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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
In this backdrop, academics and practitioners have long acknowledged the significance of assessing the market risk of multiple-asset portfolios of financial and/or commodity securities. In recent years, the growth of trading activities and instances of financial/commodity market upheavals has prompted new research underlining the necessity for market participants to develop reliable dynamic portfolio management and risk assessment methods and algorithms. In measuring the market risk of trading portfolios, one technique advanced in the literature involves the use of value at risk (VaR) models that ascertain how much the value of a trading portfolio would plunge, in monetary terms, over a given period of time with a given probability as a result of changes in market prices (Philippe, 2001; Hull, 2009). Nowadays, VaR is by far the most popular and most accepted risk measure among financial institutions; however, whether or not there is the best way to estimate VaR is still debatable. Although VaR is a very popular measure of the market risk of financial trading portfolios, it is not a panacea for all risk assessments and has several drawbacks, limitations, and undesirable properties (Sanders, 2002; Al Janabi, 2012, 2013, 2014). From a portfolio market risk point of view, VaR faces some major difficulties. Three of the most researched and discussed issues are the non-normal behavior of asset returns, volatility clustering, and the impact of illiquid assets. The effect of the latter on portfolio risk management and dynamic economic capital allocation under market liquidity constraints is the aim of this chapter.
Development appraisal and risk
Published in Richard Reed, Property Development, 2021
An understanding of the complexities of risk is essential for a successful developer. This is not to be overstated. Risk is embedded throughout the property market and is the starting point for every analysis involving property and practically all investment decisions. The two major types of risk affecting a property are broadly referred to as either systematic (i.e. market) risk or unsystematic (i.e. property-specific) risk. Most importantly a developer should never underestimate the effect of risk therefore the level of risk in every development scheme should be carefully identified and, if possible, contained or reduced. It is important to remember that as the development process progresses then the developer’s commitment increases and the possibility of variation decreases, where these both equate to a higher degree of uncertainty and associated risk.
Airline Financial Planning and Appraisal
Published in Peter S. Morrell, Airline Finance, 2018
The cost of equity is computed using the capital asset pricing model (CAPM). This assumes that equity markets are ‘efficient’ in the sense of current stock prices reflecting all relevant available information. Finance theory asserts that shareholders will be compensated for assuming higher risks by receiving higher expected returns. However, the distinction should be made between systematic risk, which is market risk attributable to factors common to all companies (e.g. impact of 9/11 on all airlines), and unsystematic risk, which is unique risk specific to the company or a small group of companies (e.g. US Airways’ bankruptcy announcement or the impact of the European Commission’s decision on airport charges on Ryanair). CAPM models the expected return related to the systematic risk. According to portfolio theory, unsystematic risk can be diversified away through portfolio selection, and thus no reward is received for assuming this risk.
Model and upper–lower bound estimation scheme for portfolio optimisation considering uncertain investment time horizon
Published in Journal of Control and Decision, 2023
Dazhi Wang, Yanhua Chen, Mingqiang Yin, Min Huang, Chunhui Xu
To properly depict the investors' attitude towards the under performance and over performance of a portfolio, a popular down-side risk technique termed as Value-at-Risk (VaR) was developed (Morgan, 1996). Given a confidence level over a fixed horizon, VaR measures the maximum possible loss of a portfolio from the market risk. For example, given a daily VaR valued as x with a confidence level 99%, it measures the risk that the loss will be greater than x at a chance of 1%. Without loss of generality, a higher confidence level means a smaller loss of a portfolio. Due to the simplicity and applicability, VaR has gained popularity in the research field of risk management and financial engineering (Benati & Rizzi, 2007; Gaivoronski & Pflug, 2004; Ghaoui et al., 2003; Glasserman et al., 2000; Huang et al., 2012; Kaplanski & Kroll, 2002; Lwin et al., 2017; Natarajan et al., 2009; Wozabal, 2012). However, VaR has also been criticised by financial regulators and academic researchers due to the non-additivity and non-convexity property (Artzner et al., 1999). To consider the potential losses ignored by VaR, many improved modifications have been investigated, including the most commonly used measure termed as Conditional VaR (CVaR) (Rockafellar & Uryasev, 2000). Specifically, CVaR aims to investigate the expected losses that occur beyond the VaR threshold and has been extensively studied in the research fields (Kibzun & Kuznetsov, 2006; Lim et al., 2011; Yao et al., 2013).
Optimal portfolio choice for ship leasing investments
Published in Maritime Policy & Management, 2019
Carisa K.W. Yu, Tsz Leung Yip, Siu Kai Choy
Decisions must also be made regarding which types of ship to finance on the basis of ship leasing. A portfolio of ships is chosen to form a ship fleet to be operated in different trades and on different routes. It is recognized that both the dry-bulk and tanker sectors of the shipping industry have distinct sub-markets, served by ships of specific sizes. The shipping industry comprises a number of broad sectors that are defined according to the characteristics of the ships involved in carrying the dry or liquid cargoes and of the trade routes in these sectors. Physical limitations on ship size demarcate between different market segments, because ship size determines the type of trade in which the ship will be involved. Capesize ships are used frequently to transport iron ore and coal, Panamax ships are used commonly to carry grain, and Handymax ships are used to transport minor bulk cargoes (e.g. sugar and steel). Large tankers are engaged in transporting dirty products (e.g. crude oil), and small tankers in transporting clean products (e.g. petroleum products). Assuming that a ship leasing firm is exposed to market risk stemming from freight rate fluctuations, the ship leasing firm should estimate Value-at-Risk (VaR) for a certain confidence level so that freight rate risk can be monitored. VaR is a widely used measure of risk that can be applied to quantify market risk (Dowd 1998). A more attractive measure of risk, Conditional Value-at-Risk (CVaR), will be used in this paper, as it has computational advantage over VaR (Artzner et al. 1997; Rockafellar and Uryasev 2002).