Portfolio sorting is ubiquitous in the empirical finance literature, where it has been widely used to identify pricing anomalies in different asset classes. Despite the popularity of portfolio sorting, little attention has been paid to the statistical properties of the procedure or to the...

In this paper we empirically investigate the consequences of domestic systemic risk for stock market investors. To tackle this issue, we consider two different investment strategies. One strategy is to be 'crisis-conscious,' i.e. taking the possibility of systemic events into account, the other...

The paper empirically analyzes stock market integration and the benefit possibilities of international portfolio diversification across the Southeast Asia (ASEAN) and U.S. equity markets. It employs daily sample of 6 ASEAN equity market indices and S&P 500 index as a proxy of U.S. market index...

It is well known that strategies that allow investors to allocate their wealth using return and volatility forecasts, the use of which are termed market and volatility timing, are of significant value. In this paper, we show that distribution timing, defined here as the ability to use forecasts...

We propose a novel risk matrix to characterize the optimal portfolio choice of an investor with tail concerns. The diagonal of the matrix contains the Value-at-Risk of each asset in the portfolio and the off-diagonal the pairwise Delta-CoVaR measures reflecting tail connections between assets....

Passive investment strategies basically aim to replicate an underlying benchmark. Thereby, the management usually selects a subset of stocks being employed in the optimization procedure. Apart from the optimization procedure, the stock selection approach determines the stock portfolios's...

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...

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...

Contrary to the theoretical principle that higher risk is compensated with higher expected return, the literature shows that low-risk stocks outperform high-risk stocks. Using a large-scale household dataset, we provide an explanation for this puzzling result that the anomalous negative...

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...