Integrated risk management for financial institutions requires an approach for aggregating risk types (market, credit, and operational) whose distributional shapes vary considerably. We construct the joint risk distribution for a typical large, internationally active bank using the method of copulas. This technique allows us to incorporate realistic marginal distributions that capture essential empirical features of these risks such as skewness and fat-tails while allowing for a rich dependence structure. We explore the impact of business mix and inter-risk correlations on total risk. We then compare the copula-based method with several conventional approaches to computing risk.

Using 31 years of data (1990–2020) on U.S. Treasury dealer positions, we find that Treasury issuance is the main driver of dealers’ weekly inventory changes. Such inventory fluctuations are only partially offset in adjacent weeks and not significantly hedged with futures. Dealers are compensated for inventory risk by means of subsequent price appreciation of their holdings. Amid increased balance sheet costs attributable to post-crisis regulatory changes, dealers significantly reduce their position taking and layoff inventory faster. Moreover, the increased participation of non-dealers (investment funds) in the primary market contributes to diminishing compensation for inventory risk taken on at auctions.

We explore the cross‐sectional pricing of volatility risk by decomposing equity market volatility into short‐ and long‐run components. Our finding that prices of risk are negative and significant for both volatility components implies that investors pay for insurance against increases in volatility, even if those increases have little persistence. The short‐run component captures market skewness risk, which we interpret as a measure of the tightness of financial constraints. The long‐run component relates to business cycle risk. Furthermore, a three‐factor pricing model with the market return and the two volatility components compares favorably to benchmark models.