Sustainable Risk Management of Financial Institution Investments: A Cbsprcv-At-Risk Capital Framework

Is it possible to propose bootstrapped regression coefficient series which possess time-series element, considering they emerged from the idiosyncratic regression residuals? If it is so, then a generalization of traditional autoregressive conditional volatility based value-at-risk model and thereby ascertaining risk capital under pointback test (Traffic signal approach) can be meaningful. Using above methodology institutions can provide reasonable justification of “risk exposure” towards intra-industry investments with idiosyncratic wage data as a decision variable. The present paper use the similar ideology stated above, considering a robust autoregressive series of bootstrapped regression coefficients as a proxy for empirical conditional systematic (micro-systematic to be precise) risk series and leading to creation of a “Risk capital” measure for Banks to ascertain the “uninsured illiquid securities/assets risk capital buffer” they may have to ascertain under extreme risk prepositions. The paper clearly demonstrates how robust risk capital (Conditional bootstrapped shadow price regression coefficient variance: CBSPRCV-at- risk capital) of shadow assets (human capital costs) makes relevance in modern economic environment of unreliable market framework.