17 research outputs found
Eroding market stability by proliferation of financial instruments
We contrast Arbitrage Pricing Theory (APT), the theoretical basis for the
development of financial instruments, with a dynamical picture of an
interacting market, in a simple setting. The proliferation of financial
instruments apparently provides more means for risk diversification, making the
market more efficient and complete. In the simple market of interacting traders
discussed here, the proliferation of financial instruments erodes systemic
stability and it drives the market to a critical state characterized by large
susceptibility, strong fluctuations and enhanced correlations among risks. This
suggests that the hypothesis of APT may not be compatible with a stable market
dynamics. In this perspective, market stability acquires the properties of a
common good, which suggests that appropriate measures should be introduced in
derivative markets, to preserve stability.Comment: 26 pages, 8 figure
Variety and Volatility in Financial Markets
We study the price dynamics of stocks traded in a financial market by
considering the statistical properties both of a single time series and of an
ensemble of stocks traded simultaneously. We use the stocks traded in the
New York Stock Exchange to form a statistical ensemble of daily stock returns.
For each trading day of our database, we study the ensemble return
distribution. We find that a typical ensemble return distribution exists in
most of the trading days with the exception of crash and rally days and of the
days subsequent to these extreme events. We analyze each ensemble return
distribution by extracting its first two central moments. We observe that these
moments are fluctuating in time and are stochastic processes themselves. We
characterize the statistical properties of ensemble return distribution central
moments by investigating their probability density functions and temporal
correlation properties. In general, time-averaged and portfolio-averaged price
returns have different statistical properties. We infer from these differences
information about the relative strength of correlation between stocks and
between different trading days. Lastly, we compare our empirical results with
those predicted by the single-index model and we conclude that this simple
model is unable to explain the statistical properties of the second moment of
the ensemble return distribution.Comment: 10 pages, 11 figure
Common Scaling Patterns in Intertrade Times of U. S. Stocks
We analyze the sequence of time intervals between consecutive stock trades of
thirty companies representing eight sectors of the U. S. economy over a period
of four years. For all companies we find that: (i) the probability density
function of intertrade times may be fit by a Weibull distribution; (ii) when
appropriately rescaled the probability densities of all companies collapse onto
a single curve implying a universal functional form; (iii) the intertrade times
exhibit power-law correlated behavior within a trading day and a consistently
greater degree of correlation over larger time scales, in agreement with the
correlation behavior of the absolute price returns for the corresponding
company, and (iv) the magnitude series of intertrade time increments is
characterized by long-range power-law correlations suggesting the presence of
nonlinear features in the trading dynamics, while the sign series is
anti-correlated at small scales. Our results suggest that independent of
industry sector, market capitalization and average level of trading activity,
the series of intertrade times exhibit possibly universal scaling patterns,
which may relate to a common mechanism underlying the trading dynamics of
diverse companies. Further, our observation of long-range power-law
correlations and a parallel with the crossover in the scaling of absolute price
returns for each individual stock, support the hypothesis that the dynamics of
transaction times may play a role in the process of price formation.Comment: 8 pages, 5 figures. Presented at The Second Nikkei Econophysics
Workshop, Tokyo, 11-14 Nov. 2002. A subset appears in "The Application of
Econophysics: Proceedings of the Second Nikkei Econophysics Symposium",
editor H. Takayasu (Springer-Verlag, Tokyo, 2003) pp.51-57. Submitted to
Phys. Rev. E on 25 June 200
Effective return, risk aversion and drawdowns
We derive two risk-adjusted performance measures for investors with risk averse preferences. Maximizing these measures is equivalent to maximizing the expected utility of an investor. The first measure, Xeff, is derived assuming a constant risk aversion while the second measure, Reff, is based on a stronger risk aversion to clustering of losses than of gains. The clustering of returns is captured through a multi-horizon framework. The empirical properties of Xeff, Reff are studied within the context of real-time trading models for foreign exchange rates and their properties are compared to those of more traditional measures like the annualized return, the Sharpe Ratio and the maximum drawdown. Our measures are shown to be more robust against clustering of losses and have the ability to fully characterize the dynamic behaviour of investment strategies
A formalization of double auction market dynamics
Biographical notes on contributors: Edward Tsang has a first degree in Business Administration (Major in Finance) and a PhD in Computer Science. He has broad interest in applied artificial intelligence, in particularly computational finance, heuristic search, constraint satisfaction and scheduling. He is currently a professor in computer science at the University of Essex where he leads the Computational Finance Group and Constraint Satisfaction and Optimization Group. He is also the Director of the Centre for Computational Finance and Economic Agents (CCFEA), an interdisciplinary centre. He founded and chaired the Technical Committee for Computational Finance under the IEEE Computational Intelligence Society in 2004-2005. Richard Olsen has a Master in Economics from Oxford University and a PhD in law from the University of Zurich. He has specialized in high frequency finance and has been a pioneer of this discipline. In 1995, he co-organized the first conference in the field. In 2001, he and his team published a book, ‘Introduction to High Frequency Finance’, Academic Press. He is CEO of Olsen Ltd, a systematic asset management company based in Zurich and co-founde
Mean square prediction error for long-memory processes
Fractional ARIMA(p,d,q) processes, long-range dependence, longrange forecasting, mean square prediction error, misspecification,