Irena Vodenska

Cascading Failures in Bi-partite Graphs: Model for Systemic Risk Propagation

As economic entities become increasingly interconnected, a shock in a financial network can provoke significant cascading failures throughout the system. To study the systemic risk of financial systems, we create a bi-partite banking network model composed of banks and bank assets and propose a cascading failure model to describe the risk propagation process during crises. We empirically test the model with 2007 US commercial banks balance sheet data and compare the model prediction of the failed banks with the real failed banks after 2007. We find that our model efficiently identifies a significant portion of the actual failed banks reported by Federal Deposit Insurance Corporation. The results suggest that this model could be useful for systemic risk stress testing for financial systems. The model also identifies that commercial rather than residential real estate assets are major culprits for the failure of over 350 US commercial banks during 2008–2011.

Partial correlation analysis: applications for financial markets

The presence of significant cross-correlations between the synchronous time evolution of a pair of equity returns is a well-known empirical fact. The Pearson correlation is commonly used to indicate the level of similarity in the price changes for a given pair of stocks, but it does not measure whether other stocks influence the relationship between them. To explore the influence of a third stock on the relationship between two stocks, we use a partial correlation measurement to determine the underlying relationships between financial assets. Building on previous work, we present a statistically robust approach to extract the underlying relationships between stocks from four different financial markets: the United States, the United Kingdom, Japan, and India. This methodology provides new insights into financial market dynamics and uncovers implicit influences in play between stocks. To demonstrate the capabilities of this methodology, we (i) quantify the influence of different companies and, by studying market similarity across time, present new insights into market structure and market stability, and (ii) we present a practical application, which provides information on the how a company is influenced by different economic sectors, and how the sectors interact with each other. These examples demonstrate the effectiveness of this methodology in uncovering information valuable for a range of individuals, including not only investors and traders but also regulators and policy makers.

Network of Interdependent Networks: Overview of Theory and Applications

Complex networks appear in almost every aspect of science and technology. Previous work in network theory has focused primarily on analyzing single networks that do not interact with other networks, despite the fact that many real-world networks interact with and depend on each other. Very recently an analytical framework for studying the percolation properties of interacting networks has been introduced. Here we review the analytical framework and the results for percolation laws for a network of networks (NON) formed by \(n\) interdependent random networks. The percolation properties of a network of networks differ greatly from those of single isolated networks. In particular, although networks with broad degree distributions, e.g., scale-free networks, are robust when analyzed as single networks, they become vulnerable in a NON. Moreover, because the constituent networks of a NON are connected by node dependencies, a NON is subject to cascading failure. When there is strong interdependent coupling between networks, the percolation transition is discontinuous (is a first-order transition), unlike the well-known continuous second-order transition in single isolated networks. We also review some possible real-world applications of NON theory.

Cohesiveness in Financial News and its Relation to Market Volatility

Motivated by recent financial crises, significant research efforts have been put into studying contagion effects and herding behaviour in financial markets. Much less has been said regarding the influence of financial news on financial markets. We propose a novel measure of collective behaviour based on financial news on the Web, the News Cohesiveness Index (NCI), and we demonstrate that the index can be used as a financial market volatility indicator. We evaluate the NCI using financial documents from large Web news sources on a daily basis from October 2011 to July 2013 and analyse the interplay between financial markets and finance-related news. We hypothesise that strong cohesion in financial news reflects movements in the financial markets. Our results indicate that cohesiveness in financial news is highly correlated with and driven by volatility in financial markets.

Coupled Network Approach To Predictability Of Financial Market Returns And News Sentiments

We analyze the network structure of lagged correlations among daily financial news sentiments and returns of financial market indices of 40 countries from 2002 to 2012. Using a spectral method, we decompose the network into bipartite sub-structures, and show that these sub-structures are relevant to the performance of prediction models, bridging concepts from network theory and time series analysis. Our results suggest that, at the daily level, endogenous influences among financial markets overwhelm exogenous influences of news outlets, and that changes in financial news sentiments respond to market movements more substantially than they drive them.

Systemic Risk and Vulnerabilities of Bank Networks

Stability of the banking system and macro-prudential regulation are essential for healthy economic growth. In this paper we study the European bank network and its vulnerability to stressing differ- ent bank assets. The importance of macro-prudential policy is emphasized by the inherent vulnerability of the financial system, high level of leverage, interconnectivity of system’s entities, similar risk exposure of financial institutions, and susceptibility for systemic crisis propagation through the system. Current stress tests conducted by the European Banking Authority do not take in consideration the connectivity of the banks and the potential of one bank vulnerability spilling over to the rest of the system. We create a bipartite network with bank nodes on one hand and asset nodes on the other with weighted links between the two layers based on the level of different countries’ sovereign debt holdings by each bank. We propose a model for systemic risk propagation based on common bank exposures to specific asset classes. We introduce the similarity in asset distribution among the banks as a measure of bank closeness. We link the closeness of asset distributions to the likelihood that banks will experience a similar level and type of distress in a given adverse scenario. We analyze the dynamics of tier 1 capital ratio after stressing the bank network and find that while the system is able to withstand shocks for a wide range of parameters, we identify a critical threshold for asset risk beyond which the system transitions from stable to unstable.

Understanding the Relationship between VIX and the S&P 500 Index Volatility

We study the VIX Index, often referred to as “the investor’s fear gauge,” relative to the observed volatility of the S&P 500 Index to investigate the relationship between these two measures of financial markets variability and to understand the directionality of their influence on one another. Calculated as a weighted average of put and call options on the S&P 500 Index, the VIX is considered as a forecasting indicator of the S&P 500 Index’s volatility over a one-month period. We examine the daily VIX and S&P 500 Index volatility data for the 20-year period between 1990 and 2009 and find that VIX lags the S&P 500 one-month volatility for the period that we study. Furthermore, we analyze the VIX Index and the S&P 500 volatility for different time periods, when the financial markets exhibit: (i) higher level of stability with volatility below two standard deviations from the mean and (ii) lower stability regimes, with volatilities above two standard deviations from the mean. We find that in general, the VIX overestimates the S&P 500 Index volatility during the stable financial market regimes, and underestimates the S&P 500 Index volatility throughout high volatility periods.

Economic and Political Effects on Currency Clustering Dynamics

We propose a new measure named the symbolic performance to better understand the structure of foreign exchange markets. Instead of considering currency pairs, we isolate a quantity that describes each currencys position in the market, independent of a base currency. We apply the k-means clustering algorithm to analyze how the roles of currencies change over time, from reference status or minimal appreciations and depreciations with respect to other currencies to large appreciations and depreciations. We show how different central bank interventions and economic and political developments, such as the cap of the Swiss franc to the euro enforced by the Swiss National Bank or the Brexit vote, affect the position of a currency in the global foreign exchange network.

Confidence and self-attribution bias in an artificial stock market

Using an agent-based model we examine the dynamics of stock price fluctuations and their rates of return in an artificial financial market composed of fundamentalist and chartist agents with and without confidence. We find that chartist agents who are confident generate higher price and rate of return volatilities than those who are not. We also find that kurtosis and skewness are lower in our simulation study of agents who are not confident. We show that the stock price and confidence index—both generated by our model—are cointegrated and that stock price affects confidence index but confidence index does not affect stock price. We next compare the results of our model with the S&P 500 index and its respective stock market confidence index using cointegration and Granger tests. As in our model, we find that stock prices drive their respective confidence indices, but that the opposite relationship, i.e., the assumption that confidence indices drive stock prices, is not significant.

Impact of Bankruptcy Through Asset Portfolios: Network Analytic Solution Unveils 1990s Japanese Banking Crisis

We investigate the Japanese banking crisis in the late 1990s with a simple network based mathematical model, which allows us to simulate the crisis as well as to obtain new perspective through analytic solution of our network model. We effectively identify the actual bankrupted banks and the robustness of the banking system using a simulation model based on properties of a bi-partite bank-asset network. We show the mean time property and analytical solution of the model revealing aggregate time dynamics of bank asset prices throughout the banking crisis. The results disclose simple but fundamental property of asset growth, instrumental for understanding the bank crisis. We also estimate the “selling pressure” for each asset type, derived from a Cascading Failure Model (CFM), offering new perspective for investigating the phenomenon of banking crisis.