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Peter Richmond

Publications and source records attributed to Peter Richmond.

49 records · Page 3Linked to original sources
arXiv2005

Pricing of options on stocks driven by multi-dimensional operator stable Levy processes

We model the price of a stock via a Langévin equation with multi-dimensional fluctuations coupled in the price and in time. We generalize previous models in that we assume that the fluctuations conditioned on the time step are compound Poisson processes with operator stable jump intensities. We derive exact relations for Fourier transforms of the jump intensity in case of different scaling indices $\underline{\underline{E}}$ of the process. We express the Fourier transform of the joint probability density of the process to attain given values at several different times and to attain a given maximal value in a given time period through Fourier transforms of the jump intensity. Then we consider a portfolio composed of stocks and of options on stocks and we derive the Fourier transform of a random variable $\mathfrak{D}_t$ (deviation of the portfolio) that is defined as a small temporal change of the portfolio diminished by the the compound interest earned. We show that if the price of the option at time $t$ satisfies a certain functional equation specified in text then the deviation of the portfolio has a zero mean $E[ \mathfrak{D}_t ] = 0$ and the option pricing problem may have a solution. We compare our approach to other approaches that assumed the log-characteristic function of the fluctuations that drive the stock price to be an analytic function.

math-ph↗
arXiv2004

Modelling the term structure of interest rates á la Heath-Jarrow-Morton but with non Gaussian fluctuations

We consider a generalization of the Heath Jarrow Morton model for the term structure of interest rates where the forward rate is driven by Paretian fluctuations. We derive a generalization of Itô's lemma for the calculation of a differential of a Paretian stochastic variable and use it to derive a Stochastic Differential Equation for the discounted bond price. We show that it is not possible to choose the parameters of the model to ensure absence of drift of the discounted bond price. Then we consider a Continuous Time Random Walk with jumps driven by Paretian random variables and we derive the large time scaling limit of the jump probability distribution function (pdf). We show that under certain conditions defined in text the large time scaling limit of the jump pdf in the Fourier domain is \tilde{omega}_t(k,t) \sim \exp{-K/(\ln(k t))^2} and is different from the case of a random walk with Gaussian fluctuations. We also derive the master equation for the jump pdf and discuss the relation of the master equation to Distributed Order Fractional Diffusion Equations.

cond-mat.other↗
arXiv2004

Dynamics of Money and Income Distributions

We study the model of interacting agents proposed by Chatterjee et al that allows agents to both save and exchange wealth. Closed equations for the wealth distribution are developed using a mean field approximation. We show that when all agents have the same fixed savings propensity, subject to certain well defined approximations defined in the text, these equations yield the conjecture proposed by Chatterjee for the form of the stationary agent wealth distribution. If the savings propensity for the equations is chosen according to some random distribution we show further that the wealth distribution for large values of wealth displays a Pareto like power law tail, ie P(w)\sim w^{1+a}. However the value of $a$ for the model is exactly 1. Exact numerical simulations for the model illustrate how, as the savings distribution function narrows to zero, the wealth distribution changes from a Pareto form to to an exponential function. Intermediate regions of wealth may be approximately described by a power law with $a>1$. However the value never reaches values of \~ 1.6-1.7 that characterise empirical wealth data. This conclusion is not changed if three body agent exchange processes are allowed. We conclude that other mechanisms are required if the model is to agree with empirical wealth data.

cond-mat.other↗
arXiv2004

Removing noise from correlations in multivariate stock price data

This paper examines the applicability of Random Matrix Theory to portfolio management in finance. Starting from a group of normally distributed stochastic processes with given correlations we devise an algorithm for removing noise from the estimator of correlations constructed from measured time series. We then apply this algorithm to historical time series for the Standard and Poor's 500 index. We discuss to what extent the noise can be removed and whether the resulting underlying correlations are sufficiently accurate for portfolio management purposes.

cond-mat.stat-mech↗
arXiv2003

Modeling of waiting times and price changes in currency exchange data

A theory which describes the share price evolution at financial markets as a continuous-time random walk has been generalized in order to take into account the dependence of waiting times t on price returns x. A joint probability density function (pdf) which uses the concept of a Lévy stable distribution is worked out. The theory is fitted to high-frequency US$/Japanese Yen exchange rate and low-frequency 19th century Irish stock data. The theory has been fitted both to price return and to waiting time data and the adherence to data, in terms of the chi-squared test statistic, has been improved when compared to the old theory.

cond-mat.stat-mech↗
arXiv2003

Langevin processes, agent models and socio-economic systems

We review some approaches to the understanding of fluctuations in some models used to describe socio and economic systems. Our approach builds on the development of a simple Langevin equation that characterises stochastic processes. This provides a unifying approach that allows first a straightforward description of the early approaches of Bachelier. We generalise the approach to stochastic equations that model interacting agents. Using a simple change of variable, we show that the peer pressure model of Marsilli and the wealth dynamics model of Solomon are closely related. The methods are further shown to be consistent with a global free energy functional that invokes an entropy term based on the Boltzmann formula. A more recent approach by Michael and Johnson maximised a Tsallis entropy function subject to simple constraints. We show how this approach can be developed from an agent model where the simple Langevin process is now conditioned by local rather than global noise. The approach yields a BBGKY type hierarchy of equations for the system correlation functions. Of especial interest is that the results can be obtained from a new free energy functional similar to that mentioned above except that a Tsallis like entropy term replaces the Boltzmann entropy term. A mean field approximation yields the results of Michael and Johnson. We show how personal income data for Brazil, the US, Germany and the UK, analysed recently by Borgas can be qualitatively understood by this approach.

cond-mat.stat-mech↗
arXiv2003

Non-monotonic spontaneous magnetization in a Sznajd-like Consensus Model

Ising or Potts models of ferromagnetism have been widely used to describe locally interacting social or economic systems. We consider a related model, introduced by Sznajd to describe the evolution of consensus in a society. In this model, the opinion or state of any spins can only be changed through the influence of neighbouring pairs of similarly aligned spins. Such pairs can polarize their neighbours. We show that, assuming the global dynamics evolve in a synchronous manner, the two-state Sznajd model exhibits a non-monotonically decreasing overall orientation that has a maximum value when the system is subject to a finite value of noise. Reinterpreting the model in terms of opinions within a society we predict that consensus can be increased by the addition of an appropriate amount of random noise. These features are explained by the presence of islands of complete orientation that are stable in the absence of noise but removed via the presence of added noise.

cond-mat.stat-mech↗
arXiv2002

Theoretical Analysis and Simulations of the Generalized Lotka-Volterra Model

The dynamics of generalized Lotka-Volterra systems is studied by theoretical techniques and computer simulations. These systems describe the time evolution of the wealth distribution of individuals in a society, as well as of the market values of firms in the stock market. The individual wealths or market values are given by a set of time dependent variables $w_i$, $i=1,...N$. The equations include a stochastic autocatalytic term (representing investments), a drift term (representing social security payments) and a time dependent saturation term (due to the finite size of the economy). The $w_i$'s turn out to exhibit a power-law distribution of the form $P(w) \sim w^{-1-α}$. It is shown analytically that the exponent $α$ can be expressed as a function of one parameter, which is the ratio between the constant drift component (social security) and the fluctuating component (investments). This result provides a link between the lower and upper cutoffs of this distribution, namely between the resources available to the poorest and those available to the richest in a given society. The value of $α$ %as well as the position of the lower cutoff is found to be insensitive to variations in the saturation term, that represent the expansion or contraction of the economy. The results are of much relevance to empirical studies that show that the distribution of the individual wealth in different countries during different periods in the 20th century has followed a power-law distribution with $1 < α< 2$.

cond-mat.stat-mech↗
arXiv2001

Power Laws of Wealth, Market Order Volumes and Market Returns

Using the Generalised Lotka Volterra (GLV) model adapted to deal with muti agent systems we can investigate economic systems from a general viewpoint and obtain generic features common to most economies. Assuming only weak generic assumptions on capital dynamics, we are able to obtain very specific predictions for the distribution of social wealth. First, we show that in a 'fair' market, the wealth distribution among individual investors fulfills a power law. We then argue that 'fair play' for capital and minimal socio-biological needs of the humans traps the economy within a power law wealth distribution with a particular Pareto exponent $α\sim 3/2$. In particular we relate it to the average number of individuals L depending on the average wealth: $α\sim L/(L-1)$. Then we connect it to certain power exponents characterising the stock markets. We obtain that the distribution of volumes of the individual (buy and sell) orders follows a power law with similar exponent $β\sim α\sim 3/2$. Consequently, in a market where trades take place by matching pairs of such sell and buy orders, the corresponding exponent for the market returns is expected to be of order $γ\sim 2 α\sim 3$. These results are consistent with recent experimental measurements of these power law exponents ([Maslov 2001] for $β$ and [Gopikrishnan et al. 1999] for $γ$).

cond-mat.stat-mech↗
arXiv2001

Stability of Pareto-Zipf Law in Non-Stationary Economies

Generalized Lotka-Volterra (GLV) models extending the (70 year old) logistic equation to stochastic systems consisting of a multitude of competing auto-catalytic components lead to power distribution laws of the (100 year old) Pareto-Zipf type. In particular, when applied to economic systems, GLV leads to power laws in the relative individual wealth distribution and in the market returns. These power laws and their exponent alpha are invariant to arbitrary variations in the total wealth of the system and to other endogenous and exogenous factors. The measured value of the exponent alpha = 1.4 is related to built-in human social and biological constraints.

cond-mat.stat-mech↗
arXiv2000

Power Laws are Boltzmann Laws in Disguise

Using a model based on generalised Lotka Volterra dynamics together with some recent results for the solution of generalised Langevin equations, we show that the equilibrium solution for the probability distribution of wealth has two characteristic regimes. For large values of wealth it takes the form of a Pareto style power law. For small values of wealth, (w less then wmin) the distribution function tends sharply to zero with infinite slope. The origin of this law lies in the random multiplicative process built into the model. Whilst such results have been known since the time of Gibrat, the present framework allows for a stable power law in an arbitrary and irregular global dynamics, so long as the market is `fair', i.e., there is no net advantage to any particular group or individual. We show for our model that the relative distribution of wealth follows a time independent distribution of this form even thought the total wealth may follow a more complicated dynamics and vary with time in an arbitrary manner. In developing the theory, we draw parallels with conventional thermodynamics and derive for the system the associated laws of `econodynamics' together with the associated econodynamic potentials. The power law that arises in the distribution function may then be identified with new additional logarithmic terms in the familiar Boltzmann distribution function for the system. The distribution function of stock market returns for our model, it is argued, will follow the same qualitative laws and exhibit power law behaviour.

cond-mat.stat-mech↗