Applying Dynamic Training-Subset Selection Methods Using Genetic Programming for Forecasting Implied Volatility

International audienceVolatility is a key variable in option pricing, trading, and hedging strategies. The purpose of this article is to improve the accuracy of forecasting implied volatility using an extension of genetic programming (GP) by means of dynamic training-subset selection methods. These methods manipulate the training data in order to improve the out-of-sample patterns fitting. When applied with the static subset selection method using a single training data sample, GP could generate forecasting models, which are not adapted to some out-of-sample fitness cases. In order to improve the predictive accuracy of generated GP patterns, dynamic subset selection methods are introduced to the GP algorithm allowing a regular change of the training sample during evolution. Four dynamic training-subset selection methods are proposed based on random, sequential, or adaptive subset selection. The latest approach uses an adaptive subset weight measuring the sample difficulty according to the fitness cases' errors. Using real data from S&P500 index options, these techniques are compared with the static subset selection method. Based on mean squared error total and percentage of non-fitted observations, results show that the dynamic approach improves the forecasting performance of the generated GP models, especially those obtained from the adaptive-random training-subset selection method applied to the whole set of training samples

Dynamic Hedging Using Generated Genetic Programming Implied Volatility Models

The purpose of this paper is to improve the accuracy of dynamic hedging using implied volatilities generated by genetic programming. Using real data from S&P500 index options, the genetic programming's ability to forecast Black and Scholes implied volatility is compared between static and dynamic training-subset selection methods. The performance of the best generated GP implied volatilities is tested in dynamic hedging and compared with Black-Scholes model. Based on MSE total, the dynamic training of GP yields better results than those obtained from static training with fixed samples. According to hedging errors, the GP model is more accurate almost in all hedging strategies than the BS model, particularly for in-the-money call options and at-the-money put options.Comment: 32 pages,13 figures, Intech Open Scienc

On the safe-haven and hedging properties of Bitcoin: new evidence from COVID-19 pandemic

Purpose: This study aims to investigate the safe-haven and hedging properties of Bitcoin against a wide variety of conventional assets before and during the coronavirus disease 2019 (COVID-19) pandemic. Design/methodology/approach: This paper uses a smooth transition regression (STR) to jointly test the hedging properties of Bitcoin in normal conditions and Bitcoin's safe-haven properties in extreme stock market conditions. Findings: Highlighting the results, the authors show that Bitcoin is able to provide safe-haven feature during the COVID-19 pandemic period while Bitcoin serves as a hedge tool in the pre-COVID-19 pandemic period. The findings also show that the prowess of the safe-haven/hedge nature is sensitive to the type of the asset market and the time horizon when switching from daily to weekly frequency data. Originality/value: This is one of the first studies that conduct a combined analysis of the safe-haven and hedging capabilities of Bitcoin against several asset classes using an STR method. This study uses the longest sample period to yet, allowing researchers to examine Bitcoin's safe-haven and hedging features both before and after the COVID-19 pandemic. 2022, Emerald Publishing Limited.Scopu

Sulfated polysaccharides from Loligo vulgaris skin: Potential biological activities and partial purification

The characteristics, biological properties, and purification of sulfated polysaccharides extracted from squid (Loligo vulgaris) skin were investigated. Their chemical and physical characteristics were determined using X-ray diffraction and infrared spectroscopic analysis. Sulfated polysaccharides from squid skin (SPSS) contained 85.06% sugar, 2.54% protein, 1.87% ash, 8.07% sulfate, and 1.72% uronic acid. The antioxidant properties of SPSS were investigated based on DPPH radical-scavenging capacity (IC50=19.42mgmL-1), hydrogen peroxide-scavenging activity (IC50=0.91mgmL-1), and β-carotene bleaching inhibition (IC50=2.79mgmL-1) assays. ACE-inhibitory activity of SPSS was also investigated (IC50=0.14mgmL-1). Further antimicrobial activity assays indicated that SPSS exhibited marked inhibitory activity against the bacterial and fungal strains tested. Those polysaccharides did not display hemolytic activity towards bovine erythrocytes. Fractionation by DEAE-cellulose column chromatography showed three major absorbance peaks. Results of this study suggest that sulfated polysaccharides from squid skin are attractive sources of polysaccharides and promising candidates for future application as dietary ingredients.This work was funded by the “Ministry of Higher Education, Scientific Research and Information and Communication Technologies Tunisia”.Peer Reviewe

Abstracts of the First International Conference on Advances in Electrical and Computer Engineering 2023

This book presents extended abstracts of the selected contributions to the First International Conference on Advances in Electrical and Computer Engineering (ICAECE'2023), held on 15-16 May 2023 by the Faculty of Science and Technology, Department of Electrical Engineering, University of Echahid Cheikh Larbi Tebessi, Tebessa-Algeria. ICAECE'2023 was delivered in-person and virtually and was open for researchers, engineers, academics, and industrial professionals from around the world interested in new trends and advances in current topics of Electrical and Computer Engineering. Conference Title: First International Conference on Advances in Electrical and Computer Engineering 2023Conference Acronym: ICAECE'2023Conference Date: 15-16 May 2023Conference Venue: University of Echahid Cheikh Larbi Tebessi, Tebessa-AlgeriaConference Organizer: Faculty of Science and Technology, Department of Electrical Engineering, University of Echahid Cheikh Larbi Tebessi, Tebessa-Algeri

Abstracts of 1st International Conference on Computational & Applied Physics

This book contains the abstracts of the papers presented at the International Conference on Computational & Applied Physics (ICCAP’2021) Organized by the Surfaces, Interfaces and Thin Films Laboratory (LASICOM), Department of Physics, Faculty of Science, University Saad Dahleb Blida 1, Algeria, held on 26–28 September 2021. The Conference had a variety of Plenary Lectures, Oral sessions, and E-Poster Presentations. Conference Title: 1st International Conference on Computational & Applied PhysicsConference Acronym: ICCAP’2021Conference Date: 26–28 September 2021Conference Location: Online (Virtual Conference)Conference Organizer: Surfaces, Interfaces, and Thin Films Laboratory (LASICOM), Department of Physics, Faculty of Science, University Saad Dahleb Blida 1, Algeria