1,720,995 research outputs found
Network Theory in Finance: Applications to Financial Contagion Analysis and Portfolio Optimization
Get PDFNetwork theory is a powerful tool for the analysis of complex systems, and in recent years a growing body of literature highlights its financial applications. This work explores two fields of application of network theory in finance. The first is the modelization of systemic risk and financial contagion in a banking network, the second is portfolio optimization. Systemic risk is studied in three chapters: first we estimate sparse partial correlation networks build from credit default swaps (CDS) spreads using tlasso, a methodology based on the multivariate t-Student distribution, suitable for data with fat tails and outliers. Then we propose an analysis based on network-∆CoVaR, a tail-risk network constructed using quantile graphical models. We study in depth the characteristics of the resulting networks, focusing in particular on the structural properties of the system. Finally, we study a liquidity contagion model in presence of a network with communities. The second field of application is portfolio optimization. In particular, the tlasso model is applied to the estimation of parameters in Markowitz style portfolios. The covariance matrix of a set of assets, and its inverse, the so-called precision matrix, are closely related to graphical models. Here, the previously cited tlasso model is used for the estimation of the precision matrix. Moreover, the interpretation of the precision matrix as a network opens the possibility to implement investment strategies based on network indicators. The main contributions of the thesis are the following: first, we introduce the use of tlasso in the financial literature, extending the results obtained. Then we introduce the network version of ∆CoVaR, and we propose an estimation procedure based on the SCAD penalization framework. Concerning the study of systemic risk, this work is among the first to focus on the presence of a community structure in a banking system, that is particularly relevant in the European setting, where national borders are still relevant divisions
Systemic Risk and Community Structure in the European Banking System
No full textFinancial contagion and systemic risk have become increasingly relevant after the financial crisis in 2008. Network theory is a powerful framework for the analysis of these phenomena and is becoming a standard tool in the literature. This paper investigates the properties of the European banking system, focusing on the community structure of the network to identify the potential channels for the propagation of financial distress. The network structure is estimated from the sparse partial correlation of CDS spreads using tlasso, a robust technique that induces sparsity in the network. The optimal community structure is then estimated by a procedure that maximises modularity. The analysis shows that, despite the high level of internationalization of the financial system, it exist a clear community structure that mirrors the geographical location of the banks. Finally, a decomposition of strength centrality based on the estimated community structure is provided. Such decomposition represents a useful and easy-to-implement tool to monitor the exposure to financial contagion, integrating the traditional risk management tools
Minimum deviation enhanced portfolio replication with expectiles
No full textThis work addresses the problem of enhanced portfolio replication, proposing a strategy based on the minimization of a novel deviation strategies based on expectiles. This measure allows to account asymmetrically for the differences between the portfolio and the benchmark, favouring positive deviations compared to negative ones. We show that the model nests the minimum TEV replication scheme. The empirical applications focuses on the Standard and Poor’s 100 index, for which we create replicating portfolios with a positive expected excess return. The results show that the replication scheme proposed here allows to overperform the benchmark out-of-sample, and that portfolios with < . allow to reduce the lower tail risk measured in terms of CVAR compared to the minimum TEV portfolio. The resulting portfolios show a sufficient level of diversification, and a controlled turnover
On the origin of systemic risk
Get PDFSystemic risk in the banking sector is usually associated with long periods of economic downturn and very large social costs. On one hand, shocks coming from correlated exposures towards the real economy may induce correlation in banks’ default probabilities thereby increasing the likelihood for systemic-tail events like the 2008 Great Financial Crisis. On the other hand, financial contagion also plays an important role in generating large-scale market failures, amplifying the initial shocks coming from the real economy. To study the sources of these rare phenomena, we propose a new definition of systemic risk (i.e. the probability of a large number of banks going into distress simultaneously) and thus we develop a multilayer microstructural model to study empirically the determinants of systemic risk. The model is then calibrated on the most comprehensive granular dataset for the euro area banking sector, capturing roughly 96% or EUR 23.2 trillion of euro area banks’ total assets over the period 2014-2018. The output of the model decompose and quantify the sources of systemic risk showing that correlated economic shocks, financial contagion mechanisms, and their interaction are the main sources of systemic events. The results obtained with the simulation engine resemble common market-based systemic risk indicators and empirically corroborate findings from existing literature. This framework gives regulators and central bankers a tool to study systemic risk and its developments, pointing out that systemic events and banks’ idiosyncratic defaults have different drivers, hence implying different policy responses
Systemic risk detection using an entropy approach in portfolio selection strategy
No full textThis paper focuses on the investigation and detection of systemic risk. Such risk significantly affects the financial markets and the banking sector, and is fundamental for macro-prudential regulation. To address this issue, we propose an early warning system to anticipate periods of distress. In particular, we consider systemic risk from the investors’ perspective, developing optimal portfolio strategies that incorporate such an early warning system based on different entropy measures to predict and hedge the occurrence of systemic risk. On top of this, we introduce a rule that, in periods of crisis, triggers a switch to a risk-free portfolio. In order to determine the optimal composition of a portfolio, we use a new double-optimization strategy, which consists of the maximization of selected performance ratios in the first step and the minimization of selected systemic risk indicators (CoVaR, Marginal expected shortfall) for a given expected return in the second step. An empirical analysis shows that the proposed strategy allows reducing the total risk of the portfolio and generally improves its profitability. We finally discuss how the introduction of these investment strategies may affect the overall stability of the financial system
Calibration of one-factor and two-factor Hull-White models using swaptions
Get PDFIn this paper, we analize a novel approach for calibrating the one-factor and the two-factor Hull–White models using swaptions under a market-consistent framework. The technique is based on the pricing formulas for coupon bond options and swaptions proposed by Russo and Fabozzi (J Fixed Income 25:76–82, 2016b; J Fixed Income 27:30–36, 2017b). Under this approach, the volatility of the coupon bond is derived as a function of the stochastic durations. Consequently, the price of coupon bond options and swaptions can be calculated by simply applying standard no-arbitrage pricing theory given the equivalence between the price of a coupon bond option and the price of the corresponding swaption. This approach can be adopted to calibrate parameters of the one-factor and the two-factor Hull–White models using swaptions quoted in the market. It represents an alternative with respect to the existing approaches proposed in the literature and currently used by practitioners. Numerical analyses are provided in order to highlight the quality of the calibration results in comparison with existing models, addressing some computational issues related to the optimization model. In particular, calibration results and sensitivities are provided for the one- and the two-factor models using market data from 2011 to 2016. Finally, an out-of-sample analysis is performed in order to test the ability of the model in fitting swaption prices different from those used in the calibration process
Economic shocks and contagion in the euro area banking sector: a new micro-structural approach
No full textThe financial system can become more vulnerable to systemic banking crises as the potential for contagion across financial institutions increases. This contagion risk could arise because of shifts in the interlinkages between financial institutions, including the volume and complexity of contracts between them, and because of shifts in the economic risks to which they are commonly exposed. Analysis of the euro area banking system’s interlinkages, using the newly available large exposure data, suggests that the system could be more vulnerable to financial contagion through long-term interbank exposures than noted in other studies. That said, common exposures to the real economy – a standard contagion channel in the literature – represent a potential source of individual bank distress and non-systemic events. This analysis also provides an insight into the changes in contagion risk in the system over time, helping us to interpret changes in market indicators of systemic risk, such as aggregated credit default swap (CDS) prices
Financial contagion in banking networks with community structure
No full textMonitoring and controlling financial contagion in banking systems is a challenging task, and micro-structural network contagion models are becoming fundamental policy tools for supervisors. A large body of literature studies the theoretical properties of the diffusion of financial shocks in banking networks, measuring the spread of different types of shocks in relationship to the structural properties. Recent studies have highlighted the relevance of network communities i.e. groups of banks with connections among them stronger than to the rest of the system. In the European Union, such communities may be related to country divisions, as a result of the progressive integration of national banking systems.In this work we study whether and how the presence of a community structure affects the diffusion of liquidity shocks in a simulated banking systems. As a starting hypothesis communities may influence contagion in two ways: a higher transitivity (or clustering) could generate loops that amplify contagion; on the other hand, shocks could be "trapped"in a community avoiding the transmission to the entire system. We find that the presence of communities highly affects contagion, increasing the amount of distress transmitted and the number of banks involved. The results are robust across a broad range of network specifications. We also test the potential effects on contagion risk of several stylized policies: the introduction of higher liquidity requirements, the definition of liquidity requirements based on network indicators, and interventions to improve the confidence in the market by individual banks (obtained for instance by policies that enhance transparency). Results can be of interest for regulators willing to study the diffusion of liquidity risk and to set up macro-prudential policy interventions
Network Theory in Finance: Applications to Financial Contagion Analysis and Portfolio Optimization
Get PDFNetwork theory is a powerful tool for the analysis of complex systems, and in recent years a growing body of literature highlights the usefulness of this approach in finance.
This thesis explores two particular fields of application of network theory in finance. The first is the modelization of systemic risk and financial contagion in a banking network, and is discussed in three chapters: first we estimate sparse partial correlation networks build from credit default swaps (CDS) spreads using tlasso, a methodology based on the multivariate t-Student distribution, suitable for data with fat tails and outliers. Then we propose an analysis based on network-DCoVaR, a tail-risk network constructed using quantile graphical models. We study in depth the characteristics of the resulting networks, focusing in particular on the structural properties of the system. Finally, we study a liquidity contagion model in presence of a network with communities.
The second field of application is portfolio optimization. In particular, the tlasso model is applied to the estimation of parameters in Markowitz style portfolios. The covariance matrix of a set of assets, and in particular its inverse, the so-called precision matrix, is closely related to graphical models. Here, the previously cited tlasso model is used for the estimation of the precision matrix. Moreover, the interpretation of the precision matrix as a network opens the possibility to implement investment strategies based on network indicators.
The main contributions of the thesis are the following: first, we introduced the use of tlasso in the financial literature, extending the results obtained. Then we introduce the network version of ∆CoVaR, and we propose an estimation procedure based on the SCAD penalization framework. Concerning the study of systemic risk, this work is among the first to focus on the presence of a community structure in a banking system, that is particular in the Europe setting where national borders are still relevant divisions
Network tail risk estimation in the European banking system
No full textMeasuring interconnectedness in a banking system to identify the potential transmission channels of systemic risk is a main issue for the analysis of financial stability. We develop a methodology based on conditional tail risk networks to assess the channels of transmission in a banking system and to identify the most relevant and/or fragile institutions. The networks are constructed using quantile graphical models and the proposed framework can be considered as a network extension of the ΔCoVaR approach by Adrian and Brunnermeier (2016). From the conditional tail risk networks we can then compute synthetic indices of systemic risk for each bank. An additional set of systemic risk indicators is computed by considering together the network of conditional tail risk and bank-specific indicators of credit risk (as an example we use the ratio of non-performing loans, NPL). The empirical analysis focuses on the European banking system and considers a panel of 36 representative banks. Among the main findings, we found evidence of regional clusters of interconnected banks, especially in crisis period. Moreover, in terms of interconnectedness alone, systemic risk is diffused relatively evenly across European banks, while the set of systemic indicators built using also NPL highlighted a concentration of risk in southern European countries
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