We analyze the effects of prudential regulation on short-term interest rates. The European Market Infrastructure Regulation (EMIR) induces clearing houses (CCPs) to supply large amounts of cash in reverse repurchase agreements (repos). Basel III, in contrast, disincentivizes the borrowing demand by tightening banks’ balance sheet constraints. Using unique regulatory data of CCP investment activity and repo transactions, we find compelling evidence for both the supply and demand channels. The overall effects are decreasing short-term rates and increasing market imbalances in various forms, all of which entail unintended consequences due to the new regulatory framework.

Using unique data at transaction and identity levels, we provide the first systematic study of interest rate swaps traded over the counter (OTC). We find substantial and persistent heterogeneity in derivative prices consistent with a pass-through of regulatory costs on to market prices via so-called valuation adjustments (XVA). A client pays a higher price to buy interest rate protection from a dealer (i.e., the client pays a higher fixed rate) if the contract is not cleared via a central counterparty. This OTC premium decreases by posting initial margins and with higher buyer’s creditworthiness. OTC premia are absent for dealers suggesting bargaining power.

We use proprietary transaction data on interest rate swaps to assess the effects of centralized trading, as mandated by Dodd–Frank, on market quality. Contracts with the most extensive centralized trading see liquidity metrics improve by between 12% and 19% relative to those of a control group. This is driven by a clear increase in competition between dealers, particularly in U.S. markets. Additionally, centralized trading has caused interdealer trading in EUR swap markets to migrate from the United States to Europe. This is consistent with swap dealers attempting to avoid being captured by the trade mandate in order to maintain market power.

We introduce a novel weighted least squares approach to estimate daily realized covariation and microstructure noise variance using high-frequency data. We provide an asymptotic theory and conduct a comprehensive Monte Carlo simulation to demonstrate the desirable statistical properties of the new estimator, compared with existing estimators in the literature. Using high-frequency data of 27 DJIA constituting stocks over a period from 2014 to 2020, we confirm that the new estimator performs well in comparison with existing estimators. We also show that the noise variance extracted based on our method can be used to improve volatility forecasting and asset allocation performance.