Does the Inclusion of Exposure to Volatility into Diversified Portfolio Improve the Investment Results? Portfolio Construction from the Perspective of a Polish Inwestor

Latoszek, Michał; Ślepaczuk, Robert

doi:10.18559/ebr.2020.1.3

Cited by 4 publications

(7 citation statements)

References 11 publications

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“…A mixed strategy occurs when the DM chooses and executes a combination of alternatives. Such an approach may be especially useful and advantageous in portfolio construction [ 13 , 14 ] and cultivation of different plants [ 9 ]. In the next subsections, the emphasis will be put on mixed strategies.…”

Section: Methodsmentioning

confidence: 99%

From Goal Programming for Continuous Multi-Criteria Optimization to the Target Decision Rule for Mixed Uncertain Problems

Gaspars-Wieloch

2021

Entropy

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Goal programming (GP) is applied to the discrete and continuous version of multi-criteria optimization. Recently, some essential analogies between multi-criteria decision making under certainty (M-DMC) and scenario-based one-criterion decision making under uncertainty (1-DMU) have been revealed in the literature. The aforementioned similarities allow the adjustment of GP to an entirely new domain. The aim of the paper is to create a new decision rule for mixed uncertain problems on the basis of the GP methodology. The procedure can be used by pessimists, optimists and moderate decision makers. It is designed for one-shot decisions. One of the significant advantages of the novel approach is related to the possibility to analyze neutral criteria, which are not directly taken into account in existing classical procedures developed for 1-DMU.

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Section: Methodsmentioning

confidence: 99%

From Goal Programming for Continuous Multi-Criteria Optimization to the Target Decision Rule for Mixed Uncertain Problems

Gaspars-Wieloch

2021

Entropy

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“…The optimization problem in (10) can not be tackled in its current form, but after the proper transformations, it becomes solvable. Let us focus on the long-short and the long-only cases one by one.…”

Section: Solution Of the Control Problemmentioning

confidence: 99%

“…Let us first consider the long-short optimization problem, where the w ≥ 0 constraint can be removed from (10). We can find the following slightly different problems that have the same solution:…”

Section: Solving the Long-short Casementioning

confidence: 99%

“…Other authors proposed to tackle the problem on different grounds, for example by incorporating the occasional presence of rare events [11] or phrasing it as a sorting problem [4]. Some even tried incorporating volatility as an additional asset [10]. Of course, properties that are not visible from the time series themselves are also important and can help optimization significantly if made available, for example, retail investor attention [18].…”

Section: Introductionmentioning

confidence: 99%

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Application of Portfolio Optimization to Achieve Persistent Time Series

Zlatniczki,

Telcs

2024

J Optim Theory Appl

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The greater the persistence in a financial time series, the more predictable it becomes, allowing for the development of more effective investment strategies. Desirable attributes for financial portfolios include persistence, smoothness, long memory, and higher auto-correlation. We argue that these properties can be achieved by adjusting the composition weights of the portfolio. Considering the fractal nature of typical financial time series, the fractal dimension emerges as a natural metric to gauge the smoothness of the portfolio trajectory. Specifically, the Hurst exponent is designed for measuring the persistence of time series. In this paper, we introduce an optimization method inspired by the Hurst exponent and signal processing to mitigate the irregularities in the portfolio trajectory. We illustrate the effectiveness of this approach using real data from an S &P100 dataset.

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“…Properly determining and forecasting the volatility of securities is crucial for investment institutions. Such a risk parameter is often used in portfolio risk management, asset pricing, and portfolio construction (Gajdka & Pietraszewski, 2017;Latoszek & Ślepaczuk, 2020;Loang & Ahmad, 2021). Short-term volatility forecasts, such as on a monthly or ideally daily basis, is particularly advantageous for active portfolio managers.…”

Section: Introductionmentioning

confidence: 99%

Forecasting realized volatility through financial turbulence and neural networks

Souto¹,

Moradi²

2023

EBR

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This paper introduces and examines a novel realized volatility forecasting model that makes use of Long Short-Term Memory (LSTM) neural networks and the risk metric Financial Turbulence (FT). The proposed model is compared to five alternative models, of which two incorporate LSTM neural networks and the remaining three include GARCH(1,1), EGARCH(1,1), and HAR models. The results of this paper demonstrate that the proposed model yields statistically significantly more accurate and robust forecasts than all other studied models when applied to stocks with middle-to-high volatility. Yet, considering low-volatility stocks, it can only be confidently affirmed that the proposed model yields statistically significantly more robust forecasts relative to all other models considered.

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Does the Inclusion of Exposure to Volatility into Diversified Portfolio Improve the Investment Results? Portfolio Construction from the Perspective of a Polish Inwestor

Cited by 4 publications

References 11 publications

From Goal Programming for Continuous Multi-Criteria Optimization to the Target Decision Rule for Mixed Uncertain Problems

From Goal Programming for Continuous Multi-Criteria Optimization to the Target Decision Rule for Mixed Uncertain Problems

Application of Portfolio Optimization to Achieve Persistent Time Series

Forecasting realized volatility through financial turbulence and neural networks

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