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Author: Peter Christoffersen Publisher: ISBN: Category : Languages : en Pages : 39
Book Description
Most recent empirical option valuation studies build on the affine square root (SQR) stochastic volatility model. The SQR model is a convenient choice, because it yields closed-form solutions for option prices. However, relatively little is known about the resulting biases. We investigate alternatives to the SQR model, by comparing its empirical performance with that of five different but equally parsimonious stochastic volatility models. We provide empirical evidence from three different sources. We first use realized volatilities to assess the properties of the SQR model and to guide us in the search for alternative specifications. We then estimate the models using maximum likelihood on Samp;P 500 returns. Finally, we employ nonlinear least squares on a panel of option data. In comparison with earlier studies that explicitly solve the filtering problem, we analyze a more comprehensive option data set. The scope of our analysis is feasible because of our use of the particle filter. The three sources of data we employ all point to the same conclusion: the SQR model is misspecified. Overall, the best of the alternative volatility specifications is a model with linear rather than square root diffusion for variance which we refer to as the VAR model. This model captures the stylized facts in realized volatilities, it performs well in fitting various samples of index returns, and it has the lowest option implied volatility mean squared errors in- and out-of-sample.
Author: Peter Christoffersen Publisher: ISBN: Category : Languages : en Pages : 39
Book Description
Most recent empirical option valuation studies build on the affine square root (SQR) stochastic volatility model. The SQR model is a convenient choice, because it yields closed-form solutions for option prices. However, relatively little is known about the resulting biases. We investigate alternatives to the SQR model, by comparing its empirical performance with that of five different but equally parsimonious stochastic volatility models. We provide empirical evidence from three different sources. We first use realized volatilities to assess the properties of the SQR model and to guide us in the search for alternative specifications. We then estimate the models using maximum likelihood on Samp;P 500 returns. Finally, we employ nonlinear least squares on a panel of option data. In comparison with earlier studies that explicitly solve the filtering problem, we analyze a more comprehensive option data set. The scope of our analysis is feasible because of our use of the particle filter. The three sources of data we employ all point to the same conclusion: the SQR model is misspecified. Overall, the best of the alternative volatility specifications is a model with linear rather than square root diffusion for variance which we refer to as the VAR model. This model captures the stylized facts in realized volatilities, it performs well in fitting various samples of index returns, and it has the lowest option implied volatility mean squared errors in- and out-of-sample.
Author: Garland Durham Publisher: ISBN: Category : Languages : en Pages : 61
Book Description
Understanding both the dynamics of volatility as well as the shape of the distribution of returns conditional on the volatility state are important for many financial applications. A simple single-factor SV model appears to be sufficient to capture most of the dynamics; it is the shape of the conditional distribution that is the problem. This paper examines the idea of modeling this distribution as a discrete mixture of normals. The flexibility of this class of distributions provides a transparent look into the tails of the returns distribution. Model diagnostics suggest that the model, SV-mix, does a good job of capturing the salient features of the data. In a direct comparison against several affine-jump models, SV-mix is strongly preferred by Akaike and Schwarz information criteria.
Author: Carl Chiarella Publisher: ISBN: Category : Languages : en Pages : 25
Book Description
This paper seeks to model the adjustment process in the stock market by a continuous time state space model focusing on input-out relations. The value of the Samp;P 500 is generated as the output of the model with earnings and the interest rate as input. The model is found to fit the data well, and indicates that the stock price dynamics can be considered as a price-following-value process. The value determines the time varying trend of price, and random buy-sell pressure drives price fluctuations about value. The 1987 stock price bubble shows up clearly as a gap between price and value.
Author: Andrew C. Harvey Publisher: Cambridge University Press ISBN: 1107034728 Category : Business & Economics Languages : en Pages : 281
Book Description
The volatility of financial returns changes over time and, for the last thirty years, Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models have provided the principal means of analyzing, modeling and monitoring such changes. Taking into account that financial returns typically exhibit heavy tails - that is, extreme values can occur from time to time - Andrew Harvey's new book shows how a small but radical change in the way GARCH models are formulated leads to a resolution of many of the theoretical problems inherent in the statistical theory. The approach can also be applied to other aspects of volatility. The more general class of Dynamic Conditional Score models extends to robust modeling of outliers in the levels of time series and to the treatment of time-varying relationships. The statistical theory draws on basic principles of maximum likelihood estimation and, by doing so, leads to an elegant and unified treatment of nonlinear time-series modeling.
Author: Hanxue Yang Publisher: ISBN: Category : Languages : en Pages : 34
Book Description
Relatively little is known about the empirical performance of infinite-activity Levy jump models, especially with non-affine volatility dynamics. We use extensive empirical data sets to study how infinite-activity Variance Gamma and Normal Inverse Gaussian jumps with affine and non-affine volatility dynamics improve goodness of fit and option pricing performance. With Markov Chain Monte Carlo, different model specifications are estimated using the joint information of the S&P 500 index and the VIX. Our paper provides clear evidence that a parsimonious non-affine model with Normal Inverse Gaussian return jumps and a linear variance specification is particularly competitive, even during the recent crisis.
Author: Sílvia Gonçalves Publisher: ISBN: Category : Languages : en Pages :
Book Description
One key stylized fact in the empirical option pricing literature is the existence of an implied volatility surface (IVS). The usual approach consists of fitting a linear model linking the implied volatility to the time to maturity and the moneyness, for each cross section of options data. However, recent empirical evidence suggests that the parameters characterizing the IVS change over time. In this paper, we study whether the resulting predictability patterns in the IVS coefficients may be exploited in practice. We propose a two-stage approach to modeling and forecasting the Samp;P 500 index options IVS. In the first stage, we model the surface along the cross-sectional moneyness and time-to-maturity dimensions, similarly to Dumas, et. al., (1998). In the second-stage, we model the dynamics of the cross-sectional first-stage implied volatility surface coefficients by means of vector autoregression models. We find that not only the Samp;P 500 implied volatility surface can be successfully modeled, but also that its movements over time are highly predictable in a statistical sense. We then examine the economic significance of this statistical predictability with mixed findings. Whereas profitable delta-hedged positions can be set up that exploit the dynamics captured by the model under moderate transaction costs and when trading rules are selective in terms of expected gains from the trades, most of this profitability disappears when we increase the level of transaction costs and trade multiple contracts off wide segments of the IVS. This suggests that predictability of the time-varying Samp;P 500 implied volatility surface may be not inconsistent with market efficiency.
Author: Eric Zivot Publisher: Springer Science & Business Media ISBN: 9780387955490 Category : Business & Economics Languages : en Pages : 648
Book Description
The field of financial econometrics has exploded since the early 1990s. This book represents an integration of theory, methods and examples using the S-PLUS statistical modeling language and the S+FinMetrics module to facilitate the practice of financial econometrics. It shows the power of S-PLUS for the analysis of time series data. It is written for researchers and practitioners in the finance industry, academic researchers in economics and finance, and advanced MBA and graduate students in economics and finance. Readers are assumed to have a basic knowledge of S-PLUS and a solid grounding in basic statistics and time series concepts.
Author: Nikolaos Panigirtzoglou Publisher: ISBN: Category : Languages : en Pages :
Book Description
This paper presents a new approach to modeling the dynamics of implied distributions. First, we obtain a parsimonious description of the dynamics of the Samp;P 500 implied cumulative distribution functions (CDFs) by applying Principal Components Analysis. Subsequently, we develop new arbitrage-free Monte-Carlo simulation methods that model the evolution of the whole distribution through time as a diffusion process. Our approach generalizes the conventional approaches of modeling only the first two moments as diffusion processes, and it has important implications for smile-consistent option pricing and for risk management. The out-of-sample performance within a Value-at-Risk framework is examined.
Author: Yue Kuen Kwok Publisher: CRC Press ISBN: 1000584259 Category : Business & Economics Languages : en Pages : 283
Book Description
Pricing Models of Volatility Products and Exotic Variance Derivatives summarizes most of the recent research results in pricing models of derivatives on discrete realized variance and VIX. The book begins with the presentation of volatility trading and uses of variance derivatives. It then moves on to discuss the robust replication strategy of variance swaps using portfolio of options, which is one of the major milestones in pricing theory of variance derivatives. The replication procedure provides the theoretical foundation of the construction of VIX. This book provides sound arguments for formulating the pricing models of variance derivatives and establishes formal proofs of various technical results. Illustrative numerical examples are included to show accuracy and effectiveness of analytic and approximation methods. Features Useful for practitioners and quants in the financial industry who need to make choices between various pricing models of variance derivatives Fabulous resource for researchers interested in pricing and hedging issues of variance derivatives and VIX products Can be used as a university textbook in a topic course on pricing variance derivatives
Author: Peter Christoffersen
Author: Peter Christoffersen
Author: Garland Durham
Author: Carl Chiarella
Author: Andrew C. Harvey
Author: Hanxue Yang
Author: 
Author: Sílvia Gonçalves
Author: Eric Zivot
Author: Nikolaos Panigirtzoglou
Author: Yue Kuen Kwok