Publications - Niels Strange Grønborg

Realizing Correlations Across Asset Classes

Grønborg, N. S., Lunde, A., Olesen, K. V., Elst, H. V. — Wed, 01 Jun 2022 21:30:41 +0200

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.

Asset pricing and FOMC press conferences

Bodilsen, S., Eriksen, J. N., Grønborg, N. S. — Tue, 01 Jun 2021 21:30:41 +0200

A press conference (PC) organized by the Federal Open Market Committee (FOMC) followed half of the scheduled announcements from 2011 to 2018. We document that excess stock returns are strongly and positively related to their betas on announcement days with a PC. In addition, the cross-sectional dispersion in betas declines substantially on PC days when measured using both daily and intraday return data. These effects are absent on announcement days without a PC. Last, we find that stock-bond correlations are positive (negative) on PC (all other) days and that their variations are related to uncertainty and yield curve information. We discuss implications and possible explanations for our findings.

Picking Funds with Confidence

Grønborg, N. S., Lunde, A., Timmermann, A., Wermers, R. — Fri, 01 Jan 2021 21:30:41 +0100

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.

Mutual Fund Selection for Realistically Short Samples

Christiansen, C., Grønborg, N. S., Nielsen, O. L. — Wed, 01 Jan 2020 21:30:41 +0100

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.

Analyzing Oil Futures with a Dynamic Nelson-Siegel Model

Hansen, N. S., Lunde, A. — Fri, 01 Jan 2016 21:30:41 +0100

The dynamic Nelson-Siegel model is used to model the term structure of futures contracts on oil and obtain forecasts of prices of these contracts. Three factors are extracted and modelled in a very flexible framework. The outcome of this exercise is a class of models which describes the observed prices of futures contracts well and performs better than conventional benchmarks in realistic real-time out-of-sample exercises.