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Author: Sam Howison
Publisher:
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Category :
Languages : en
Pages :
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Author: Sam Howison
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Category :
Languages : en
Pages :
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Author: Eckhard Platen
Publisher:
ISBN:
Category : Finance
Languages : en
Pages : 19
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Author: Jeff Fleming
Publisher:
ISBN:
Category :
Languages : en
Pages : 39
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We use state-space methods to investigate the relation between volume, volatility, and ARCH effects within a Mixture-of-Distributions Hypothesis (MDH) framework. In most recent studies of the MDH, the information flow or its logarithm is modeled as an AR(1) process. We argue that this is too restrictive because it requires the information flow to be highly persistent. Using a more general process, we find evidence of a large nonpersistent component of return volatility that is closely related to the contemporaneous nonpersistent component of trading volume. However, unlike previous studies that fit volume-augmented GARCH models, we find no evidence that trading volume subsumes ARCH effects. Because volume-augmented GARCH models are subject to simultaneity bias, our findings should be more robust than these prior results.
Author: Sadayuki Ono
Publisher:
ISBN:
Category :
Languages : en
Pages : 54
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This paper presents a pricing formula for European options that is derived from a model in which changes in the underlying price and trading volumes are jointly determined by exogenous events. The joint determination of volume and price changes provides a crucial link between volatility of the price process and an observable variable. The model works as follows: the process of information arrival (news) is taken to be a point process that induces simultaneous jumps in price and trading volume. In addition, price has a diffusion component that corresponds to background noise, and the parameter that governs the volatility of this component is a continuously weighted average of past trading volume. This specification makes increments to the volatility process depend on the current level of volatility and news and thereby accounts for the observed persistence in volatility. Moreover, it makes volatility an observable instead of a latent variable, as it is in the usual stochastic volatility setups. Options can be priced as in the Heston framework by inverting the conditional characteristic function of underlying price at expiration. We find that the model accounts well for time varying volatility smiles and term structures and that out-of-sample price forecasts for a sample of stock options are superior not only to those of standard stochastic volatility models but even to the benchmark ad hoc procedure of plugging current implicit volatilities into the Black-Scholes formula.
Author: Joann Jasiak
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Category :
Languages : en
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In this paper, we study stochastic volatility models with time deformation. Such processes relate to the early work by Mandelbrot and Taylor (1967), Clark (1973), Tauchen and Pitts (1983), among others. In our setup, the latent process of stochastic volatility evolves in an operational time which differs from calendar time. The time deformation can be determined by past volume of trade, past returns, possibly with an asymmetric leverage effect, and other variables setting the pace of information arrival. The econometric specification exploits the state-space approach for stochastic volatility models proposed by Harvey, Ruiz and Shephard (1994) as well as the matching moment estimation procedure using SNP densities of stock returns and trading volume estimated by Gallant, Rossi and Tauchen (1992). Daily data on returns and trading volume of the NYSE are used in the empirical application. Supporting evidence for a time deformation representation is found and its impact on the behavior of returns and volume is analyzed. We find that increases in volume accelerate operational time, resulting in volatility being less persistent and subject to shocks with a higher innovation variance. Downward price movements have similar effects while upward price movements increase the persistence in volatility and decrease the dispersion of shocks by slowing down market time. We present the basic model as well as several extensions; in particular, we formulate and estimate a bivariate return-volume stochastic volatility model with time deformation. The latter is examined through bivariate impulse response profiles following the example of Gallant, Rossi and Tauchen (1993).
Author: Torben G. Andersen
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Category :
Languages : en
Pages :
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An empirical model for the return volatility-trading volume system is developed from a mircostructure framework in which informational asymmetries and liquidity needs motivate trade in response to the arrival of new information. The specification modifies the quot;Mixture of Distribution Hypothesisquot; (MDH). The dynamic features of the system are governed by the information flow, modeled as a stochastic volatility process that generalizes successful ARCH specifications. The persistence of volatility is fairly low, hinting at a quot;robustifyingquot; impact of including volume in the system. Speciification tests support the modified specification and show that it outperforms the standard MDH.
Author: Ghysels, Eric
Publisher: Montréal : CIRANO
ISBN:
Category :
Languages : en
Pages : 35
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Author: Torben G. Andersen
Publisher:
ISBN:
Category :
Languages : en
Pages : 273
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Author: Jean-Pierre Fouque
Publisher: Cambridge University Press
ISBN: 9780521791632
Category : Business & Economics
Languages : en
Pages : 222
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This book, first published in 2000, addresses pricing and hedging derivative securities in uncertain and changing market volatility.
Author: Robert A. Meyers
Publisher: Springer Science & Business Media
ISBN: 1441977007
Category : Business & Economics
Languages : en
Pages : 919
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Book Description
Finance, Econometrics and System Dynamics presents an overview of the concepts and tools for analyzing complex systems in a wide range of fields. The text integrates complexity with deterministic equations and concepts from real world examples, and appeals to a broad audience.
