and prevented workers from moving for jobs. The paper characterizes flows out of unemployment that are related to mobility to construct an upper bound on the effect of mobility on unemployment between 2007 and 2012. The effect of mobility is always small: Using pre-recession mobility rates, decreased mobility can account for only an 11 basis points increase in the unemployment rate over the period. Using dynamics of renter mobility in this period to calculate homeowner counterfactual mobility, can account for an 8 basis points increase. Using the highest mobility rate observed in the data, reduced mobility accounts for only a 34 basis points increase in the unemployment rate. The third essay, entitled "Long-term Bonds in a Housing Model", looks into a housing model where mortgages are modeled as a long-term bond. Most house purchases in the US are financed through a mortgage with maturity between 15 and 30 years. This essay studies house price dynamics when modeling mortgages as long-term bonds instead of the more standard one-period bond. With this new feature in the model, results show that the equilibrium price-rent ratio and mortgages borrowing are much less sensitive to changes in the interest rates. In addition, the model can generate negative equity, which matches the presence of negative equity in the housing market downturn in data.
Author: Eric Neis
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Category : Municipal bonds
Languages : en
Pages : 618
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Author: Chaoqun Lai
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Category : Electronic dissertations
Languages : en
Pages :
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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.
Author: Peilin Hsieh
Publisher:
ISBN:
Category :
Languages : en
Pages : 318
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My dissertation focuses on economic studying of volatility issues. Three essays are contained in my dissertation. Essay 1 extends a microstructure model to explain the change of volatility and thus links traders' belief to the volatility change. Our model shows that when market is more uncertain about the value of the stock, the higher the (return) volatility. Essay 2 turns to explore more economic factors that could cause volatility regime switch. We find that US stock return processes, including drift, diffusion, and jump, differ along with US political cycle. Our results imply that the presidency in different parties has distinct policy making processes and thus influence the way information flows into the market, altering the return processes. In the final essay, we document and explain a volatility Bid-Ask spread pattern that increases as time to maturity decreases. Our research develops a model that explains the volatility spread pattern. We show that, as time passes, the required hedging uncertainty premium charged by the liquidity providers decays more slowly while the premium contained in the quoted options price decays at an increasingly higher rate which is determined by the option pricing model. Therefore, liquidity providers need to increase asking and decrease bidding volatility to maintain the profit necessary to compensate slowly decaying hedging uncertainty premium. Our results strongly suggest that studies on volatility spread should detrend the data to make the estimation models correct as well as the series stationary. Without adjusting the trend and autocorrelation problems, statistical results are inaccurate and misleading. More importantly, based on our theoretical model, we also find that: (a) the implied volatility spread does not increase in proportion to the increase of implied volatility, and (b) the increase of volatility uncertainty is not a sufficient condition for an increase in the percentage spread. Finally, to augment the validity of our claims, we provide rigorous econometric tests which support our propositions.
