Kouaissah Noureddine

• PhD in Analytics for Economics and Business--Finance
• Professor (Associate) at Africa Business School

De novo programming (DNP) is an efficient technique for optimal system design. This paper explores the ability to link the DNP technique with Simon’s satisficing theory to deal with a system design that is satisfactory rather than optimal. To achieve this aim, the ideal vector is replaced by an aspiration-level vector, and the solutions are determi...

The COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 has had significant detrimental effects on human well-being, psychologically, socially, economically, and operationally. As the fight against the pandemic continues, the design of efficient policies for the transitional phase remains fraught with complexity and uncertai...

The consequences of any extreme event can deteriorate any system at all levels: socially, economically, and operationally. The COVID-19 pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), provides a good example of the tremendous impact that can be produced by such extreme events. To effectively measure and mitigate th...

In this paper, we propose a framework for robustifying reward-risk-based portfolio optimization equipped with weak type second-order stochastic dominance constraints that substantially improves upon their conventional versions. In particular, relying on stable sub-Gaussian and Student’s t distributions, we extend a robust optimization technique tha...

The application of fuzzy multi-objective decision-making (F-MODM) to the optimization problems of renewable energy (RE) planning with uncertainties has become increasingly popular, although it is still rare in contrast to the use of traditional multi-attribute decision-making (MADM) methods. This study contributes to the domain of F-MODM modeling a...

The conventional data envelopment analysis (DEA) method suffers from its inability to incorporate the decision-makers’ preferences and cope with the uncertainty that exists in real-life decision problems. Exclusive-or (XOR for short) is an uncertain logic that describes a situation in which there is only one choice between two or more competitive a...

This paper investigates the implications for portfolio theory of using multivariate semiparametric estimators and a copula-based approach, especially when the number of risky assets becomes substantial. Parametric, nonparametric, and semiparametric regression methods are compared to approximate their returns in large-scale portfolio selection probl...

This paper investigates volatility spillovers between energy and stock markets during periods of crises. Our main findings reveal that transmissions of volatilities among these markets during the Covid-19 pandemic crisis exceeded the ones recorded throughout the 2008 global financial crisis. All stock markets are net transmitters of volatility to e...

In this paper, we develop robust portfolio optimization models for conditional expectation type reward-risk performance measures that significantly improve upon conventional portfolio selection techniques. In particular, we directly address estimation error in the portfolio optimization process by adopting a robust optimization method that is typic...

In this paper, we develop a portfolio optimization methodology that significantly improves upon conventional portfolio selection problems. In particular, we propose a two-step optimization problem where we first select the efficient assets based on alternative parametric assumptions and then maximize portfolio wealth using well-known performance me...

This paper proposes and implements methods for determining whether incorporating technical trading rules accurately forecasts systemic risk and improves the performance of out‐of‐sample portfolios. The proposed methodology considers various trading rules for forecasting and addressing potential systemic risk in portfolio selection problems. The met...

This paper proposes a novel weighted-additive fuzzy multi-choice goal programming (WA-FMCGP) model for the imprecise decision context wherein several conflicting goals are present but each goal has multiple-choice aspiration levels (MCALs) and, around them, the fuzzinesses are expressed in terms of membership functions (MFs). The main contribution...

Optimizing sustainable renewable energy portfolios is one of the most complicated decision making problems in energy policy planning. This process involves meeting the decision maker’s preferences, which can be uncertain, while considering several conflicting criteria, such as environmental, societal, and economic impact. In this paper, rather than...

This paper proposes parameterized multivariate stochastic dominance (PMSD) rules under different distributional assumptions for a class of non-satiable risk-seeking investors. In particular, it determines the PMSD rules for both stable symmetric and Student's t distributions. Methodologically, the PMSD rules for ordering are based on comparison of...

In this study, we investigate whether sector-weighted portfolios based on alternative parametric assumptions are consistent with multivariate stochastic dominance (MSD) conditions for a class of non-satiable risk-averse investors. Focusing specifically on stable symmetric and Student’s t distributions, we propose and motivate an MSD rule to determi...

Uncertainty is a ubiquitous and inherent feature of the decision-making process. This paper proposes a new method called the XOR-analytical hierarchy process (XOR-AHP) to solve multi-criteria decision-making problems in uncertain and imprecise environments. In particular, the method derives a priority vector from an XOR comparison matrix, an XOR we...

This paper provides some theoretical foundations for using moving average (MA) rules in the stock market. In particular, the paper analyzes the conditional probability of price increments and examines how this probability varies over time. We prove under certain assumptions that the probability of being in an uptrend is greater than the probability...

In this paper, we examine the use of conditional expectation, either to reduce the dimensionality of large-scale portfolio problems or to propose alternative reward–risk performance measures. In particular, we focus on two financial problems. In the first part, we discuss and examine correlation measures (based on a conditional expectation) used to...

Selecting a renewable energy source portfolio is an uncertain multi-criteria decision-making (MCDM) problem. In particular, it involves searching for the best portfolio of renewable energy that meets the decision maker’s preferences by considering and leveraging conflicting criteria such as technical, environmental, societal, and economic. To tackl...

In this paper, we investigate the implications for portfolio theory of using conditional expectation estimators. First, we focus on the approximation of the conditional expectation within large-scale portfolio selection problems. In this context, we propose a new consistent multivariate kernel estimator to approximate the conditional expectation an...

In this paper, we present alternative methods to evaluate the presence of the arbitrage opportunities in the market. In particular, we investigate empirically the well-known put-call parity no-arbitrage relation and the state price density. First, we measure the violation of the put call parity as the difference in implied volatilities between call...

In this paper, we present different approaches to evaluate the presence of arbitrage opportunities in the market. In particular, we investigate empirically the well-known put-call parity no-arbitrage relation and the state price density. First, we measure the violation of the put-call parity as the difference in implied volatilities between call an...

In this paper, we propose different approaches to estimate the state price density under the classical hypothesis of the Black and Scholes model. In particular, we use the nonparametric approaches based on kernel and on the conditional expectation estimators. Firstly, we examine the real mean return function using local polynomial smoothing techniq...