Academic literature on the topic 'Portfolio allocation, co-variance, co-skewness and co-kurtosis'

Due to many theoretical and practical shortcomings of the traditional CAPM model, this study aims at analyzing the CAPM with possible extensions. The analysis aims to know the empirical soundness of Conditional Higher Moment CAPM in emerging India’s capital market. The sample consists of 69 company’s daily stock price data from April 2004 to March 2019 from NSE 100. Panel data analysis is used on 21 cross-sections. The overall results show that when both up and down markets are incorporated separately, all three moments, namely, co-variance, co-skewness, and co-kurtosis, are priced during the normal Indian economy phase. Further, this study states that including higher moments (co-skewness and co-kurtosis) in the two-moment model provides symmetry in both the up and down markets. This is one of the first studies in the Indian Stock market explaining the variation in portfolio returns through panel data analysis by extending CAPM with conditional higher-order co-moments. The portfolio managers should consider skewness and kurtosis along with variance in constructing the optimal portfolios.

Sustainable economic growth and development of stock market plays an important role in diversifying the investment opportunities that can be assessed accordingly. However, a true diversification in portfolio is impossible without inclusion of higher-order moments, skewness and kurtosis. However, the risk-taking behavior of investors is modelled with the help of higher-order moments of risk. Therefore, this study is intended to construct optimal portfolios and efficient frontiers with the inclusion of higher-order moments of risk. The findings show that optimized portfolios with inclusion of skewness and kurtosis are sustainable and significantly different than those from mean-variance optimized portfolios which show asymmetric and fat-tail risk. Results further confirm its significance in balancing the additional risk dimensions and returns in Asian emerging stock markets for sustainable returns. The results also endorse that induction of skewness and kurtosis affects portfolio allocation weights and expected returns. Therefore, this study strongly recommends the inclusion of higher moments of risk for optimization to curtail their effect and sub-optimal decisions.

In this paper, we quantify the impact on the representative agent's welfare of the presence of derivative products spanning covariance risk. In an asset allocation framework with stochastic (co)variances, we allow the agent to invest not only in the stocks but also in the associated variance swaps. We solve this optimal portfolio allocation program using the Wishart Affine Stochastic Correlation framework, as introduced in Da Fonseca, Grasselli and Tebaldi (2007): it shares the analytical tractability of the single-asset counterpart represented by the [36] model and it seems to be the natural framework for studying multivariate problems when volatilities as well as correlations are stochastic. What is more, this framework shows how variance swaps can implicitly span the covariance risk. We provide the explicit solution to the portfolio optimization problem and we discuss the structure of the portfolio loadings with respect to model parameters. Using real data on major indexes, we find that the impact of covariance risk on the optimal strategy is huge. It first leads to a portfolio that is mostly driven by the market price of volatility-covolatility risks. It is then strongly leveraged through variance swaps, thus leading to a much higher utility, when compared to the case when investing in such derivatives is not possible.

Purpose This paper aims to explore the impact of Sharīʿah-compliant stocks on other investor risk preferences beyond the risk aversion, namely, prudence and temperance. Design/methodology/approach This paper uses the non-parametric model data envelopment analysis with the shortage function as a measure of performance. The model uses three specifications considering skewness and kurtosis that describe according to expected utility theory, prudence and temperance. Findings Results show that first, efficient portfolios consist mainly of conventional stocks in the three-model specification. Second, the skewness improvement is achieved only when considering conventional stocks while Sharīʿah-compliant assets do not exhibit any impact on the third moment. Finally, diversification through both conventional and Sharīʿah-compliant stocks does not lead to kurtosis reduction. Sharīʿah-compliant stocks in efficient portfolios are sensitive to return and risk solely, and hence, prudence and temperance as related to skewness and kurtosis measures can be ignored in optimal portfolio selection during normal market conditions. Research limitations/implications Findings suggest the same conclusions for four Islamic screening methods; however, readers should be prudent due to the limited sample. Results show that Sharīʿah-compliant assets do not have an impact on higher-order moments optimal portfolio returns, and hence, question the commonly admitted assumption of non-normality return distributions at least for Sharīʿah-compliant stocks. Practical implications The research findings suggest that Islamic investor preferences are described only by return and risk along with Sharīʿah criteria for stock selection and portfolio allocation. Portfolio managers should not care about higher-order moments to manage Sharīʿah-compliant funds. The traditional mean-variance Markowitz framework will be sufficient for investment or allocation decision-making. Description of Sharīʿah-compliant portfolio returns with only the first two order moments gives such asset more resiliency to extreme events like a crisis. Originality/value This research is the first in literature exploring whether prudence and temperance defined by higher-order moments can be drivers, besides Sharīʿah criteria, in portfolio allocation decision-making. This study is unique in terms of methodology and application. It uses individual stock data on the Casablanca Stock Exchange.

It has been widely studied how investors will allocate their assets to an investment when the return of assets is normally distributed. In this context usually, the problem of portfolio optimization is analyzed using mean-variance. When asset returns are not normally distributed, the mean-variance analysis may not be appropriate for selecting the optimum portfolio. This paper will examine the consequences of abnormalities in the process of allocating investment portfolio assets. Here will be shown how to adjust the mean-variance standard as a basic framework for asset allocation in cases where asset returns are not normally distributed. We will also discuss the application of the optimum strategies for this problem. Based on the results of literature studies, it can be concluded that the expected utility approximation involves averages, variances, skewness, and kurtosis, and can be extended to even higher moments.

The traditional models to optimize portfolios based on mean-variance analysis aim to determine the portfolio weights that minimize the variance for a certain return level. The covariance matrices used to optimize are difficult to estimate and ad hoc methods often need to be applied to limit or smooth the mean-variance efficient allocations recommended by the model. Although the method is efficient, the tracking error isn’t certainly stationary, so the portfolio can get distant from the benchmark, requiring frequent re-balancements. This work uses cointegration methodology to devise two quantitative strategies: index tracking and long-short market neutral. We aim to design optimal portfolios acquiring the asset prices’ co-movements. The results show that the devise of index tracking portfolios using cointegration generates goods results, replicating the benchmark’s return and volatility. The long-short strategy generated stable returns under several market circumstances, presenting low volatility.

Abstract A classical portfolio theory deals with finding the optimal proportion in which an agent invests a wealth in a risk-free asset and a probabilistic risky asset. Formulating and solving the problem depend on how the risk is represented and how, combined with the utility function defines a notion of expected utility. In this paper the risk is a fuzzy variable and the notion of expected utility is defined in the setting of Liu’s credibility theory. Thus, the portfolio choice problem is formulated as an optimization problem in which the objective function is a credibilistic expected utility. Different approximation calculation formulas for the optimal allocation of the credibilistic risky asset are proved. These formulas contain two types of parameters: Various credibilistic moments associated with fuzzy variables (expected value, variance, skewness and kurtosis) and the risk aversion, prudence and temperance indicators of the utility function.

AbstractThis article examines whether international investors benefit from adding real estate investment trusts (REITs) to a mixed asset portfolio consisting of global stocks, bonds, hedge funds, and commodities. Previous literature has shown that REITs provide a strong co-movement with direct real estate in the long run. We therefore test the diversification potential of international REITs within the strategic asset allocation. Using the Johansen cointegration technique, we show that there is no long-term co-movement between REITs and the other asset classes in the period from January 1990 to December 2009. Thus, the empirical evidence suggests that REITs improve the diversification potential for active investors and those with a long-term investment horizon by simultaneously generating continuous cash flows.

This paper focuses on three “safe haven” assets (gold, oil, and the Swiss Franc) and examines the impact of recent financial crises and some macroeconomic variables on their return co-movements during the last two decades. All financial crises produced significant increases in conditional correlations between these asset returns, thus revealing consistent portfolio shifts from more traditional towards safer financial instruments during turbulent periods. The world equity risk premium stands out as the most relevant macroeconomic variable affecting return co-movements, while economic policy uncertainty indicators also exerted significant effects. Overall, this evidence points out that gold, oil, and the Swiss currency played an important role in global investors’ portfolio allocation choices, and that these assets preserved their essential “safe haven” properties during the period examined.

Modern Portfolio theory, developed by Markowitz (1952), is based on finding the best trade-off between risk and expected return. This model assumes that returns are normally distributed. In real life, for the majority of the assets this assumption is not true, as generally the distribution of returns has negative skewness and fat tails. This is more evident in case of hedge funds, commodities or emerging markets portfolios. Therefore, in these cases, a portfolio allocation based on the first two moments does not seem to be the right procedure, because we cannot ignore the higher moments. So, we need to find a way to incorporate the higher moments in the portfolio allocation decision. This is the reason why in this dissertation we will extend the Markowitz model to the higher moments and we will analyze the impact that skewness and kurtosis have on portfolio allocation. To introduce the higher moments in the portfolio allocation, we will approximate the expected utility by a fourth order Taylor expansion and we will compare the portfolio allocation based on four moments with the portfolio based on the first two moments. To compare two different optimal portfolios we will use a measure called, Monetary Utility Gain/Loss (MUG) . Furthermore, in the issue of constructing the optimal portfolio allocation, we will consider different approaches for the estimation of the co-moments. We will describe in more details three different approaches: i. Sample approach ii. Constant Correlation approach iii. Shrinkage approach In the empirical part, we will use a fix-mixed rolling window strategy with different calibrations periods, sample periods and levels of risk aversion.

Depuis quelques années, la recherche financière s'inscrit dans une nouvelle dynamique. La nécessité de mieux modéliser le comportement des rendements des actifs financiers et les risques sur les marchés pousse les chercheurs à trouver des mesures de risque plus adéquates. Ce travail de recherche se situe dans cette évolution, ayant admis les caractéristiques des séries financières par des faits stylisés tels que la non normalité des rendements. A travers cette thèse nous essayons de montrer l'importance d'intégrer des mesures de risque qui tiennent compte de la non normalité dans le processus d'évaluation et d'allocation des actifs financiers sur le marché français. Cette thèse propose trois chapitres correspondant chacun à un article de recherche académique. Le premier article propose de revisiter les modèles d'évaluation en prenant en compte des moments d'ordres supérieurs dans un cadre de downside risk. Les résultats indiquent que les downside co-moments d'ordres supérieurs sont déterminants dans l'explication des variations des rendements en coupe transversale. Le second chapitre propose de mettre en relation la rentabilité financière et le risque mesuré par la VaR ou la CVaR. Nous trouvons que la VaR présente un pouvoir explicatif plus élevé que celui de la CVaR et que l'approche normale est plus intéressante que l'approche basée sur l'expansion de Cornish-Fisher (1937). Ces deux résultats contredisent les prédictions théoriques mais nous avons pu démontrer qu'ils sont inhérents au marché français. Le troisième chapitre propose une autre piste, nous revisitons le modèle moyenne-CVaR dans un cadre dynamique et en présence des coûts de transaction
This dissertation is part of an ongoing researches looking for an adequate model that apprehend the behavior of financial asset returns. Through this research, we propose to analyze the relevance of risk measures that take into account the non-normality in the asset pricing and portfolio allocation models on the French market. This dissertation is comprised of three articles. The first one proposes to revisit the asset pricing model taking into account the higher-order moments in a downside framework. The results indicate that the downside higher order co-moments are relevant in explaining the cross sectional variations of returns. The second paper examines the relation between expected returns and the VaR or CVaR. A cross sectional analysis provides evidence that VaR is superior measure of risk when compared to the CVaR. We find also that the normal estimation approach gives better results than the approach based on the expansion of Cornish-Fisher (1937). Both results contradict the theoretical predictions but we proved that they are inherent to the French market. In the third paper, we review the mean-CVaR model in a dynamic framework and we take into account the transaction costs. The results indicate that the asset allocation model that takes into account the non-normality can improve the performance of the portfolio comparing to the mean-variance model, in terms of the average return and the return-to CVaR ratio. Through these three studies, we think that it is possible to modify the risk management framework to apprehend in a better way the risk of loss associated to the non-normality problem

An association rule (AR) is a common type of mined knowledge in data mining that describes an implicative co-occurring relationship between two sets of binary-valued transaction-database attributes, expressed in the form of an ? rule. A variation of ARs is the (WARs), which addresses the weighting issue in ARs. In this chapter, the authors introduce the concept of “one-sum” WAR and name such WARs as allocating patterns (ALPs). An algorithm is proposed to extract hidden and interesting ALPs from data. The authors further indicate that ALPs can be applied in portfolio management. Firstly by modelling a collection of investment portfolios as a one-sum weighted transaction-database that contains hidden ALPs. Secondly the authors show that ALPs, mined from the given portfolio-data, can be applied to guide future investment activities. The experimental results show good performance that demonstrates the effectiveness of using ALPs in the proposed application.

An association rule (AR) is a common type of mined knowledge in data mining that describes an implicative co-occurring relationship between two sets of binary-valued transaction-database attributes, expressed in the form of an ? rule. A variation of ARs is the (WARs), which addresses the weighting issue in ARs. In this chapter, the authors introduce the concept of “one-sum” WAR and name such WARs as allocating patterns (ALPs). An algorithm is proposed to extract hidden and interesting ALPs from data. The authors further indicate that ALPs can be applied in portfolio management. Firstly by modelling a collection of investment portfolios as a one-sum weighted transaction- database that contains hidden ALPs. Secondly the authors show that ALPs, mined from the given portfolio-data, can be applied to guide future investment activities. The experimental results show good performance that demonstrates the effectiveness of using ALPs in the proposed application.

Abstract This paper highlights the efforts to mitigate the unprecedented situation of truncated gas demand via network modeling development and harmoniously harvesting value generation by its implementation. Global Pandemic in year 2020 resulted into unique situation of steep truncated gas demand due to economic slowdown worldwide. Hence as a prudent operator its deemed necessary to pursue strategic ideas and innovative concepts to manage offshore complex gas network to handle reduced supply demand balance, whilst protecting fulfill technical and contractual obligations also by optimizing value generation. It therefore demands the development and implementation of robust integrated system (end to end value chain system for hydrocarbon molecule) that would be leveraged on for agile response for deploying appropriate resolution considering dynamics of supply/demand balance and system equilibrium. This study focuses on a state of art that was commenced to develop an End to End Holistic Network Model from well head (fields) to product delivery terminal to scrutinize the complex offshore facilities to decipher appropriate pain points in terms of capabilities, risks, uncertainty, opportunity and exposures by performing robust analysis for trouble shooting, root cause analysis, gap analysis and expansion strategies for required scenario(s). A novel approach was influenced to create simulation model for complex network with building components i.e. source (100+ fields), sinks (multiple terminals), connectors (120+ export pipelines and ∼8 gas highways) along with pressure boosters (pump/compressor) etc. embedded in model. Major hubs, sub-hub, spill-over pipelines/loop lines including main gas transporting facilities with dedicated receiving terminal which formed integral part of network were also modeled in single platform. Flow co-relations for hydraulic estimations and material balance calculations along with engineering thermodynamics formulae for seamless data transfer in collaboration with operations were inbuilt for representative and resilient results. Simulation model was further validated with actual plant data as history matching and that precise forecasting analysis output. Multiple scenarios utilizing system ullage/ pipeline hydraulics (adhering to first principles) were studied and suitable alteration in operating philosophy e.g. were proposed to cater the truncated demand and to shape development strategies for future portfolios. Multi-level diagnostic was conducted to assure that system parameters such as operating pressure, velocity limits and required quality specifications are within operating envelope for the entire landscape. Lookahead analysis (what-if scenarios) were performed to evaluate to root cause analysis and troubleshoot at various intensities of the network to cater for equilibrium balance. Multiple contemplating scenarios were accomplished to analyze complex network parameters such as ullage opportunity, pressure variations, hydraulic fluxes, potential choking of low-pressure wells/fields & prospective blending specifications with variations in the supply/demand outlook. Gap analysis was executed in addition to arrive at necessary alterations for operating philosophy, partial segregation of system for pressure balancing due to low flow volume and product quality adherence. Model output assisted to gauge the potential for operating network by implementing appropriate reforms to optimize truncated flow in system and ensuring system is above its minimum turndown rate flow regime and could also propose to have the necessary mitigations to be in place for vigorous liquid management system due to low flow in network. Above methodology describes how by developing an end to end network model that summarize the granularity of a complex offshore network has facilitated to steer the agile response for operating envelope to cater the fluctuations in the demand/supply balance and optimize offshore allocations by network balancing for value maximization and to form a vision of portfolio strategy for future developments.