Network Connectivity, Systemic and Systematic Risk
| Project Start: | 01/2016 |
| Status: | Completed |
| Researchers: | Monica Billio, Massimiliano Caporin, Aleksey Kolokolov, Roberto Panzica, Loriana Pelizzon, Zorka Simon |
| Area: | Financial Markets, Systemic Risk Lab |
| Funded by: | LOEWE |
This project was part of the team project "Systemic Risk and Network Connectivity".
Topic & Objectives
The project aims to shed light on understanding the propagation mechanisms behind the recent financial crisis. Part of the literature postulates that systemic risk is strictly related (if not equal to) systematic risk. In this extension, we will elaborate on this hypothesis and introduce a modeling framework where systemic and systematic risks co-exist. The model is a simplification of the Branger et al. (2014) model and a variation of the traditional Capital Asset Pricing Model (CAPM) where networks are used to infer the exogenous/lagged and contemporaneous links across assets. The econometric approach used to estimate the model refers to the spatial econometric framework, namely the use of concentrated likelihoods. In our framework network, connections are exogenously provided by direct exposures and indirect exposures.
The project has allowed extending the classic factor-based asset pricing model by including network linkages in linear factor models. We assume that the network linkages are exogenously provided. This extension of the model allows a better understanding of the causes of systematic risk.
Key Findings
- We show that (i) network exposures act as an inflating factor for systematic exposure to common factors; (ii) the power of diversification is reduced by the presence of network connections.
- Empirically, in the presence of network links, a misspecified traditional linear factor model presents residuals that are correlated and heteroskedastic. We support our claims with an extensive simulation experiment.
- This approach allows us to decompose the risk of a single asset (or a portfolio) into four components: (i) the systematic component, (ii) the idiosyncratic component, (iii) the impact of the asset interconnections on the systematic risk component, the contribution of network exposure to the systematic risk component, and (iv) the effect of interconnections on the effect of idiosyncratic risk on the systematic risk component (the amplification of idiosyncratic risks that generates systematic/non-diversifiable risk).
- Our approach also allows us to decompose the risk premium component of returns into three components: the risk premiums associated with (i) common factor exposures, (ii) the impact of asset connections on common factors and (iii) the amplification effects of idiosyncratic risk.
- The simulation analysis we perform shows that the new model we propose can be used to better understand the different components of systematic risk and volatilities and analyze the causes of systematic risk.
- Moreover, the simulation allows us to disentangle the error estimation of linear factor models that ignore the presence of network connections. In particular, we show that the residual correlations start drifting away from zero if network connectedness is ignored in the model estimation.
- Finally, we confirm the simulation analysis using real data based on equity returns and a network based on the input-output table.
Policy Implications
This new model is relevant for policymakers and regulators since they need to be aware of the implications of the different possible policy choices on network connections and their effects on equilibrium stock returns and volatilities. For investors and other market participants, the model is relevant since they need to understand whether and to what degree network connectivity has an impact on risk premiums, volatilities, and spillovers between markets.
Related Published Papers
| Author/s | Title | Year | Area | Keywords |
|---|---|---|---|---|
| Massimiliano Caporin, Aleksey Kolokolov, Roberto Renò |
Systemic Co-Jumps
Journal of Financial Economics |
2017 | Financial Markets, Systemic Risk Lab | Jumps; Return predictability; Systemic events; Variance risk premium |
| Giovanni Bonaccolto, Massimiliano Caporin, Roberto Panzica |
Estimation and Model-Based Combination of Causality Networks
Journal of Empirical Finance |
2019 | Financial Markets, Systemic Risk Lab | Granger causality, quantile causality, multi-layer network, network combination |
| Monica Billio, Massimiliano Caporin, Roberto Panzica, Loriana Pelizzon |
The Impact of Network Connectivity on Factor Exposures, Asset Pricing and Portfolio Diversification
International Review of Economics & Finance |
2023 | Financial Markets, Systemic Risk Lab | CAPM, volatility, network, interconnections, systematic risk |
Related Working Papers
| No. | Author/s | Title | Year | Area | Keywords |
|---|---|---|---|---|---|
| 228 | Roberto Panzica | Idiosyncratic Volatility Puzzle: The Role of Assets' Interconnections | 2018 | Financial Markets, Systemic Risk Lab | Idiosyncratic volatility puzzle; Networks; Expected Returns; Granger Causality |
| 149 | Massimiliano Caporin, Aleksey Kolokolov, Roberto Renò | Systemic Co-Jumps | 2016 | Financial Markets, Systemic Risk Lab | Jumps, Return predictability, Systemic events, Variance Risk Premium |
| 166 | Monica Billio, Massimiliano Caporin, Roberto Panzica, Loriana Pelizzon | The Impact of Network Connectivity on Factor Exposures, Asset Pricing and Portfolio Diversification | 2016 | Financial Markets, Systemic Risk Lab | CAPM, volatility, network, interconnections, systematic risk |
| 165 | Giovanni Bonaccolto, Massimiliano Caporin, Roberto Panzica | Estimation and Model-Based Combination of Causality Networks | 2017 | Financial Markets, Systemic Risk Lab | Granger causality, quantile causality, multi-layer network, network combination |
