Assessing systemic risk due to fire sales spillover through maximum entropy network reconstruction

Assessing systemic risk in financial markets is of great importance but it often requires data that are unavailable or available at a very low frequency. For this reason, systemic risk assessment with partial information is potentially very useful for regulators and other stakeholders. In this paper we consider systemic risk due to fire sales spillover and portfolio rebalancing by using the risk metrics defined by Greenwood et al. (2015). By using the Maximum Entropy principle we propose a method to assess aggregated and single bank's systemicness and vulnerability and to statistically test for a change in these variables when only the information on the size of each bank and the capitalization of the investment assets are available. We prove the effectiveness of our method on 2001-2013 quarterly data of US banks for which portfolio composition is available.Comment: 36 pages, 6 figures, Accepted on Journal of Economic Dynamics and Contro

Score-driven generalized fitness model for sparse and weighted temporal networks

Temporal network data have recently received increasing attention due to the rich information content and valuable insight that appropriate modeling of links’ dynamics can unveil. While most of the literature on temporal network models focuses on binary graphs, each link of a real networks is often associated with a weight, a positive number describing the intensity of the relation between the nodes. Here we propose a novel dynamical model for sparse and weighted temporal networks as a combination of an extension of the fitness model and of the score-driven framework. We consider a zero-augmented generalized linear model to handle the weights and an observation-driven approach to describe time-varying parameters. We propose a flexible approach where the existence probability of a link is independent of its expected weight. This fact represents a crucial difference with alternative specifications proposed in the recent literature, with relevant implications both for the model's flexibility and for the forecasting capability. Our approach also accommodates the network dynamics’ dependence on external variables. We present a link forecasting analysis to data describing the overnight exposures in the Euro interbank market and investigate whether the influence of EONIA rates on the interbank network dynamics has changed over time during the sovereign debt crisis. (c) 2022 Elsevier Inc. All rights reserved

Modelling time-varying interactions in complex systems: the Score Driven Kinetic Ising Model

A common issue when analyzing real-world complex systems is that the interactions between their elements often change over time. Here we propose a new modeling approach for time-varying interactions generalising the well-known Kinetic Ising Model, a minimalistic pairwise constant interactions model which has found applications in several scientific disciplines. Keeping arbitrary choices of dynamics to a minimum and seeking information theoretical optimality, the Score-Driven methodology allows to extract from data and interpret the presence of temporal patterns describing time-varying interactions. We identify a parameter whose value at a given time can be directly associated with the local predictability of the dynamics and we introduce a method to dynamically learn its value from the data, without specifying parametrically the system's dynamics. We extend our framework to disentangle different sources (e.g. endogenous vs exogenous) of predictability in real time, and show how our methodology applies to a variety of complex systems such as financial markets, temporal (social) networks, and neuronal populations

Network Sensitivity of Systemic Risk

A growing body of studies on systemic risk in financial markets has emphasized the key importance of taking into consideration the complex interconnections among financial institutions. Much effort has been put in modeling the contagion dynamics of financial shocks, and to assess the resilience of specific financial markets - either using real network data, reconstruction techniques or simple toy networks. Here we address the more general problem of how shock propagation dynamics depends on the topological details of the underlying network. To this end we consider different realistic network topologies, all consistent with balance sheets information obtained from real data on financial institutions. In particular, we consider networks of varying density and with different block structures, and diversify as well in the details of the shock propagation dynamics. We confirm that the systemic risk properties of a financial network are extremely sensitive to its network features. Our results can aid in the design of regulatory policies to improve the robustness of financial markets

Statistical Mechanics of Complex Networks for Systemic Risk Reconstruction

Systemic risk concerns the stability of systems composed by different parts, specifically the prediction and prevention of systemic events. A systemic event is typically defined as a phenomenon that emerges from the complex interactions of the constituents and compromises the normal functioning of the system. In particular, in finance the elementary constituents are financial institutions, such as banks, and systemic risk pertains the description and prevention of collapses that involve large portions of the financial system. After the recent troubled years for the global economy, in which two severe crises (the 2007 crisis of financial markets and the 2010 sovereign debt crisis) have put the whole economic system in dramatic distress, vulnerability of banks to systemic events is now the main focus of a growing number of investigations of the academic community, crosswise different disciplines. When a bank undergoes some sort of malfunctioning or distress, and its troubled situation negatively affects other institutions, we say that distress propagates, and that the interactions among the two institutions serves as channel of contagion. Networks are one of the main tools in the modelling and description systemic risk in financial systems, since they allow for a straightforward description of different channels of contagion. The indirect channel of contagion can be described as a bipartite network, i.e. a network where the set of nodes is sharply divided into two subsets, one containing only banks, the other only assets. Banks can only be connected with assets but not with other banks. Moreover, a link exist if the bank holds the asset in its portfolio, i.e. invests in that particular assets. In order to quantify losses from indirect contagion a full knowledge of how the banks divide their investments is needed. As quantifiers of systemic risk we consider systemicness and vulnerability of a bank as, respectively, the total percentage loss induced on the system by the distress of the bank and the total percentage loss experienced by the bank when the whole system is in distress. An important part of our thesis is dedicated to the empirical analysis of a dataset that we originally developed, collecting data publicly available from the databases of the Federal Reserve, that describes the quarterly networks of US commercial banks’ exposures in the period 2001-2015. Specifically we compute, for each quarter, systemicness and vulnerability of each bank and the aggregate vulnerability of the system. From our network description of the system, it emerges that there is a clear relation between the vulnerability of the system to external shocks and the way in which the largest banks present manage their investments, as we show in Figure 1. The central topic of the thesis is the problem of reconstructing systemic risk measures from partial information on the network, e.g. knowing only the size of the banks present and the capitalization of the assets available.Our main purpose is to develop efficient methods to estimate systemic risk from partial information, without the full knowledge of the bipartite network. A prolific analogy between statistical mechanics and the statistical inference of networks, has been proposed and exploited by physicists. Network ensembles can be defined, akin to grand canonical ensembles in statistical mechanics, and they can be used to efficiently reconstruct networks from the sole partial information available. Ensembles are endowed with a probability mass function on a set of graphs that depends on a set of parameters, that in turn need to be estimated numerically from the partial information available. Banks statistics, that are related to the network structure, are estimated through their expected values on the statistical ensemble. We implemented the numerical procedures needed to practically employ ensembles of bipartite networks to systemic risk reconstruction, and applied them to the reconstruction of systemicness and indirect vulnerability. In order to test the reconstruction capability of our ensembles, we assume that the balance sheet compositions of the banks are not known, estimate the measures of systemic risk, trough network ensembles, and finally compare the real statistics computed for our dataset, with the values inferred from partial information. This ex-post comparison allows us to asses whether our methods are appropriate when the network structure is actually unknown. While network ensembles based on Bose-Einstein statistic are now a standard tool in network science, we found them to be unfit for the reconstruction of systemic risk. In fact better suited ensembles, for the purpose of systemic risk reconstruction, are defined trough a bold extension to networks of the correct Boltzmann counting of states. We refer to the resulting ensembles as Maxwell-Boltzmann (MB) ensembles. Inspired by the analogy with statistical mechanics, we propose an extension of the diffused Max-Ent approach to the definition of network ensembles, and we refer to it as Minx-Ent. Via Minx-Entr we define a family of ensembles that includes as limiting cases BE and MB ensembles, and propose it as a new tool for the reconstruction of networks statistics. At the end of this thesis we propose a, previously unexplored, application of grand- canonical ensembles, to a subject not directly related to systemic risk, i.e. “filtering of complex networks”. Often complex networks, that describe real systems, are extremely dense. A large network with a high edge density may be hard to interpret, or visualize by traditional tools of network analysis. In addition, a large portion of the links might not be informative, or subject to measurement errors. Hence it is sometimes useful to extract sub-networks, that contain only a portion of the original nodes and links. The reduction of “noisy” networks, in order to maintain only relevant information, is known as information filtering in complex networks. We propose an original technique for the filtering of complex networks based on grand-canonical ensembles. A Portion of this thesis has been the object of the paper Di Gangi, Lillo, and Pirino (“Assessing systemic risk due to fire sales spillover through maximum entropy network recon- struction”), currently undergoing reviewing process

Recurrent Deep Neural Networks for Nucleosome Classification

Nucleosomes are the fundamental repeating unit of chromatin. A nucleosome is an 8 histone proteins complex, in which approximately 147–150 pairs of DNA bases bind. Several biological studies have clearly stated that the regulation of cell type-specific gene activities are influenced by nucleosome positioning. Bioinformatic studies have improved those results showing proof of sequence specificity in nucleosomes’ DNA fragment. In this work, we present a recurrent neural network that uses nucleosome sequence features representation for their classification. In particular, we implement an architecture which stacks convolutional and long short-term memory layers, with the main purpose to avoid the features extraction and selection steps. We have computed classifications using eight datasets of three different organisms with a growing genome complexity, from yeast to human. We have also studied the capability of the model trained on the highest complex species in recognizing nucleosomes of the other organisms

Polymorphism of cytochrome P450 (CYP) genes and response to chemiotherapy in patients with colorectal cancer (CRC)

Background: Genes coding for the cytochrome P450 (CYP) enzyme system implied in antineoplastic drug metabolism pathways are highly polymorphic. This may influence both carcinogen metabolism and drug pharmacodynamics modifying their therapeutic efficacy and side effects. Methods: We investigated the influence of genetic polymorphisms of CYP enzymes: rs1799853 (CYP2C9), rs35742686 (CYP2D), rs5030655 (CYP2D6/3), rs2740574 (CYP3A4/1) rs776746 (CYP3A5) on the response of chemotherapy and clinical outcomes, in a group of 56 patients affected by sporadic CRC, treated with the standard protocols. A total of 44 patients were in complete remission after treatment, 12 had persistence of the disease. Polymorphisms were typed using a competitive allele specific PCR assay (KASPar), developed by KBioscience. Statistical were analyzed using the χ2 test with Yates correction and Fisher's Exact Test. Significance was defined as p values <0.05. Results: No significant genetic contribute was observed for 4 of the 5 SNPs tested. A significant different genetic distribution between patients in complete remission after treatment and those of symptomatic patients was observed for the polymorphism C→T (rs1799853) responsible of an Arg144Cys change in CYP2C9 and associated with reduced enzyme activity (p=0.031, O.R.= 4.760, 95% C. I.: 1.237 to 18.311). Conclusions: These results suggest that rs1799853 is a functionaly relevant SNP of CYP2C9 that may influence the efficacy of therapy. Thus, pharmacogenetic biomarkers have the potential of optimizing chemotherapy for individual patient