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Fleckenstein et al. (2014) document that nominal Treasuries trade at higher prices than inflation-swapped indexed bonds, which exactly replicate the nominal cash flows. We study whether this mispricing arises from liquidity premiums in inflation-indexed bonds (TIPS) and inflation swaps. Using US data, we show that the level of liquidity affects TIPS, whereas swap yields include a liquidity risk premium. We also allow for liquidity effects in nominal bonds. These results are based on a model with a systematic liquidity risk factor and asset-specific liquidity characteristics. We show that these liquidity (risk) premiums explain a substantial part of the TIPS underpricing.
Data show that sovereign risk reduces liquidity, increases funding cost and risk of banks highly exposed to it. I build a model that rationalizes this fact. Banks act as delegated monitors and invest in risky projects and in risky sovereign bonds. As investors hear rumors of increased sovereign risk, they run the bank (via global games). Banks could rollover liquidity in repo market using government bonds as collateral, but as sovereign risk raises collateral values shrink. Overall banks’ liquidity falls (its cost increases) and so does banks’ credit. In this context noisy news (announcements with signal extraction) of consolidation policies are recessionary in the short run, as they contribute to investors and banks pessimism, and mildly expansionary in the medium run. The banks liquidity channel plays a major role in the fiscal transmission.
We introduce a copula-based dynamic model for multivariate processes of (non-negative) high-frequency trading variables revealing time-varying conditional variances and correlations. Modeling the variables’ conditional mean processes using a multiplicative error model we map the resulting residuals into a Gaussian domain using a Gaussian copula. Based on high-frequency volatility, cumulative trading volumes, trade counts and market depth of various stocks traded at the NYSE, we show that the proposed copula-based transformation is supported by the data and allows capturing (multivariate) dynamics in higher order moments. The latter are modeled using a DCC-GARCH specification. We suggest estimating the model by composite maximum likelihood which is sufficiently flexible to be applicable in high dimensions. Strong empirical evidence for time-varying conditional (co-)variances in trading processes supports the usefulness of the approach. Taking these higher-order dynamics explicitly into account significantly improves the goodness-of-fit of the multiplicative error model and allows capturing time-varying liquidity risks.
Euro area data show a positive connection between sovereign and bank risk, which increases with banks’ and sovereign long run fragility. We build a macro model with banks subject to moral hazard and liquidity risk (sudden deposit withdrawals): banks invest in risky government bonds as a form of capital buffer against liquidity risk. The model can replicate the positive connection between sovereign and bank risk observed in the data. Central bank liquidity policy, through full allotment policy, is successful in stabilizing the spiraling feedback loops between bank and sovereign risk.
Euro area data show a positive connection between sovereign and bank risk, which increases with banks’ and sovereign long run fragility. We build a macro model with banks subject to incentive problems and liquidity risk (in the form of liquidity based banks’ runs) which provides a link between endogenous bank capital and macro and policy risk. Our banks also invest in risky government bonds used as capital buffer to self-insure against liquidity risk. The model can replicate the positive connection between sovereign and bank risk observed in the data. Central bank liquidity policy, through full allotment policy, is successful in stabilizing the spiraling feedback loops between bank and sovereign risk.