A memory-based method to select the number of relevant components in Principal Component Analysis

We propose a new data-driven method to select the optimal number of relevant components in Principal Component Analysis (PCA). This new method applies to correlation matrices whose time autocorrelation function decays more slowly than an exponential, giving rise to long memory effects. In comparison with other available methods present in the literature, our procedure does not rely on subjective evaluations and is computationally inexpensive. The underlying basic idea is to use a suitable factor model to analyse the residual memory after sequentially removing more and more components, and stopping the process when the maximum amount of memory has been accounted for by the retained components. We validate our methodology on both synthetic and real financial data, and find in all cases a clear and computationally superior answer entirely compatible with available heuristic criteria, such as cumulative variance and cross-validation.Comment: 29 pages, publishe

Quasar Clustering in Cosmological Hydrodynamic Simulations: Evidence for mergers

We examine the clustering properties of a population of quasars drawn from fully hydrodynamic cosmological simulations that directly follow black hole growth. We find that the black hole correlation function is best described by two distinct components: contributions from BH pairs occupying the same dark matter halo ('1-halo term') which dominate at scales below 300 kpc/h, and contributions from BHs occupying separate halos ('2-halo term') which dominate at larger scales. From the 2-halo BH term we find a typical host halo mass for faint-end quasars (those probed in our simulation volumes) ranging from 10^11 to a few 10^12 solar masses from z=5 to z=1 respectively (consistent with the mean halo host mass). The BH correlation function shows a luminosity dependence as a function of redshift, though weak enough to be consistent with observational constraints. At small scales, the high resolution of our simulations allows us to probe the 1-halo clustering in detail, finding that the 1-halo term follows an approximate power law, lacking the characteristic decrease in slope at small scales found in 1-halo terms for galaxies and dark matter. We show that this difference is a direct result of a boost in the small-scale quasar bias caused by galaxies hosting multiple quasars (1-subhalo term) following a merger event, typically between a large central subgroup and a smaller, satellite subgroup hosting a relatively small black hole. We show that our predicted small-scale excess caused by such mergers is in good agreement with both the slope and amplitude indicated by recent small-scale measurements. Finally, we note the excess to be a strong function of halo mass, such that the observed excess is well matched by the multiple black holes of intermediate mass (10^7-10^8 solar masses) found in hosts of 4-8*10^11 solar masses, a range well probed by our simulations.Comment: 12 pages, 10 figures. Submitted to MNRA

Relation between Financial Market Structure and the Real Economy: Comparison between Clustering Methods

We quantify the amount of information filtered by different hierarchical clustering methods on correlations between stock returns comparing it with the underlying industrial activity structure. Specifically, we apply, for the first time to financial data, a novel hierarchical clustering approach, the Directed Bubble Hierarchical Tree and we compare it with other methods including the Linkage and k-medoids. In particular, by taking the industrial sector classification of stocks as a benchmark partition, we evaluate how the different methods retrieve this classification. The results show that the Directed Bubble Hierarchical Tree can outperform other methods, being able to retrieve more information with fewer clusters. Moreover, we show that the economic information is hidden at different levels of the hierarchical structures depending on the clustering method. The dynamical analysis on a rolling window also reveals that the different methods show different degrees of sensitivity to events affecting financial markets, like crises. These results can be of interest for all the applications of clustering methods to portfolio optimization and risk hedging.Comment: 31 pages, 17 figure

True and Apparent Scaling: The Proximity of the Markov- Switching Multifractal Model to Long-Range Dependence

In this paper, we consider daily financial data of a collection of different stock market indices, exchange rates, and interest rates, and we analyze their multi-scaling properties by estimating a simple specification of the Markov- switching multifractal model (MSM). In order to see how well the estimated models capture the temporal dependence of the data, we estimate and compare the scaling exponents H(q) (for q = 1; 2) for both empirical data and simulated data of the estimated MSM models. In most cases the multifractal model appears to generate `apparent' long memory in agreement with the empirical scaling laws. --Scaling,Generalized Hurst exponent,Multifractal model,GMM estimation

Multifractality and long-range dependence of asset returns: The scaling behaviour of the Markov-switching multifractal model with lognormal volatility components

In this paper we consider daily financial data from various sources (stock market indices, foreign exchange rates and bonds) and analyze their multi-scaling properties by estimating the parameters of a Markov-switching multifractal model (MSM) with Lognormal volatility components. In order to see how well estimated models capture the temporal dependency of the empirical data, we estimate and compare (generalized) Hurst exponents for both empirical data and simulated MSM models. In general, the Lognormal MSM models generate ?apparent? long memory in good agreement with empirical scaling provided one uses sufficiently many volatility components. In comparison with a Binomial MSM specification [7], results are almost identical. This suggests that a parsimonious discrete specification is flexible enough and the gain from adopting the continuous Lognormal distribution is very limited. --Markov-switching multifractal , scaling , return volatility