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Optimization algorithms for multiple-asset portfolios with machine learning techniques
Published in Noura Metawa, M. Kabir Hassan, Saad Metawa, Artificial Intelligence and Big Data for Financial Risk Management, 2023
This chapter shows that the performance of efficient and coherent economic capital portfolios depends on the expected returns, individual L-VaR positions, liquidity horizons of each trading asset, and the set of portfolio weights. The empirical findings indicate that the risk tolerance in the L-VaR framework is time-varying and closely related to the selection of the unwinding liquidity horizons and expected returns, in addition, to the impact of the assumed correlation factors of the portfolio. Moreover, in this work, the relative performance of L-VaR and the economic capital selection model is compared in a dynamic asset allocation framework. The goal of the dynamic asset allocation is to find the optimum equity asset allocation mix by minimizing the objective function of L-VaR and economic capital subject to the imposition of certain realistic operational and financial constraints based on fundamental asset management considerations.
Sustainability of Minerals Processing and Metal Production for European Economies in Transition
Published in Sheila Devasahayam, Kim Dowling, Manoj K. Mahapatra, Sustainability in the Mineral and Energy Sectors, 2016
Vladimir Strezov, Natasa Markovska, Meri Karanfilovska
Minerals have no intrinsic value in the form of ore and when buried in the earth (Petrie, 2007). Their value is added through mineral processing and production of metals. Management of the rate of conversion of these minerals into economic capital and the way this economic capital is then invested is the primary driver for sustainability of this industry. Giurco and Cooper (2012) point out on the importance for defining the level at which it is acceptable for the natural capital to be transformed into an economic capital. This is primarily because the natural capital is exhaustible through mining; hence depletion of mineral resources is one of the most significant factors for industrial sustainability of the mineral processing industries. According to Hilson and Murck (2000), examples of sustainable mineral processing include extending the longevity of the mineral reserves through conservation and increased recycling of the minerals and metals.
A Case Study of Enterprise Machine Learning Frame Work for Investment Platforms
Published in Durgesh Kumar Mishra, Nilanjan Dey, Bharat Singh Deora, Amit Joshi, ICT for Competitive Strategies, 2020
Rao Casturi, Rajshekhar Sunderraman
UL: Unexpected loss is dened as any loss on a nancial product that was not expected by a nancial organization and therefore not factored into the price of the product. The purpose of the Basel regulations is to force banks to retain capital to cover the entire amount of the Value-at-Risk (VaR), which is a combination of this unexpected loss plus the expected loss. For the current paper we will be looking at PD in terms of Default and Non-Default. The Variable Default Categorization is a binary variable which can take Default or Non-Default. We will try to predict the value by creating the ML model (Two-Class Boosted Decision Tree). This analysis only a small part in overall credit risk default prediction and economic capital calculations.
Modelling default dependence in automotive supply networks using vine-copula
Published in International Journal of Production Research, 2019
We contextualise our study using empirical data from the automotive industry. Takeishi (2001) points out the modern supply chain networks in the automotive industry are characterised by horizontal ties between supplier firms, which are sequentially arranged based on vertical ties to automobile manufacturers. Thus, the profitability of automobile manufacturers is highly dependent on the suppliers’ capabilities and how effectively they manage associated risks. The recognition and accounting of these simultaneous interdependencies is crucial for a more advanced understanding of default propagation in supply networks. Further, we employ the concept of economic capital as an important application for our analysis in supply chain risk management. Economic capital is a level of liquid capital (e.g. cash or term deposits) that companies maintain, so that they can absorb unexpected losses. Obviously, over-capitalisation implies idle capital and compromises profitability. On the other hand, under-capitalisation may cause financial distress should the firm be unable to honour its contracts. We show that the significance of modelling exposure to supply network default in determination of economic capital. Our analysis, for example, shows that the Gaussian model specification, almost always, underestimates the economic capital.