We model the impact of bank mergers on loan competition, reserve holdings and aggregate liquidity. A merger creates an internal money market that affects reserve holdings and induces financial cost advantages, but also withdraws liquidity from the interbank market. Loan market competition...

There are many indications that formal methods are not used to their full potential by central banks today. In this paper we demonstrate how BVAR and DSGE models can be used to shed light on questions that policy makers deal with in practice using data from Sweden. We compare the forecast...

Using an empirical New-Keynesian model with optimal discretionary monetary policy, we calibrate key parameters - the central bank's preference parameters; the degree of forward-looking behavior in the determination of inflation and output; and the variances of inflation and output shocks - to...

This paper analyzes the interaction between oil prices and macroeconomic outcomes by incorporating oil as an input in production alongside a precautionary motive for holding oil in a real-business-cycle model. The driving forces are factor-specific technology shocks and supply shocks that can be...

We measure the incidence of latency arbitrage for cross-listed stocks around the time of an exogenous shock that made the markets faster. Our sample is from NASDAQ Nordic and consists of Nordic blue chip firms listed and traded in multiple markets. We document a sharp decline in the incidence of...

Cross institutional forecast evaluations may be severely distorted by the fact that forecasts are made at different points in time, and thus with different amount of information. This paper proposes a method to account for these differences. The method computes the timing effect and the...

The computing time for Markov Chain Monte Carlo (MCMC) algorithms can be prohibitively large for datasets with many observations, especially when the data density for each observation is costly to evaluate. We propose a framework where the likelihood function is estimated from a random subset of...

We propose a generic Markov Chain Monte Carlo (MCMC) algorithm to speed up computations for datasets with many observations. A key feature of our approach is the use of the highly efficient difference estimator from the survey sampling literature to estimate the log-likelihood accurately using...

We develop a novel high-dimensional non-Gaussian modeling framework to infer measures of conditional and joint default risk for many financial sector firms. The model is based on a dynamic Generalized Hyperbolic Skewed-t block-equicorrelation copula with time-varying volatility and dependence...