Essays on Fine Structure of Asset Returns, Jumps, and Stochastic Volatility PDF Download

Author: Jung-suk Yu
Publisher:
ISBN:
Category :
Languages : en
Pages : 122

View

Book Description

Author: . Jung-Suk Yu
Publisher: LAP Lambert Academic Publishing
ISBN: 9783659392009
Category :
Languages : en
Pages : 128

View

Book Description
The various models have been built upon pioneering work of Robert F. Engle (2003) and Robert C. Merton (1997) for methods of analyzing economic time series with time-varying volatility and a new method to determine the value of derivatives, respectively. This book fills the gaps which Harry M. Markowitz's (1990) mean-variance analysis fails to capture. Especially, this book investigates dynamic processes of asset returns, volatility, and jumps which are time-varying and stochastic in discrete- and continuous-time settings. I demonstrate that these additional computational and modeling efforts provide us with significant benefits to better capture actual financial time-series data and to reduce option pricing errors. If we only consider mean and variance as in Markowitz, most likely we may not fully appreciate recent advances in risk managements, investments, and derivatives pricing since many researchers recognize the importance of economic and statistical roles of skewness and kurtosis. To better explain well-known skewness and excess kurtosis of financial time-series returns, I employ asymmetric fat-tailed distributions such as Hansen's skewed t-distribution and Levy jump models.

Author: Hélyette Geman
Publisher:
ISBN:
Category :
Languages : en
Pages :

View

Book Description
We investigate the importance of diffusion and jumps in a new model for asset returns. In contrast to standard models, we allow for jump components displaying finite or infinite activity and variation. Empirical investigations of time series indicate that index dynamics are devoid of a diffusion component, which may be present in the dynamics of individual stocks. This leads to the conjecture, confirmed on options data, that the risk-neutral process should be free of a diffusion component. We conclude that the statistical and risk-neutral processes for equity prices are pure jump processes of infinite activity and finite variation.

Author: Stefan A. Jacewitz
Publisher:
ISBN:
Category :
Languages : en
Pages :

View

Book Description
This dissertation collects two papers regarding the econometric and economic theory and testing of the predictability of asset returns. It is widely accepted that stock returns are not only predictable but highly so. This belief is due to an abundance of existing empirical literature finding often overwhelming evidence in favor of predictability. The common regressors used to test predictability (e.g., the dividend-price ratio for stock returns) are very persistent and their innovations are highly correlated with returns. Persistence when combined with a correlation between innovations in the regressor and asset returns can cause substantial over-rejection of a true null hypothesis. This result is both well documented and well known. On the other hand, stochastic volatility is both broadly accepted as a part of return time series and largely ignored by the existing econometric literature on the predictability of returns. The severe effect that stochastic volatility can have on standard tests are demonstrated here. These deleterious effects render standard tests invalid. However, this problem can be easily corrected using a simple change of chronometer. When a return time series is read in the usual way, at regular intervals of time (e.g., daily observations), then the distribution of returns is highly non-normal and displays marked time heterogeneity. If the return time series is, instead, read according to a clock based on regular intervals of volatility, then returns will be independent and identically normally distributed. This powerful result is utilized in a unique way in each chapter of this dissertation. This time-deformation technique is combined with the Cauchy t-test and the newly introduced martingale estimation technique. This dissertation finds no evidence of predictability in stock returns. Moreover, using martingale estimation, the cause of the Forward Premium Anomaly may be more easily discerned.

Author: Worapree Maneesoonthorn
Publisher:
ISBN:
Category :
Languages : en
Pages : 604

View

Book Description
Planning for future movements in asset prices and understanding the variation in the return on assets are key to the successful management of investment portfolios. This thesis investigates issues related to modelling both asset return volatility and the large movements in asset prices that may be induced by the events in the general economy, as random processes, with the implications for risk compensation and the prediction thereof being a particular focus. Exploiting modern numerical Bayesian tools, a state space framework is used to conduct all inference, with the thesis making three novel contributions to the empirical finance literature. First, observable measures of physical and option-implied volatility on the S&P 500 market index are combined to conduct inference about the latent spot market volatility, with a dynamic structure specified for the variance risk premia factored into option prices. The pooling of dual sources of information, along with the use of a dynamic model for the risk premia, produces insights into the workings of the U.S. markets, plus yields accurate forecasts of several key variables, including over the recent period of stock market turmoil. Second, a new continuous time asset pricing model allowing for dynamics in, and interactions between, the occurrences of price and volatility jumps is proposed. Various hypotheses about the nature of extreme movements in both S&P 500 returns and the volatility of the index are analyzed, within a state space model in which the usual returns measure is supplemented by direct measures of physical volatility and price jumps. The empirical results emphasize the importance of modelling both types of jumps, with the link between the intensity of volatility jumps and certain key extreme events in the economy being drawn. Finally, an empirical exploration of an alternative framework for the statistical evaluation of price jumps is conducted, with the aim of comparing the resultant measures of return variance and jumps with those induced by more conventional methods. The empirical analysis sheds light on the potential impact of the method of measurement construction on inference about the asset pricing process, and ultimately any financial decisions based on such inference.

Author: Jie Cao
Publisher:
ISBN:
Category :
Languages : en
Pages : 232

View

Book Description
In this dissertation, I explore the impact of idiosyncratic risk on asset returns. The first essay examines how idiosyncratic risk affects the cross-section of stock returns. I use an exponential GARCH model to forecast expected idiosyncratic volatility and employ a combination of the size effect, value premium, return momentum and short-term reversal to measure relative mispricing. I find that stock returns monotonically increase in idiosyncratic risk for relatively undervalued stocks and monotonically decrease in idiosyncratic risk for relatively overvalued stocks. This phenomenon is robust to various subsamples and industries, and cannot be explained by risk factors or firm characteristics. Further, transaction costs, short-sale constraints and information uncertainty cannot account for the role of idiosyncratic risk. Overall, these findings are consistent with the limits of arbitrage arguments and demonstrate the importance of idiosyncratic risk as an arbitrage cost. The second essay studies the cross-sectional determinants of delta-hedged stock option returns with an emphasis on the pricing of volatility risk. We find that the average delta-hedged option returns are significantly negative for most stocks, and they decrease monotonically with both total and idiosyncratic volatility of the underlying stock. Our results are robust and cannot be explained by the Fama-French factors, market volatility risk, jump risk, or the effect of past stock return and volatility-related option mispricing. Our results strongly support a negative market price of volatility risk specification that is proportional to the volatility level. Reflecting this volatility risk premium, writing covered calls on high volatility stocks on average earns about 2% more per month than selling covered calls on low volatility stocks. This spread is higher when it is more difficult to arbitrage between stock and option.

Author: Jun Pan
Publisher:
ISBN:
Category :
Languages : en
Pages : 26

View

Book Description
This paper presents a model for asset returns incorporating both stochastic volatility and jump effects. The return process is driven by two types of randomness: small random shocks and large jumps. The stochastic volatility process is affected by both types of randomness in returns. Specifically, in the absence of large jumps, volatility is driven by the small random shocks in returns through a GARCH(1,1) model, while the occurrence of a jump event breaks the persistence in the volatility process, and resets it to an unknown deterministic level. Model estimation is performed on daily returns of Samp;P~500 index using the maximum-likelihood method. The empirical results are discussed.

Author:
Publisher:
ISBN:
Category : Dissertations, Academic
Languages : en
Pages : 678

View

Book Description

Author: Shu Yan
Publisher:
ISBN:
Category : Stock exchanges
Languages : en
Pages : 310

View

Book Description

Author: Young Il Kim
Publisher:
ISBN:
Category : Assets (Accounting)
Languages : en
Pages : 176

View

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.