The paper develops a network-based framework to assess the impact of a firm's financial interconnections and interdependencies on it optimal hedging and risk transfer strategy. This is important consideration as risk concentrations and propagation embedded in risk transfers pose as a significant challenge for financial regulations and risk management. Using an a priori and an a posteriori analysis, we investigate the impact of the network structure on optimal hedge decisions and hedge efficacy. The a priori analysis shows that network features play an important role in determining corporate hedging decisions through their impact on hedge costs. In the a posteriori analysis for counterparty risk, benefit of modification from the a priori optimal hedge decision is realized when variance of firm value matters, but not when tail risk measures are utilized. Financial network assessment indicates that an a priori analysis is insufficient for robust hedging decisions, and choice of risk measures behind risk management objectives dictates the extent of impact of network characteristics.

This paper presents a new framework to model and calibrate the process of firm value evolution when an unanticipated exogenous event impacting one firm can contagiously affect other firms. The nature of propagation of such contagion is determined by the underlying connections between firms, which can adversely affect the tail risks of firm value, hence the securities issued by the firm. This paper combines the insights gained from the existing firm-value models and historical events into a structural model for flow of contagion among firms using a network-based approach. Rather than using stylized networks, we develop a data-driven approach for network construction where we define and calibrate several contagion variables to model the spread of contagion. This framework is applied for assessing firm-level risk under downside risk measures. Using actual data, our model illustrates how connections between firms can lead to heavy-tailed default distributions and default clustering observed in practice.

We develop a network representation-based methodology to aid an exploratory analysis of temporally evolving comovement in asset prices. This parsimonious order-n representation of the most significant comovement in asset prices, filtered by common factors, allows tackling a large number of assets and unraveling their complex comovement structure. Flexibility in choosing explanatory factors to suit the specific objectives of a study makes this methodology useful for portfolio analysis, risk parity approaches, and risk management decisions. We illustrate the features of the methodology for a set of major industry equity indices and to blue chip stocks, where we analyze the dynamic relevance of Fama–French factors. Investigating the network for more than 20 years, including the dot-com bust, global financial crisis, and European debt crisis, helps draw many insights. For instance, unexpected industries are seen to connect idiosyncratically through the dot-com bust. We demonstrate that a network factor model based portfolio allocation performs better than a regular factor model based allocation.