Massimiliano Caporin, Daniele Girolimetto, Emanuele Lopetuso
arXiv 18 Mar 2026 · Statistics — Applications
arXiv:2603.17463 · PDF · DOI · OpenAlex · Extracted main text
We assess the advantage of combining univariate and multivariate portfolio risk forecasts with the aid of forecast reconciliation techniques. In our analyzes, we assume knowledge of portfolio weights, a standard for portfolio risk management applications. With an extensive simulation experiment, we show that, if the true covariance is known, forecast reconciliation improves over a standard multivariate approach, in particular when the adopted multivariate model is misspecified. However, if noisy proxies are used, correctly specified models and the misspecified ones (for instance, neglecting spillovers) turn out to be, in several cases, indistinguishable, with forecast reconciliation still providing improvements. The noise in the covariance proxy plays a crucial role in determining the improvement of both the forecast reconciliation and the correct model specification. An empirical analysis shows how forecast reconciliation can be adopted with real data to improve traditional GARCH-based portfolio variance forecasts.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Wickramasuriya, Shanika L. and Athanasopoulos, George and Hyndman, R… (2019) Optimal Forecast Reconciliation for Hierarchical and Grouped Time Series Through Trace Minimization | 0.928 | 4 | 3 | 100% |
| 2 | Caporin, M. and Di Fonzo, T. and Girolimetto, D (2024) Exploiting Intraday Decompositions in Realized Volatility Forecasting: A Forecast Reconciliation Approach self | 0.811 | 4 | 2 | 100% |
| 3 | Engle, R.F. and Kroner, K.K (1995) Multivariate simultaneous generalized ARCH | 0.811 | 4 | 2 | 100% |
| 4 | Panagiotelis, Anastasios and Athanasopoulos, George and Gamakumara,… (2021) Forecast reconciliation: A geometric view with new insights on bias correction | 0.737 | 3 | 2 | 100% |
| 5 | Athanasopoulos, George and Hyndman, Rob J. and Kourentzes, Nikolaos… (2024) Forecast reconciliation: A review | 0.644 | 2 | 2 | 100% |
| 6 | Ding, Z. and R. Engle (2001) Large scale conditional covariance modeling, estimation and testing matrix | 0.644 | 2 | 2 | 100% |
| 7 | Engle, R.F (2002) Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models | 0.644 | 2 | 2 | 100% |
| 8 | Girolimetto, Daniele and Di Fonzo, Tommaso (2024) Point and probabilistic forecast reconciliation for general linearly constrained multiple time series self | 0.644 | 2 | 2 | 100% |
| 9 | Hyndman, Rob J. and Ahmed, Roman A. and Athanasopoulos, George and S… (2011) Optimal combination forecasts for hierarchical time series | 0.644 | 2 | 2 | 100% |
| 10 | Laurent, S. and Rombouts, J.V.K. and Violante, F (2013) On loss function and ranking forecasting performances of multivariate volatility models | 0.644 | 2 | 2 | 100% |
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