Publications - Niels Strange Grønborg en-us PURE Extension (Web Department) 30 <![CDATA[Realizing Correlations Across Asset Classes]]> Grønborg, N. S., Lunde, A., Olesen, K. V., Elst, H. V. We introduce a simple and intuitive composite model for forecasting correlations for use in portfolio optimization. Each element of the composite model is based on a realized volatility model. To test our model, we consider an investor seeking to diversify an equity portfolio by including commodities. In a high-frequency setting, we demonstrate that significant economic gains can be achieved by basing portfolio decisions on our modeling framework. The gains depend on the quality of the chosen volatility model, and for our preferred model, they are economically significant despite the realistic constraints on short selling and portfolio turnover.

Research Wed, 01 Jun 2022 18:19:44 +0200 5beb6098-a907-4dfb-994b-451ee47e416b
<![CDATA[Asset pricing and FOMC press conferences]]> Bodilsen, S., Eriksen, J. N., Grønborg, N. S. Research Tue, 01 Jun 2021 18:19:44 +0200 7c61d057-08ae-4057-b856-08ca4365a539 <![CDATA[Picking Funds with Confidence]]> Grønborg, N. S., Lunde, A., Timmermann, A., Wermers, R. We present a new approach to selecting actively managed mutual funds that uses both portfolio holdings and fund return information to eliminate funds with predicted inferior performance through a sequence of pairwise fund comparisons. Our methodology determines both the number of skilled funds and their identities, and locates funds with substantially higher risk-adjusted returns than those identified by conventional alpha-ranking methods. We find strong evidence of time-series variation in both the number of funds identified as superior using our approach, as well as in their performance across different economic states.

Research Fri, 01 Jan 2021 18:19:44 +0100 e4c6c8ee-0504-4493-979c-eb9127389740
<![CDATA[Mutual Fund Selection for Realistically Short Samples]]> Christiansen, C., Grønborg, N. S., Nielsen, O. L. Performance of mutual fund selection methods is typically assessed using long samples (long time series). We investigate how well the methods perform in shorter samples. We carry out an extensive simulation study based on empirically motivated skill distributions. For both short and long samples, we present evidence of large differences in performance between popular fund selection methods. In an empirical analysis, we show that the differences documented by the simulations are empirically relevant.

Research Wed, 01 Jan 2020 18:19:44 +0100 f37328c3-2da7-4c7c-ab0e-5c506c9cbe75
<![CDATA[Analyzing Oil Futures with a Dynamic Nelson-Siegel Model]]> Hansen, N. S., Lunde, A. Research Fri, 01 Jan 2016 18:19:44 +0100 865b2fbe-c1ac-411d-82c4-21130c813325