Results 1  10
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40
SelfSimilarity in File Systems
, 1998
"... We demonstrate that highlevel file system events exhibit selfsimilar behaviour, but only for shortterm time scales of approximately under a day. We do so through the analysis of four sets of traces that span time scales of milliseconds through months, and that differ in the trace collection metho ..."
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Cited by 41 (0 self)
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We demonstrate that highlevel file system events exhibit selfsimilar behaviour, but only for shortterm time scales of approximately under a day. We do so through the analysis of four sets of traces that span time scales of milliseconds through months, and that differ in the trace collection method, the file systems being traced, and the chronological times of the tracing. Two sets of detailed, shortterm file system trace data are analyzed; both are shown to have selfsimilar like behaviour, with consistent Hurst parameters (a measure of selfsimilarity) for all file system traffic as well as individual classes of file system events. Longterm file system trace data is then analyzed, and we discover that the traces' high variability and selfsimilar behaviour does not persist across time scales of days, weeks, and months. Using the shortterm trace data, weshow that sources of file system traffic exhibit ON/OFF source behaviour, which is characterized by highly variably lengthed bursts of activity, followed by similarly variably lengthed periods of inactivity. This ON/OFF behaviour is used to motivate a simple technique for synthesizing a stream of events that exhibit the same selfsimilar shortterm behaviour as was observed in the file system traces.
Dynamic Semiotics
"... this paper I shall make a case for a dynamic semiotics. I list a set of phenomena that are difficult to understand in standard theories, and suggest a model borrowed from theories of complex dynamic systems. Since such theories rely on processes of selforganization that often defy analytical treatm ..."
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Cited by 35 (2 self)
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this paper I shall make a case for a dynamic semiotics. I list a set of phenomena that are difficult to understand in standard theories, and suggest a model borrowed from theories of complex dynamic systems. Since such theories rely on processes of selforganization that often defy analytical treatment, I use small computational models for assessing the empirical consequences of the theories.
A Jump Diffusion Model for Option Pricing with Three Properties: Leptokurtic Feature, Volatility Smile, and Analytical Tractability
, 2000
"... Brownian motion and normal distribution have been widely used, for example, in the BlackScholesMerton option pricing framework, to study the return of assets. However, two puzzles, emerged from many empirical investigations, have got much attention recently, namely (a) the leptokurtic feature that ..."
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Cited by 20 (1 self)
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Brownian motion and normal distribution have been widely used, for example, in the BlackScholesMerton option pricing framework, to study the return of assets. However, two puzzles, emerged from many empirical investigations, have got much attention recently, namely (a) the leptokurtic feature that the return distribution of assets may have a higher peak and two (asymmetric) heavier tails than those of the normal distribution, and (b) an empirical abnormity called "volatility smile" in option pricing. To incorporate both the leptokurtic feature and \volatility smile", this paper proposes, for the purpose of studying option pricing, a jump diffusion model, in which the price of the underlying asset is modeled by two parts, a continuous part driven by Brownian motion, and a jump part with the logarithm of the jump sizes having a double exponential distribution. In addition to the above two desirable properties, leptokurtic feature and \volatility smile", the model is simple enough to produce analytical solutions for a variety of option pricing problems, including options, future options, and interest rate derivatives, such as caps and floors, in terms of the Hh function. Although there are many models can incorporate some of the three properties (the leptokurtic feature, "volatility smile", and analytical tractability), the current model can incorporate all three under a unified framework.
General BlackScholes models accounting for increased market volatility from hedging strategies
, 1997
"... Increases in market volatility of asset prices have been observed and analyzed in recent years and their cause has generally been attributed to the popularity of portfolio insurance strategies for derivative securities. The basis of derivative pricing is the BlackScholes model and its use is so ext ..."
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Cited by 18 (1 self)
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Increases in market volatility of asset prices have been observed and analyzed in recent years and their cause has generally been attributed to the popularity of portfolio insurance strategies for derivative securities. The basis of derivative pricing is the BlackScholes model and its use is so extensive that it is likely to influence the market itself. In particular it has been suggested that this is a factor in the rise in volatilities. In this work we present a class of pricing models that account for the feedback effect from the BlackScholes dynamic hedging strategies on the price of the asset, and from there back onto the price of the derivative. These models do predict increased implied volatilities with minimal assumptions beyond those of the BlackScholes theory. They are characterized by a nonlinear partial differential equation that reduces to the BlackScholes equation when the feedback is removed. We begin with a model economy consisting of two distinct groups of traders: Reference traders who are the majority investing in the asset expecting gain, and program traders who trade the asset following a BlackScholes type dynamic hedging strategy, which is not known a priori, in order to insure against the risk of a derivative security. The interaction of these groups leads to a stochastic process for the price of the asset which depends on the hedging strategy of the program traders. Then following a BlackScholes argument, we
Artificial neural networks and their business applications
 Information and Management
, 1994
"... Artificial neural networks are increasingly popular in today's business fields. They have been hailed as the greatest technological advance since the invention of transistors. The purpose of this paper is to answer two of the most frequently asked questions: "What are neural networks? " &q ..."
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Cited by 11 (0 self)
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Artificial neural networks are increasingly popular in today's business fields. They have been hailed as the greatest technological advance since the invention of transistors. The purpose of this paper is to answer two of the most frequently asked questions: "What are neural networks? " "Why are they so popular in today's business fields? " The paper reviews the common characteristics of neural networks and discusses the feasibility of neuralnet applications in business fields. It then presents four actual application cases and identifies the limitations of the current neuralnet technology.
Optimization of Trading Physics Models of Markets
, 2001
"... We describe an endtoend realtime S&P futures trading system. Innershell stochastic nonlinear dynamic models are developed, and Canonical Momenta Indicators (CMI) are derived from a fitted Lagrangian used by outershell trading models dependent on these indicators. Recursive and adaptive optimiza ..."
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Cited by 7 (6 self)
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We describe an endtoend realtime S&P futures trading system. Innershell stochastic nonlinear dynamic models are developed, and Canonical Momenta Indicators (CMI) are derived from a fitted Lagrangian used by outershell trading models dependent on these indicators. Recursive and adaptive optimization using Adaptive Simulated Annealing (ASA) is used for fitting parameters shared across these shells of dynamic and trading models.
Tcheou, Scaling transformation and probability distributions for financial time series, preprint condmat/9905169
, 1999
"... The price of financial assets are, since [1], considered to be described by a (discrete or continuous) time sequence of random variables, i.e a stochastic process. Sharp scaling exponents or unifractal behavior of such processes has been reported in several works [2] [3] [4] [5] [6]. In this letter ..."
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Cited by 6 (0 self)
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The price of financial assets are, since [1], considered to be described by a (discrete or continuous) time sequence of random variables, i.e a stochastic process. Sharp scaling exponents or unifractal behavior of such processes has been reported in several works [2] [3] [4] [5] [6]. In this letter we investigate the question of scaling transformation of price processes by establishing a new connexion between nonlinear group theoretical methods and multifractal methods developed in mathematical physics. Using two sets of financial chronological time series, we show that the scaling transformation is a nonlinear group action on the moments of the price increments. Its linear part has a spectral decomposition that puts in evidence a multifractal behavior of the price increments. 1
Stochastic Volatility
 Statistics in Finance. Applications of Statistics Series
, 1996
"... The volatility of a financial asset is the variance per unit time of the logarithm of the price of the asset. Volatility has a key role to play in the determination of risk and in the valuation of options and other derivative securities. The widespread BlackScholes model for asset prices assumes co ..."
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Cited by 6 (0 self)
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The volatility of a financial asset is the variance per unit time of the logarithm of the price of the asset. Volatility has a key role to play in the determination of risk and in the valuation of options and other derivative securities. The widespread BlackScholes model for asset prices assumes constant volatility. The purpose of this chapter is to review the evidence for nonconstant volatility and to consider the implications for option pricing of alternative random or stochastic volatility models. We concentrate on continuous time diffusion models for the volatility, but we also make comments about certain classes of discrete time models, such as ARV, ARCH and GARCH. 1 Volatility and the need for Stochastic Volatility models 1.1 Introduction A common approach in the modelling of financial assets is to assume that the proportional price changes of an asset form a Gaussian process with stationary independent increments. The celebrated (and ubiquitous) BlackScholes option pricin...
Morphodynamic Models of Communication
 In B
, 1996
"... this paper I offer some ideas of possible ways of doing this. I shall use two types of dynamic models, Thom's catastrophe theory already mentioned and the formalism of cellular automata. In addition, I shall use the theory of autopoiesis developed by N. Luhmann, H. Maturana, F. Varela, and others as ..."
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Cited by 4 (3 self)
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this paper I offer some ideas of possible ways of doing this. I shall use two types of dynamic models, Thom's catastrophe theory already mentioned and the formalism of cellular automata. In addition, I shall use the theory of autopoiesis developed by N. Luhmann, H. Maturana, F. Varela, and others as the general framework in which the formal ideas are developed.
Observing gay, lesbian and heterosexual couples’ relationships: Mathematical modeling of conflict interaction
 Journal of Homosexuality
, 2003
"... ABSTRACT. Two samples of committed gay and lesbian cohabiting couples and two samples of married couples (couples in which the woman presented the conflict issue to the man, and couples in which the John Mordechai Gottman is affiliated with the University of Washington. Robert Wayne Levenson is affi ..."
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Cited by 4 (0 self)
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ABSTRACT. Two samples of committed gay and lesbian cohabiting couples and two samples of married couples (couples in which the woman presented the conflict issue to the man, and couples in which the John Mordechai Gottman is affiliated with the University of Washington. Robert Wayne Levenson is affiliated with the University of California at Berkeley. Catherine