26,277 research outputs found
Asymmetric correlation matrices: an analysis of financial data
We analyze the spectral properties of correlation matrices between distinct
statistical systems. Such matrices are intrinsically non symmetric, and lend
themselves to extend the spectral analyses usually performed on standard
Pearson correlation matrices to the realm of complex eigenvalues. We employ
some recent random matrix theory results on the average eigenvalue density of
this type of matrices to distinguish between noise and non trivial correlation
structures, and we focus on financial data as a case study. Namely, we employ
daily prices of stocks belonging to the American and British stock exchanges,
and look for the emergence of correlations between two such markets in the
eigenvalue spectrum of their non symmetric correlation matrix. We find several
non trivial results, also when considering time-lagged correlations over short
lags, and we corroborate our findings by additionally studying the asymmetric
correlation matrix of the principal components of our datasets.Comment: Revised version; 11 pages, 13 figure
Measuring market liquidity: An introductory survey
Asset liquidity in modern financial markets is a key but elusive concept. A
market is often said to be liquid when the prevailing structure of transactions
provides a prompt and secure link between the demand and supply of assets, thus
delivering low costs of transaction. Providing a rigorous and empirically
relevant definition of market liquidity has, however, provided to be a
difficult task. This paper provides a critical review of the frameworks
currently available for modelling and estimating the market liquidity of
assets. We consider definitions that stress the role of the bid-ask spread and
the estimation of its components that arise from alternative sources of market
friction. In this case, intra-daily measures of liquidity appear relevant for
capturing the core features of a market, and for their ability to describe the
arrival of new information to market participants
Gold, Oil, and Stocks
We employ a wavelet approach and conduct a time-frequency analysis of dynamic
correlations between pairs of key traded assets (gold, oil, and stocks)
covering the period from 1987 to 2012. The analysis is performed on both
intra-day and daily data. We show that heterogeneity in correlations across a
number of investment horizons between pairs of assets is a dominant feature
during times of economic downturn and financial turbulence for all three pairs
of the assets under research. Heterogeneity prevails in correlations between
gold and stocks. After the 2008 crisis, correlations among all three assets
increase and become homogenous: the timing differs for the three pairs but
coincides with the structural breaks that are identified in specific
correlation dynamics. A strong implication emerges: during the period under
research, and from a different-investment-horizons perspective, all three
assets could be used in a well-diversified portfolio only during relatively
short periods
Rice World Market Prices
The marketing loan program associated with rice features benefits calculated using a USDA-announced World Market Price (WMP) rather than the posted county prices that are used for most other commodities. This results in reduced risk protection for producers relative to other crops, and greater difficulty in making optimal use of program benefits. This research investigates the rice WMP, identifying the relative importance of various foreign prices and other potential influencing factors. The results of this research have important implications for financial planning and optimal risk management strategies for rice producers.Agricultural and Food Policy,
Community detection for correlation matrices
A challenging problem in the study of complex systems is that of resolving,
without prior information, the emergent, mesoscopic organization determined by
groups of units whose dynamical activity is more strongly correlated internally
than with the rest of the system. The existing techniques to filter
correlations are not explicitly oriented towards identifying such modules and
can suffer from an unavoidable information loss. A promising alternative is
that of employing community detection techniques developed in network theory.
Unfortunately, this approach has focused predominantly on replacing network
data with correlation matrices, a procedure that tends to be intrinsically
biased due to its inconsistency with the null hypotheses underlying the
existing algorithms. Here we introduce, via a consistent redefinition of null
models based on random matrix theory, the appropriate correlation-based
counterparts of the most popular community detection techniques. Our methods
can filter out both unit-specific noise and system-wide dependencies, and the
resulting communities are internally correlated and mutually anti-correlated.
We also implement multiresolution and multifrequency approaches revealing
hierarchically nested sub-communities with `hard' cores and `soft' peripheries.
We apply our techniques to several financial time series and identify
mesoscopic groups of stocks which are irreducible to a standard, sectorial
taxonomy, detect `soft stocks' that alternate between communities, and discuss
implications for portfolio optimization and risk management.Comment: Final version, accepted for publication on PR
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