Realized-VAR: Estimating Financial Networks by Realized Interdependencies

We develop a network-based vector autoregressive approach to uncover the interactions among financial assets by integrating multiple realized measures. Under a restricted parameter structure characterized by a single integrated multimeasure network, our approach captures cross-sectional and time dependencies embedded in a large panel of assets. We propose a block coordinate descent procedure for the least square estimation, investigate its theoretical properties and assess estimation consistency through simulations. Using data on U.S. stocks, we identify a large array of interdependencies with a limited computational effort. We provide a new ranking for the systemically important financial institutions and carry out an impulse-response analysis to quantify the effects of adverse shocks on the financial system. Exploiting information through the integration of multiple realized measures, our method leads to significantly more accurate out-of-sample forecasts, compared to single measure models and competing Principal Component Analysis (PCA)-based factor model specifications.