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In asset management, what if clients want to purchase protection from risk factors, under the form of variance risk premia. This paper aims to address this topic by developing a portfolio optimization framework based on the criterion of the minimum variance risk premium (VRP) for any investor selecting stocks with an expected target return while minimizing the risk aversion associated to the portfolio according to “good” and “bad” times.

To accomplish this portfolio selection problem, the authors compute variance risk-premium as the difference from high-frequencies' realized volatility and options' implied volatility stemming from 19 stock markets, estimate a 2-state Markov-switching model on the variance risk-premia and optimize variance risk-premia portfolios across non-overlapping regions. The period goes from March 16, 2011, to March 28, 2018.

The authors find that optimized portfolios based on variance-covariance matrices stemming from VRP do not consistently outperform the benchmark based on daily returns. Several robustness checks are investigated by minimizing historical, realized or implicit variances, with/without regime switching. In a boundary case, accounting for the realized variance risk factor in portfolio decisions can be seen as a promising alternative from a portfolio performance perspective.

As a new management “style”, the realized volatility approach can, therefore, bring incremental value to construct the conditional covariance matrix estimates.

The authors assess the portfolio performance determined by the variance-covariance matrices that are derived by four models: “naive” (Markowitz returns benchmark), non-switching VRP, maximum likelihood regime-switching VRP and Bayesian regime switching VRP. The authors examine the best return-risk combination through the calculation of the Sharpe ratio. They also assess another different portfolio strategy: the risk parity approach.

This paper provides a critique of minimum variance hedging using futures. The paper develops the conventional minimum variance hedge ratio (MVHR) and discusses its estimation. A review of the wide variety of alternative methods used to construct MVHRs is then performed. These methods highlight many of the potential limitations in the conventional framework. The paper argues that the literature should focus more on the assumptions underlying the conventional MVHR, rather than improving the techniques used to estimate the conventional MVHR.

Midwest Independent Transmission System Operator, Inc. (MISO) is a nonprofit regional transmission organization (RTO) that oversees electricity production and transmission across 13 states and 1 Canadian province. MISO also operates an electronic exchange for buying and selling electricity for each of its five regional hubs.

MISO oversees two types of markets. The forward market, which is referred to as the day-ahead (DA) market, allows market participants to place demand bids and supply offers on electricity to be delivered at a specified hour the following day. The equilibrium price, known as the locational marginal price (LMP), is determined by MISO after receiving sale offers and purchase bids from market participants. MISO also coordinates a spot market, which is known as the real-time (RT) market. Traders in the RT market must submit bids and offers by 30minutes prior to the hour for which the trade will be executed. After receiving purchase and sale offers for a given hour in the RT market, MISO then determines the LMP for that particular hour.

The existence of the DA and RT markets allows producers and retailers to hedge against the large fluctuations that are common in electricity prices. Hedge ratios on the MISO exchange are estimated using various techniques. No hedge ratio technique examined consistently outperforms the unhedged portfolio in terms of variance reduction. Consequently, none of the hedge ratio methods in this study meet the general interpretation of FASB guidelines for a highly effective hedge.

This paper compares the hedging performance of the minimum‐extended Gini hedge ratio (MEGHR) and the minimum‐variance hedge ratio (MVHR) using three emerging market currencies. The MEGHR is consistent with the expected utility hypothesis under very general conditions, unlike the MVHR which requires special distributional assumptions. Our sample violates these conditions, and thus provides a context for contrasting the performance of the MEGHR and MVHR. Our results show that the MVHR and MEGHR are indeed different and in some cases the differences are substantial, both statistically and in order of magnitude. This indicates that the MEGHR should provide superior hedging performance given its theoretical robustness. Our hedging performance results support this conclusion for all currencies.

Beginning from the onset of the Covid-19 pandemic, crypto assets have intensely been in the spotlight and have attracted significant investor attention. By being the first blockchain product, Bitcoin is the first crypto asset and still dominates the entire crypto market capitalization. In this study, we shed light on whether this energy-hungry crypto asset is an effective tool for portfolio volatility reduction from the perspective of the Modern Portfolio Theory. Based on a two-year period from April 2019 to April 2021, which includes the extreme impacts (crash and rally) of the pandemic on markets, we conclude that Bitcoin is not a beneficial instrument for volatility reduction if short-selling is not allowed. After removing this restriction, Bitcoin has very small negative investment weights in minimum variance portfolios. In other words, short-selling Bitcoin slightly reduces portfolio volatility.

In this paper we develop a new semi-parametric model for conditional correlations, which combines parametric univariate Generalized Auto Regressive Conditional Heteroskedasticity specifications for the individual conditional volatilities with nonparametric kernel regression for the conditional correlations. This approach not only avoids the proliferation of parameters as the number of assets becomes large, which typically happens in conventional multivariate conditional volatility models, but also the rigid structure imposed by more parsimonious models, such as the dynamic conditional correlation model. An empirical application to the 30 Dow Jones stocks demonstrates that the model is able to capture interesting asymmetries in correlations and that it is competitive with standard parametric models in terms of constructing minimum variance portfolios and minimum tracking error portfolios.

The recent development of the European debt sovereign crisis showed that sovereign debt is not “risk free.” The traditional index bond management used during the last two decades such as the market-capitalization weighting scheme has been severely called into question. In order to overcome these drawbacks, alternative weighting schemes have recently prompted attention, both from academic researchers and from market practitioners. One of the key developments was the introduction of passive funds using economic fundamental indicators.

In this chapter, the authors introduced models with economic drivers with an aim of investigating whether the fundamental approaches outperformed the other models on risk-adjusted returns and on other terms.

The authors did this by constructing five portfolios composed of the Eurozone sovereigns bonds. The models are the Market-Capitalization RP, GDP model RP, Ratings RP model, Fundamental-Ranking RP, and Fundamental-Weighted RP models. These models were created exclusively for this chapter. Both Fundamental models are using a range of 10 country fundamentals. A variation from other studies is that this dissertation applied the risk parity concept which is an allocation technique that aims to equalize risk across different assets. This concept has been applied by assuming the credit default swap as proxy for sovereign credit risk. The models were run using the Generalized Reduced Gradient (GRG) method as the optimization model, together with the Lagrange Multipliers as techniques and the Karush–Kuhn–Tucker conditions. This led to the comparison of all the models mentioned above in terms of performance, risk-adjusted returns, concentration, and weighted average ratings.

By analyzing the whole period between 2006 and 2014, it was found that both the fundamental models gave very appealing results in terms of risk-adjusted returns. The best results were returned by the Fundamental-Ranking RP model followed by the Fundamental-Weighting RP model. However, better results for the mixed performance and risk-adjusted returns were achieved on a yearly basis and when sub-dividing the whole period in three equal periods. Moreover, the authors concluded that over the long term, the fundamental bond indexing triumphed over the other approaches by offering superior return and risk characteristics. Thus, one can use the fundamental indexation as an alternative to other traditional models.

Portfolio selection has been extensively studied in field of business and economics. Many methods have been developed to construct a well-diversified portfolio that is expected to result in higher investment return with minimum risk. One of the most foundational works contributing to modern portfolio selection is the Markowitz mean variance optimization approach. The Markowitz approach heavily relies on past stock price performance, both in term of correlation structure and the return, to predict the future outcome. We constructed both Markowitz portfolio and the Fundamental Indexing portfolio independently, then using Buffet ratio to weight, combined both portfolio into a newly blended portfolio, test out-of-sample the new portfolio in term of return and then compare it to the Indonesian LQ45 benchmark index. The result shows that the new combined portfolio returns annually on average 43.89% higher than the benchmark index.

The purpose of this paper is to present and solve the problem of adaptive beamforming (ABF) for a uniform linear array (ULA) as an optimization problem. ABF mainly concerns with estimation of weights of antenna array so as to direct the major lobe in the direction of desired user and nulls in the direction of interfering signals with reduced side lobe level (SLL).

The potential of gravitational search algorithm is explored to optimize multi-objective fitness function for ABF using MATLAB software.

The performance of the algorithm has been compared by considering different number of interference signals at different power levels. The proposed algorithm presents good convergence rate and accurate steering of main lobe and nulls with reduced SLL compared to the well-known ABF technique, namely, minimum variance distortionless response (MVDR) and previously reported results. The simulation results are presented in tabular form.

The present work is limited to simulation. The researchers are encouraged to solve the problem of ABF using the proposed approach in hardware.

The application of proposed algorithm is to optimize multi-objective function for ABF with reduced SLL in linear antenna arrays.

This paper aims to review the development of the Channel Islands exchange and assess the potential diversification benefits arising from the inclusion of this market in investment portfolios containing UK and French equity assets.

First this paper uses a simple stochastic drift, GARCH, and time‐varying parameter CAPM to model total returns indices. Second, it uses the unconditional and conditional means and variances from first stage as inputs into a mean‐variance portfolio quadratic optimisation problem: the solutions of which denote the optimal asset weights.

The evidence suggests that although there are serious difficulties in modelling time series from small illiquid equity markets owing to price‐rigidity, the limited benefits that do exist for the inclusion of Channel Islands assets in portfolios do so preferentially with Paris as opposed to London assets.

This paper extends the literature development policy options for small offshore markets and provides the first analysis of the Channel Islands.