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Robust portfolio optimization and management

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Modern Time Series Tools for Scenario Analysis and Portfolio Optimization robust portfolio optimization and management

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Preprint Free to read. The fast-growing Emerging Market EM economies and their source transparency and liquidity have attracted international investors.

However, the external price shocks can result in a higher level of volatility as well as domestic policy instability. Therefore, an efficient risk measure and hedging strategies are needed to help investors protect their investments against this risk. We then study the relationship between those factors and the tail event network behavior to build our policy recommendations to help the investors to choose the suitable market for investment and tail-event optimized portfolios. For that purpose, an overlapping region between optimizattion optimization strategies and FRM network centrality is developed.

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We propose a robust and well-diversified tail-event and cluster risk-sensitive portfolio allocation model and compare it to more classical approaches. Emerging markets have been commonly acclaimed for providing robust growth potential and offering investors a higher expected return compared to developed markets. Indeed, due to the possibility of higher profits and the low level of global equity markets integration, EMs have been considered as an investment opportunity for investors, whose aim to build an internationally diversified portfolio. Moreover, the reputation of EMs, in the framework of portfolio diversification, has received the attention of international investors, especially after the financial crisis that affected mostly developed markets.

In fact, EMs are exposed to additional economic, political, and currency risks. Therefore, the existing more traditional methods of risk evaluation may be misleading in EMs, especially in short and medium investment horizons. Indeed, the existing risk measure methods are not suited to provide the up to date point of view representing current market structures, if they not supplemented with the latest market information. Hence, an efficient systemic risk measure is needed for EM. For that purpose, how does the death come true? is crucial to understand and measure the spillover risk across EMs financial system network, which is important for financial risk measurement and portfolio diversification; From the perspective of financial risk measurement, the interdependence among FIs becomes more important, especially during periods of distress, when losses spread through institutions, rendering the global financial system more vulnerable.

Robust portfolio optimization and management this regard, a systemic crisis that disturbs the financial system stability can have serious effects and lead to high losses for the entire economy and society. From the perspectives of risk management and portfolio diversification, the contagion risk across the FIs from the same market, and across the worldwide markets, leads to a decrease in diversification potential.

Hence, understanding the network structure of interdependence among FIs is crucial to risk managers and portfolio investors, as this can help them design investment strategies to reduce dependence risk and thereby increase diversification. Investors are also interested in recognizing the FIs that contribute the most robust portfolio optimization and management risk to their portfolio so that they are considered with caution in their portfolio design, especially during financial market turmoil. It is worth to note that our dataset covers several global crisis periods, allowing us to examine how the EMs Financial systems respond to the different crisis.

robust portfolio optimization and management

After determining the interdependencies among FIs and ahd factors, our research link also to build a robust strategy based on portfolio diversification in EMs. Are some categories of Macro factors more important than others? What FIs from EMs are the largest smallest spillover transmitters receivers? What FIs contribute the most least risk to total portfolio risk? What FIs offer greater diversification benefits?

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And lastly, how the tail spillover effect and portfolio weights change over time, and how they react to the different tail risk levels? Answering all these questions is crucial, as international investors interested in understanding the forces behind the interdependence among macroeconomic factors and FIs, to identify potential risks and rewards and benefit from global diversification. Economic policymakers and regulators in BRIMST are interested robust portfolio optimization and management forces behind the co-movement between these markets to further establish market resilience in EMs.

The FRM is based on Lasso quantile regression designed to examine tail event co-movements financial securites.]

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