http://hdl.handle.net/11089/54433 Fri, 03 Apr 2026 17:26:15 GMT 2026-04-03T17:26:15Z https://dspace.uni.lodz.pl:443/xmlui/bitstream/id/2c8e82fe-7719-4f37-93e2-ce0f31abc463/ http://hdl.handle.net/11089/54433 http://hdl.handle.net/11089/54442 Tail Risks Across Investment Funds Lin, Jerchern The purpose of the article. Managed portfolios are subject to tail risks, which can be either index level (systematic) or fund-specific. Examples of fund-specific extreme events include those due to big bets or fraud. This paper studies the two components in relation to compensation structure in managed portfolios.Methodology. A novel methodology is developed to decompose return skewness and kurtosis into various systematic and idiosyncratic components and applied it to the returns of different fund types to assess the significance of these sources. In addition, a simple model generates fund-specific tail risk and its asymmetric dependence on the market, and makes predictions for where such risks should be concentrated. The model predicts that systematic tail risks increase with an increased weight on systematic returns in compensation and idiosyncratic tail risks increase with the degree of convexity in contracts.Results of the research. The model predictions are supported with empirical results. Hedge funds are subject to higher idiosyncratic tail risks and Exchange Traded Funds exhibit higher systematic tail risks. In skewness and kurtosis decompositions, the results indicate that coskewness is an important source for fund skewness, but fund kurtosis is driven by cokurtosis, as well as volatility comovement and residual kurtosis, with the importance of these components varying across fund types. Investors are subject to different sources of skewness and fat tail risks through delegated investments. Volatility based tail risk hedging is not effective for all fund styles and types. Tue, 31 Dec 2024 00:00:00 GMT http://hdl.handle.net/11089/54442 2024-12-31T00:00:00Z http://hdl.handle.net/11089/54440 Portfolio Management in Times of Elevated Risk. Safe-Haven and Hedge Assets in CAPM Setting Feder-Sempach, Ewa The purpose of the article. The purpose of the article is to present the safe-haven concept according to the latest academic literature and distinguish it from the hedge and diversifier terms that are sometimes used interchangeably by researchers and portfolio managers. The ultimate goal of the paper is to place the safe-haven and hedge assets in the portfolio theory setting by introducing the negative beta parameter as stated in the Capital Asset Pricing Model. According to the literature, this article proposes a few approaches to identify and characterize safe-haven assets and to discover the perspective and outline further research in the portfolio theory.Methodology. The work uses the method of descriptive and comparative analysis of literature, i.e., Systematic Literature Review (SLR). This method is used to present scientific overview of portfolio management when uncertainty rises to identify safe-haven and hedge assets.Results of the research. This paper aims to characterize and identify three main types of assets helping investors to reduce the portfolio risk: safe haven, hedge, and diversifier. It introduces an improved analytical framework of beta parameter and drawdown beta concept to contribute to the rapidly expanding research on portfolio theory. Lastly it depicts a trade-off effect, which is stronger in-crisis performance of safe-haven assets. The returns of safe-haven assets are more positive when the stock market returns are more negative that may safeguard the financial system. Tue, 31 Dec 2024 00:00:00 GMT http://hdl.handle.net/11089/54440 2024-12-31T00:00:00Z http://hdl.handle.net/11089/54441 Empirical Study of Multi-Objective Risk Portfolio Optimization Based on NSGA-II Gao, Qian; Kresta, Aleš The purpose of the article. The application of multi-objective optimization in portfolio management has gained significant attention in asset management. This study aims to uncover the potential advantages of dynamic portfolio optimization using a multi-objective genetic algorithm to address the challenges of ever-changing market conditions.Methodology. By incorporating multi-objective optimization, this paper comprehensively examines three key portfolio objectives: minimizing two risk types and maximizing returns. The approach involves constructing portfolios, initializing the population using the Non-Dominated Sorting Genetic Algorithm II (NSGA-II), and employing crossover and mutation steps to achieve Pareto optimality. Additionally, this study compares the performance of two risk minimization strategies through traditional portfolio backtesting.Results of the research. The results indicate that the multi-objective risk genetic algorithm not only effectively explores the portfolio space but also handles conflicting optimization objectives, thereby enhancing the comprehensiveness and flexibility of investment decisions. However, its performance depended on the chosen risk measurement methods, and the backtesting returns were unstable. Tue, 31 Dec 2024 00:00:00 GMT http://hdl.handle.net/11089/54441 2024-12-31T00:00:00Z http://hdl.handle.net/11089/54439 Transparency of the Federal Reserve, a Force of Stability or Volatility in Financial Markets Post 2008 and Prior to COVID-19? Soper, Carolyne C.; Sywak, Monika K. The purpose of this article is to analyze how the Central Bank of the United States, the Federal Reserve’s decision to provide greater transparency after the Financial Crisis of 2008 impacted the volatility in financial markets. This study uses five Chicago Board Options Exchange Volatility Indices as a proxy for overall market volatility and attempts to capture their deviances from expected returns. The event dates identified are when the United States Federal Reserve met and released their “summary of economic predictions”.The methodology deployed uses an event study framework on daily financial market data from the Federal Open Market Committee (FOMC) meeting days, to determine how an increased availability of information impacted financial markets in the period of January 2008 – January 2020.The results of the empirical analysis do not reveal abnormal returns pre or post the event dates. This finding suggests that the FOMC announcements did not lead to significant abnormal returns of the analyzed assets. Tue, 31 Dec 2024 00:00:00 GMT http://hdl.handle.net/11089/54439 2024-12-31T00:00:00Z