Network-based asset allocation strategies

Abstract

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractIn this study, we construct financial networks in which nodes are represented by assets and where edges are based on long-run correlations. We construct four networks (complete graph, a minimum spanning tree, a planar maximally filtered graph, and a threshold significance graph) and use three centrality measures (betweenness, eigenvalue centrality, and the expected force). To improve riskreturn characteristics of well-known return maximization and risk minimization benchmark portfolios, we propose simple adjustments to portfolio selection strategies that utilize centralization measures from financial networks. From a sample of 45 assets (stock market indices, bond and money market instruments, commodities, and foreign exchange rates) and from data for 1999 to 2015, we show that irrespective of the network and centrality employed, the proposed network-based asset allocation strategies improve key portfolio return characteristics in an out-of-sample framework, most notably, risk and left-tail risk-adjusted returns. Resolving portfolio model selection uncertainties further improves risk-return characteristics. Improvements made to portfolio strategies based on risk minimization are also robust to transaction costs.

A Weighted and Directed Perspective of Global Stock Market Connectedness: A Variance Decomposition and GERGM Framework

“…The inner logic of financial networks is that the intricate connectedness, either physical based on bilateral exposures/flows between financial institutions, or association-based depicting return dependency among financial markets, could be captured and analyzed in complex financial systems using a network approach [1,2,6]. The network approach, which describes relationship architecture and regularities involved in complex multivariate systems, has become a powerful tool in financial crises early warning and tracking [7,8], risk spillover sources tracing [9,10], or exploitation of asset allocation [11,12]. Three research paradigms exist in the current financial network literature [1], namely, (i) mean-spillover network or Granger-causality network [13], (ii) volatility spillover network represented by variance decomposition-based network [14] and GARCH-based network [15], and (iii) risk spillover network with the main forms in tail-risk driven network [16] and extreme risk network [8].…”

Network Models to Enhance Automated Cryptocurrency Portfolio Management

“…Peralta and Zareei ( 2016 ) show that the centrality of assets within a network are negatively related with the optimal weights obtained through the Markowitz technique. Building on that, Vỳrost et al ( 2018 ) conclude that asset allocation strategies including the network structure of financial asset are able to improve a portfolio's risk-return profile.…”

International Financial US Linkages: Networks Theory and MS-VAR Analyses

“…Dealing directly with stock exchanges activity, networks theory research is limited and regarding international capital flows is absent, which underlines the importance of this paper. Nevertheless, it is worth mentioning recent works by Baitinger and Papenbrock (2017), Sandoval Junior (2017) and Výrost, Lyóska and Baulmöhl (2019). The former, propose to overcome the limitations of conventional mean-variance thinking; they introduce a model dealing with financial networks, and their active management which compares mutual-information-based networks with correlation-based networks on a stand-alone basis and in the framework of investment strategies in course.…”

“…Additionally, Sandoval Junior's results dealing with volatilities also present considerable ties between the exchange indexes of Middle Eastern countries. Finally, Výrost, Lyóska, and Baulmöhl (2019) propose centralization measures from financial networks to improve portfolio returns in an out-of-sample framework. In their network, nodes are represented by assets, while edges are based on long-run correlations.…”