In this article, we use partial correlations to derive bi‐directional connections between major firms listed in the Moscow Stock Exchange. We obtain coefficients of partial correlation from the correlation estimates of the Constant Conditional Correlation GARCH (CCC‐GARCH) and the consistent Dynamic Conditional Correlation GARCH (cDCC‐GARCH) models. We map the graph of partial correlations using the Gaussian Graphical Model and apply network analysis to identify the most central firms in terms of both shock propagation and connectedness with others. Moreover we analyze some network characteristics over time and based on these we construct a measure of system vulnerability to external shocks. Our findings suggest that during the crisis interconnectedness between firms strengthens and becomes polarized and the system becomes more vulnerable to systemic shocks. In addition, we found that the most connected firms are the state‐owned firms Sberbank and Gazprom and the private oil company Lukoil, while in terms of the top most central systemic risk contributors, Sberbank gave its place to the NLMK Group.

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We develop a novel filtering and estimation procedure for parametric option pricing models driven by general affine jump-diffusions. Our procedure is based on the comparison between an option-implied, model-free representation of the conditional log-characteristic function and the model-implied conditional log-characteristic function, which is functionally affine in the model's state vector. We formally derive an associated linear state space representation and establish the asymptotic properties of the corresponding measurement errors. The state space representation allows us to use a suitably modified Kalman filtering technique to learn about the latent state vector and a quasi-maximum likelihood estimator of the model parameters, which brings important computational advantages. We analyze the finite-sample behavior of our procedure in Monte Carlo simulations. The applicability of our procedure is illustrated in two case studies that analyze S&P 500 option prices and the impact of exogenous state variables capturing Covid-19 reproduction and economic policy uncertainty.

In this article we use partial correlations to derive bidirectional connections between major …rms listed in the Moscow Stock Exchange. We obtain coe¢ cients of partial correlation from the correlation estimates of the Constant Conditional Correlation GARCH (CCC-GARCH) and the consistent Dynamic Conditional Correlation GARCH (cDCC-GARCH) models. We map the graph of partial correlations using the Gaussian Graphical Model and apply network analysis to identify the most central …rms in terms of both shock propagation and connectedness with others. Moreover, we analyze some network characteristics over time and based on these we construct a measure of system vulnerability to external shocks. Our …ndings suggest that during the crisis interconnectedness between …rms strengthens and becomes polarized and the system becomes more vulnerable to systemic shocks. In addition, we found that the most connected …rms are the state-owned …rms Sberbank and Gazprom and the private oil company Lukoil, while in the top most central in terms of systemic risk contributors Sberbank gave its place to NLMK Group.