The U.S. Federal Reserve (Fed) is expected to start raising policy interest rates in the near term and thus commence a tightening cycle for the first time in nearly a decade. The taper tantrum episode of May-June 2013 is a reminder that even a long anticipated change in Fed policies can trigger...

We study the European Union’s Emission Trading System (EU ETS) from a financial perspective. Using ARMA-eGARCH filtered volatilities, we first discuss the evolution of the volatility of EU ETS allowances’ returns from 2008 to 2021. Second, we study the degree of co-movement and...

Data on short investments in Swedish long-term bonds as the bonds mature contains unusually rich information about the relationship between duration and the first and second moments of bond returns. We identify three different channels through which duration affects bond returns. The liquidity...

The paper presents an empirical study of volatility spillover from oil prices to stock markets within an asymmetric BEKK model. Using weekly data on the aggregate stock markets of Japan, Norway, Sweden, the U.K., and the U.S., strong evidence of volatility spillover is found for all stock...

Conditional heteroskedasticity can be exploited to identify the structural vector autoregressions (SVAR) but the implications for inference on structural impulse responses have not been investigated in detail yet. We consider the conditionally heteroskedastic SVAR-GARCH model and propose a...

This paper injects factor structure into the estimation of time-varying, large-dimensional covariance matrices of stock returns. Existing factor models struggle to model the covariance matrix of residuals in the presence of conditional heteroskedasticity in large universes. Conversely,...

Two basic solutions have been proposed to fix the well-documented incompatibility of the sample covariance matrix with Markowitz mean-variance portfolio optimization: first, restrict leverage so much that no short sales are allowed; or, second, linearly shrink the sample covariance matrix towards...

Modeling and forecasting dynamic (or time-varying) covariance matrices has many important applications in finance, such as Markowitz portfolio selection. A popular tool to this end are multivariate GARCH models. Historically, such models did not perform well in large dimensions due to the...

Multivariate GARCH models do not perform well in large dimensions due to the so-called curse of dimensionality. The recent DCC-NL model of Engle et al. (2019) is able to overcome this curse via nonlinear shrinkage estimation of the unconditional correlation matrix. In this paper, we show how...

Markowitz portfolio selection is a cornerstone in finance, both in academia and in the industry. Most academic studies either ignore transaction costs or account for them in a way that is both unrealistic and suboptimal by (i) assuming transaction costs to be constant across stocks and (ii)...