Risk Minimization through Portfolio Replication

We use a replica approach to deal with portfolio optimization problems. A given risk measure is minimized using empirical estimates of asset values correlations. We study the phase transition which happens when the time series is too short with respect to the size of the portfolio. We also study the noise sensitivity of portfolio allocation when this transition is approached. We consider explicitely the cases where the absolute deviation and the conditional value-at-risk are chosen as a risk measure. We show how the replica method can study a wide range of risk measures, and deal with various types of time series correlations, including realistic ones with volatility clustering.Comment: 12 pages, APFA5 conferenc

Integration is not Walkability: The limits of axial topological analysis at neighbourhood scale

Spatial syntax analysis has become an influential method of analysing street networks as spaces of pedestrian movement. While significant correlations have been found between pedestrian flows and axial topological models, these models are inconsistent in the way urban morphologies are represented and measured. Meanwhile, in the fields of health, transport and urban design research, correlations have been found between walking and a range of urban morphological attributes that topological models ignore. This paper shows that while space syntax analysis has become increasingly sophisticated over the past decades, substantial limitations persist. Focusing on the limits of the theory in its own substantive field, it is shown that the abstraction of the street to its axial line poses three fundamental problems. First, it eliminates the street section and thus does not recognise that the social logic of space is also transversal across the street. Second, it ignores permeability as a key morphological attribute linked to walkability at neighbourhood scale. Third, it transposes smooth urban conditions into striated measurable models that iron-out ambiguities, eliminating conditions of liminality, porosity and complexity. Yet all these dimensions have been recognised as key attributes of urban intensity at street level. In conclusion it is argued that while axial integration may be useful in studying larger urban networks to capture particular morphogenetic tendencies, it can be misleading as a measure of walkable access at neighbourhood scale

Random Matrix Filtering in Portfolio Optimization

We study empirical covariance matrices in finance. Due to the limited amount of available input information, these objects incorporate a huge amount of noise, so their naive use in optimization procedures, such as portfolio selection, may be misleading. In this paper we investigate a recently introduced filtering procedure, and demonstrate the applicability of this method in a controlled, simulation environment.Comment: 9 pages with 3 EPS figure

Signal and Noise in Financial Correlation Matrices

Using Random Matrix Theory one can derive exact relations between the eigenvalue spectrum of the covariance matrix and the eigenvalue spectrum of its estimator (experimentally measured correlation matrix). These relations will be used to analyze a particular case of the correlations in financial series and to show that contrary to earlier claims, correlations can be measured also in the ``random'' part of the spectrum. Implications for the portfolio optimization are briefly discussed.Comment: 6 pages + 2 figures, corrected references, Talk at Conference: Applications of Physics in Financial Analysis 4, Warsaw, 13-15 November 200

Divergent estimation error in portfolio optimization and in linear regression

The problem of estimation error in portfolio optimization is discussed, in the limit where the portfolio size N and the sample size T go to infinity such that their ratio is fixed. The estimation error strongly depends on the ratio N/T and diverges for a critical value of this parameter. This divergence is the manifestation of an algorithmic phase transition, it is accompanied by a number of critical phenomena, and displays universality. As the structure of a large number of multidimensional regression and modelling problems is very similar to portfolio optimization, the scope of the above observations extends far beyond finance, and covers a large number of problems in operations research, machine learning, bioinformatics, medical science, economics, and technology.Comment: 5 pages, 2 figures, Statphys 23 Conference Proceedin

Noisy Covariance Matrices and Portfolio Optimization

According to recent findings [1,2], empirical covariance matrices deduced from financial return series contain such a high amount of noise that, apart from a few large eigenvalues and the corresponding eigenvectors, their structure can essentially be regarded as random. In [1], e.g., it is reported that about 94% of the spectrum of these matrices can be fitted by that of a random matrix drawn from an appropriately chosen ensemble. In view of the fundamental role of covariance matrices in the theory of portfolio optimization as well as in industry-wide risk management practices, we analyze the possible implications of this effect. Simulation experiments with matrices having a structure such as described in [1,2] lead us to the conclusion that in the context of the classical portfolio problem (minimizing the portfolio variance under linear constraints) noise has relatively little effect. To leading order the solutions are determined by the stable, large eigenvalues, and the displacement of the solution (measured in variance) due to noise is rather small: depending on the size of the portfolio and on the length of the time series, it is of the order of 5 to 15%. The picture is completely different, however, if we attempt to minimize the variance under non-linear constraints, like those that arise e.g. in the problem of margin accounts or in international capital adequacy regulation. In these problems the presence of noise leads to a serious instability and a high degree of degeneracy of the solutions.Comment: 7 pages, 3 figure

An analysis of Cross-correlations in South African Market data

We apply random matrix theory to compare correlation matrix estimators C obtained from emerging market data. The correlation matrices are constructed from 10 years of daily data for stocks listed on the Johannesburg Stock Exchange (JSE) from January 1993 to December 2002. We test the spectral properties of C against random matrix predictions and find some agreement between the distributions of eigenvalues, nearest neighbour spacings, distributions of eigenvector components and the inverse participation ratios for eigenvectors. We show that interpolating both missing data and illiquid trading days with a zero-order hold increases agreement with RMT predictions. For the more realistic estimation of correlations in an emerging market, we suggest a pairwise measured-data correlation matrix. For the data set used, this approach suggests greater temporal stability for the leading eigenvectors. An interpretation of eigenvectors in terms of trading strategies is given in lieu of classification by economic sectors.Comment: 19 pages, 15 figures, additional figures, discussion and reference