Search results for: volatility target

Authors: Ruomeng Xiao, Zhulin Zong, Longfa Yang

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Aiming at the problem that the target signal is difficult to detect under the strong ground clutter environment, this paper proposes a clutter suppression algorithm based on the combination of singular value decomposition and the Mallat fast wavelet algorithm. The method first carries out singular value decomposition on the radar echo data matrix, realizes the initial separation of target and clutter through the threshold processing of singular value, and then carries out wavelet decomposition on the echo data to find out the target location, and adopts the discard method to select the appropriate decomposition layer to reconstruct the target signal, which ensures the minimum loss of target information while suppressing the clutter. After the verification of the measured data, the method has a significant effect on the target extraction under low SCR, and the target reconstruction can be realized without the prior position information of the target and the method also has a certain enhancement on the output SCR compared with the traditional single wavelet processing method.

Keywords: clutter suppression, singular value decomposition, wavelet transform, Mallat algorithm, low SCR

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Authors: Fatma Ismailia, Malek Saihi

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This research provides an explanation of exchange incentives on the Tunis stock market from a behavioural point of view. The elucidation of the anomalies of excessive volume of transactions and that of excessive volatility cannot be done without the recourse to the psychological aspects of investors. The excessive confidence has been given the predominant role for the explanation of these phenomena. Indeed, when investors store increments, they become more confident about the precision of their private information and their exchange activities then become more aggressive on the subsequent periods. These overconfident investors carry out the intensive exchanges leading to an increase of securities volatility. The objective of this research is to identify whether the trading volume and the excessive volatility of securities observed on the Tunisian stock market come from the excessive exchange of overconfident investors. We use a sample of daily observations over the period January 1999 - October 2007 and we relied on various econometric tests including the VAR model. Our results provide evidence on the importance to consider the bias of overconfidence in the analysis of Tunis stock exchange specificities. The results reveal that the excess of confidence has a major impact on the trading volume while using daily temporal intervals.

Keywords: overconfidence, trading volume, efficiency, rationality, anomalies, behavioural finance, cognitive biases

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Authors: Manas Shah

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Black Scholes option pricing model is one of the most important concepts in modern world of computational finance. However, its practical use can be challenging as one of the input parameters must be estimated; implied volatility of the underlying security. The more precisely these values are estimated, the more accurate their corresponding estimates of theoretical option prices would be. Here, we present a novel model based on Cuckoo Search Optimization (CS) which finds more precise estimates of implied volatility than Particle Swarm Optimization (PSO) and Genetic Algorithm (GA).

Keywords: black scholes model, cuckoo search optimization, particle swarm optimization, genetic algorithm

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Authors: Abdelli Soulaima, Belhadj Besma

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The paper examines the choice of the central bank toward the movements of the nominal exchange rate and evaluates its effects on the volatility of the output growth and the inflation. The novel hybrid method of the dynamic stochastic general equilibrium called the DSGE-VAR is proposed for analyzing this policy experiment in a small scale open economy in particular Tunisia. The contribution is provided to the empirical literature as we apply the Tunisian data with this model, which is rarely used in this context. Note additionally that the issue of treating the degree of response of the central bank to the exchange rate in Tunisia is special. To ameliorate the estimation, the Bayesian technique is carried out for the sample 1980:q1 to 2011 q4. Our results reveal that the central bank should not react or softly react to the exchange rate. The variance decomposition displayed that the overall inflation volatility is more pronounced with the fixed exchange rate regime for most of the shocks except for the productivity and the interest rate. The output volatility is also higher with this regime with the majority of the shocks exempting the foreign interest rate and the interest rate shocks.

Keywords: DSGE-VAR modeling, exchange rate, monetary policy, Bayesian estimation

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Authors: Timothy Kayode Samson, Adedoyin Isola Lawal

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The past five years have shown a sharp increase in public interest in the crypto market, with its market capitalization growing from $100 billion in June 2017 to $2158.42 billion on April 5, 2022. Despite the outrageous nature of the volatility of cryptocurrencies, the use of skewed error innovation distributions in modelling the volatility behaviour of these digital currencies has not been given much research attention. Hence, this study models the volatility of 5 largest cryptocurrencies by market capitalization (Bitcoin, Ethereum, Tether, Binance coin, and USD Coin) using four variants of GARCH models (GJR-GARCH, sGARCH, EGARCH, and APARCH) estimated using three skewed error innovation distributions (skewed normal, skewed student- t and skewed generalized error innovation distributions). Daily closing prices of these currencies were obtained from Yahoo Finance website. Finding reveals that the Binance coin reported higher mean returns compared to other digital currencies, while the skewness indicates that the Binance coin, Tether, and USD coin increased more than they decreased in values within the period of study. For both Bitcoin and Ethereum, negative skewness was obtained, meaning that within the period of study, the returns of these currencies decreased more than they increased in value. Returns from these cryptocurrencies were found to be stationary but not normality distributed with evidence of the ARCH effect. The skewness parameters in all best forecasting models were all significant (p<.05), justifying of use of skewed error innovation distributions with a fatter tail than normal, Student-t, and generalized error innovation distributions. For Binance coin, EGARCH-sstd outperformed other volatility models, while for Bitcoin, Ethereum, Tether, and USD coin, the best forecasting models were EGARCH-sstd, APARCH-sstd, EGARCH-sged, and GJR-GARCH-sstd, respectively. This suggests the superiority of skewed Student t- distribution and skewed generalized error distribution over the skewed normal distribution.

Keywords: skewed generalized error distribution, skewed normal distribution, skewed student t- distribution, APARCH, EGARCH, sGARCH, GJR-GARCH

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Authors: Yohanna Panshak

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The impact of macroeconomic instability on economic growth and prosperity has been at forefront in many discourses among researchers and policy makers and has generated a lot of controversies over the years. This has generated series of research efforts towards understanding the remote causes of this phenomenon; its nature, determinants and how it can be targeted and mitigated. While others have opined that the root cause of macroeconomic flux in Nigeria is attributed to Oil-Price volatility, others viewed the issue as resulting from some constellation of structural constraints both within and outside the shores of the country. Research works of scholars such as [Akpan (2009), Aliyu (2009), Olomola (2006), etc] argue that oil volatility can determine economic growth or has the potential of doing so. On the contrary, [Darby (1982), Cerralo (2005) etc] share the opinion that it can slow down growth. The earlier argument rest on the understanding that for a net balance of oil exporting economies, price upbeat directly increases real national income through higher export earnings, whereas, the latter allude to the case of net-oil importing countries (which experience price rises, increased input costs, reduced non-oil demand, low investment, fall in tax revenues and ultimately an increase in budget deficit which will further reduce welfare level). Therefore, assessing the precise impact of oil price volatility on virtually any economy is a function of whether it is an oil-exporting or importing nation. Research on oil price volatility and its outcome on the growth of the Nigerian economy are evolving and in a march towards resolving Nigeria’s macroeconomic instability as long as oil revenue still remain the mainstay and driver of socio-economic engineering. Recently, a major importer of Nigeria’s oil- United States made a historic breakthrough in more efficient source of energy for her economy with the capacity of serving significant part of the world. This undoubtedly suggests a threat to the exchange earnings of the country. The need to understand fluctuation in its major export commodity is critical. This paper leans on the Renaissance growth theory with greater focus on theoretical work of Lee (1998); a leading proponent of this school who makes a clear cut of difference between oil price changes and oil price volatility. Based on the above background, the research seeks to empirically examine the impact oil-price volatility on government expenditure using quarterly time series data spanning 1986:1 to 2014:4. Vector Auto Regression (VAR) econometric approach shall be used. The structural properties of the model shall be tested using Augmented Dickey-Fuller and Phillips-Perron. Relevant diagnostics tests of heteroscedasticity, serial correlation and normality shall also be carried out. Policy recommendation shall be offered on the empirical findings and believes it assist policy makers not only in Nigeria but the world-over.

Keywords: oil-price, volatility, prosperity, budget, expenditure

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Authors: Edson Vengesai

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Derivative products have become essential instruments in portfolio diversification, price discovery, and, most importantly, risk hedging. Derivatives are complex instruments; their valuation, volatility implications, and real impact on the underlying assets' behaviour are not well understood. Little is documented empirically, with conflicting conclusions on how these instruments affect firm risk exposures. Given the growing interest in using derivatives in risk management and portfolio engineering, this study examines the practical impact of derivative usage on the underlying stock price exposure and systematic risk. The paper uses data from South African listed firms. The study employs GARCH models to understand the effect of derivative uses on conditional stock volatility. The GMM models are used to estimate the effect of derivatives use on stocks' systematic risk as measured by Beta and on the total risk of stocks as measured by the standard deviation of returns. The results provide evidence on whether derivatives use is instrumental in reducing stock returns' systematic and total risk. The results are subjected to numerous controls for robustness, including financial leverage, firm size, growth opportunities, and macroeconomic effects.

Keywords: derivatives use, hedging, volatility, stock price exposure

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Authors: Massimiliano Frezza, Sergio Bianchi, Augusto Pianese

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The relation between stock price volatility and trading volume represents a controversial issue which has received a remarkable attention over the past decades. In fact, an extensive literature shows a positive relation between price volatility and trading volume in the financial markets, but the causal relationship which originates such association is an open question, from both a theoretical and empirical point of view. In this regard, various models, which can be considered as complementary rather than competitive, have been introduced to explain this relationship. They include the long debated Mixture of Distributions Hypothesis (MDH); the Sequential Arrival of Information Hypothesis (SAIH); the Dispersion of Beliefs Hypothesis (DBH); the Noise Trader Hypothesis (NTH). In this work, we analyze whether stock market efficiency can explain the diversity of results achieved during the years. For this purpose, we propose an alternative measure of market efficiency, based on the pointwise regularity of a stochastic process, which is the Hurst–H¨older dynamic exponent. In particular, we model the stock market by means of the multifractional Brownian motion (mBm) that displays the property of a time-changing regularity. Mostly, such models have in common the fact that they locally behave as a fractional Brownian motion, in the sense that their local regularity at time t0 (measured by the local Hurst–H¨older exponent in a neighborhood of t0 equals the exponent of a fractional Brownian motion of parameter H(t0)). Assuming that the stock price follows an mBm, we introduce and theoretically justify the Hurst–H¨older dynamical exponent as a measure of market efficiency. This allows to measure, at any time t, markets’ departures from the martingale property, i.e. from efficiency as stated by the Efficient Market Hypothesis. This approach is applied to financial markets; using data for the SP500 index from 1978 to 2017, on the one hand we find that when efficiency is not accounted for, a positive contemporaneous relationship emerges and is stable over time. Conversely, it disappears as soon as efficiency is taken into account. In particular, this association is more pronounced during time frames of high volatility and tends to disappear when market becomes fully efficient.

Keywords: volume–volatility relationship, efficient market hypothesis, martingale model, Hurst–Hölder exponent

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Authors: Boulis M. Ibrahim, Iordanis A. Kalaitzoglou

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A micro structural pricing model is proposed in which price components account for learning by incorporating changing expectations of the trading intensity and the risk level of incoming trades. An analysis of European carbon futures transactions finds expected trading intensity to increase the information component and decrease the liquidity component of price changes, but at different rates. Among the results, the expected persistence in trading intensity explains the majority of the auto correlations in the level and the conditional volatility of price changes, helps predict hourly patterns in the bid–ask spread and differentiates between the impact of buy versus sell and continuing versus reversing trades.

Keywords: CO2 emission allowances, market microstructure, duration, price discovery

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Authors: Gabriele Iannotta

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This paper investigates credit cycles by means of an experiment based on a Kiyotaki & Moore (1997) model with heterogeneous expectations. The aim is to examine how a credit squeeze caused by high lender-level risk perceptions affects the real prices of a collateralised asset, with a special focus on the macroeconomic implications of rising price volatility in terms of total welfare and the number of bankruptcies that occur. To do that, a learning-to-forecast experiment (LtFE) has been run where participants are asked to predict the future price of land and then rewarded based on the accuracy of their forecasts. The setting includes one lender and five borrowers in each of the twelve sessions split between six control groups (G1) and six treatment groups (G2). The only difference is that while in G1 the lender always satisfies borrowers’ loan demand (bankruptcies permitting), in G2 he/she closes the entire credit market in case three or more bankruptcies occur in the previous round. Experimental results show that negative risk-driven supply shocks amplify the volatility of collateral prices. This uncertainty worsens the agents’ ability to predict the future value of land and, as a consequence, the number of defaults increases and the total welfare deteriorates.

Keywords: Behavioural Macroeconomics, Credit Cycle, Experimental Economics, Heterogeneous Expectations, Learning-to-Forecast Experiment

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Authors: H. D. Ibrahim, H. C. Chinwenyi, T. Danjuma

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An option is defined as a financial contract that provides the holder the right but not the obligation to buy or sell a specified quantity of an underlying asset in the future at a fixed price (called a strike price) on or before the expiration date of the option. This paper examined two approaches for derivation of Partial Differential Equation (PDE) options price valuation formula for the Heston stochastic volatility model. We obtained various PDE option price valuation formulas using the riskless portfolio method and the application of Feynman-Kac theorem respectively. From the results obtained, we see that the two derived PDEs for Heston model are distinct and non-unique. This establishes the fact of incompleteness in the model for option price valuation.

Keywords: Black-Scholes partial differential equations, Ito process, option price valuation, partial differential equations

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Authors: Manel Youssef, Lotfi Belkacem

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This paper employs multivariate long memory GARCH models to simultaneously estimate mean and conditional variance spillover effects between oil prices and different financial markets. Since different financial assets are traded based on these market sector returns, it’s important for financial market participants to understand the volatility transmission mechanism over time and across these series in order to make optimal portfolio allocation decisions. We examine weekly returns from January 1, 2003 to November 30, 2012 and find evidence of significant transmission of shocks and volatilities between oil prices and some of the examined financial markets. The findings support the idea of cross-market hedging and sharing of common information by investors.

Keywords: oil prices, stock indices returns, oil volatility, contagion, DCC-multivariate (FI) GARCH

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Authors: Nishi Malhotra

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Art has developed as a mode of investment and saving. Art and culture of any nation is the source of foreign direct investment (FDI) generation and growth development. Several intermediaries and skill-building organizations thrive on at and culture for their earnings. Indian art market has grown to Rs. 2000 Crores. Art establishment houses access to privileged information is the main reason for arbitrariness and volatility in the market. The commercialization of art and development of the markets with refinement in the taste of the customers have led to the development of art as an investment avenue. Investors keen on investing in these products can do so, and earnings from art are taxable too, like any other capital asset. This research paper is aimed at exploring the role of art and culture as an investment avenue in India and reasons for increasing volatilities in the art market. Based on an extensive literature review and secondary research, a benchmarking study has been conducted to capture the growth of the art as an investment avenue. These studies indicate that during the financial crisis of 2008-10, the art emerged as an alternative investment avenue. The paper aims at discussing the financial engineering of various art funds and instruments. Based on secondary data available from Sotheby’s, Christies, Bonham, there is a positive correlation between strategic diversification and increasing return in the Art market. Similarly, digitization has led to disintermediation in the art markets and also helped to increase the market base. The data clearly enumerates the growing interest of the Indian investor towards art as an investment option. Much like any other broad asset class, art market too thrives on excess returns provided by diversification. Many financial intermediaries and art funds have emerged, to offer valuable investment planning advisory to a genuine investor. This paper clearly highlights the increasing returns of strategic diversification and its impact on reducing volatility in the art markets. Moreover, with coming up of e-auctions and websites, investors are able to analyse art more objectively. Digitization and commercialization of art have definitely helped in reducing volatility in world art markets.

Keywords: art, investment avenue, diversification, digitization

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Authors: Tianyang Xu

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Small underwater targets generally have a non-centrosymmetric geometry, and the acoustic scattering field of the target has spatial inhomogeneity under active sonar detection conditions. In view of the above problems, this paper takes the hemispherical cylindrical shell as the research object, and considers the angle continuity implied in the echo characteristics, and proposes a cluster-driven research method for the non-uniform characteristics of target echo angle. First, the target echo features are extracted, and feature vectors are constructed. Secondly, the t-SNE algorithm is used to improve the internal connection of the feature vector in the low-dimensional feature space and to construct the visual feature space. Finally, the implicit angular relationship between echo features is extracted under unsupervised condition by cluster analysis. The reconstruction results of the local geometric structure of the target corresponding to different categories show that the method can effectively divide the angle interval of the local structure of the target according to the natural acoustic scattering characteristics of the target.

Keywords: underwater target;, non-uniform characteristics;, cluster-driven method;, acoustic scattering characteristics

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Authors: Katja Ignatieva, Patrick Wong

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We investigated the dynamics of high frequency energy prices, including crude oil and electricity prices. The returns of underlying quantities are modelled using various parametric models such as stochastic framework with jumps and stochastic volatility (SVCJ) as well as non-parametric alternatives, which are purely data driven and do not require specification of the drift or the diffusion coefficient function. Using different statistical criteria, we investigate the performance of considered parametric and nonparametric models in their ability to forecast price series and volatilities. Our models incorporate possible seasonalities in the underlying dynamics and utilise advanced estimation techniques for the dynamics of energy prices.

Keywords: stochastic volatility, affine jump-diffusion models, high frequency data, model specification, markov chain monte carlo

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Authors: Yegnanew A. Shiferaw

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Background: commodity price volatility originating from excessive commodity price fluctuation has been a global problem especially after the recent financial crises. Volatility is a measure of risk or uncertainty in financial analysis. It plays a vital role in risk management, portfolio management, and pricing equity. Objectives: the core objective of this paper is to examine the relationship between the prices of agricultural commodities with oil price, gas price, coal price and exchange rate (USD/Rand). In addition, the paper tries to fit an appropriate model that best describes the log return price volatility and estimate Value-at-Risk and expected shortfall. Data and methods: the data used in this study are the daily returns of agricultural commodity prices from 02 January 2007 to 31st October 2016. The data sets consists of the daily returns of agricultural commodity prices namely: white maize, yellow maize, wheat, sunflower, soya, corn, and sorghum. The paper applies the three-state Markov-switching (MS) regression, the standard single-regime GARCH and the two regime Markov-switching GARCH (MS-GARCH) models. Results: to choose the best fit model, the log-likelihood function, Akaike information criterion (AIC), Bayesian information criterion (BIC) and deviance information criterion (DIC) are employed under three distributions for innovations. The results indicate that: (i) the price of agricultural commodities was found to be significantly associated with the price of coal, price of natural gas, price of oil and exchange rate, (ii) for all agricultural commodities except sunflower, k=3 had higher log-likelihood values and lower AIC and BIC values. Thus, the three-state MS regression model outperformed the two-state MS regression model (iii) MS-GARCH(1,1) with generalized error distribution (ged) innovation performs best for white maize and yellow maize; MS-GARCH(1,1) with student-t distribution (std) innovation performs better for sorghum; MS-gjrGARCH(1,1) with ged innovation performs better for wheat, sunflower and soya and MS-GARCH(1,1) with std innovation performs better for corn. In conclusion, this paper provided a practical guide for modelling agricultural commodity prices by MS regression and MS-GARCH processes. This paper can be good as a reference when facing modelling agricultural commodity price problems.

Keywords: commodity prices, MS-GARCH model, MS regression model, South Africa, volatility

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Authors: Vincenzo Russo, Rosella Giacometti

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The paper aims to assess the insurance industry's resilience under Solvency II against adverse scenarios. Starting from the economic balance sheet available under Solvency II for insurance and reinsurance undertakings, we assume that assets and liabilities follow a bivariate geometric Brownian motion (GBM). Then, using the results available under Margrabe's formula, we establish an analytical solution to calibrate the volatility of the asset-liability ratio. In such a way, we can estimate the probability of default and the probability of breaching the undertaking's Solvency Capital Requirement (SCR). Furthermore, since estimating the volatility of the Solvency Ratio became crucial for insurers in light of the financial crises featured in the last decades, we introduce a novel measure that we call Resiliency Ratio. The Resiliency Ratio can be used, in addition to the Solvency Ratio, to evaluate the insurance industry's resilience in case of adverse scenarios. Finally, we introduce a simplified stress test tool to evaluate the economic balance sheet under stressed conditions. The model we propose is featured by analytical tractability and fast calibration procedure where only the disclosed data available under the Solvency II public reporting are needed for the calibration. Using the data published regularly by the European Insurance and Occupational Pensions Authority (EIOPA) in an aggregated form by country, an empirical analysis has been performed to calibrate the model and provide the related results at the country level.

Keywords: Solvency II, solvency ratio, volatility of the asset-liability ratio, probability of default, probability to breach the SCR, resilience ratio, stress test

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Authors: Teh Raihana Nazirah Roslan, Siti Zulaiha Ibrahim, Sharmila Karim

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A warrant is a financial contract that confers the right but not the obligation, to buy or sell a security at a certain price before expiration. The standard procedure to value equity warrants using call option pricing models such as the Black–Scholes model had been proven to contain many flaws, such as the assumption of constant interest rate and constant volatility. In fact, existing alternative models were found focusing more on demonstrating techniques for pricing, rather than empirical testing. Therefore, a mathematical model for pricing and analyzing equity warrants which comprises stochastic interest rate and stochastic volatility is essential to incorporate the dynamic relationships between the identified variables and illustrate the real market. Here, the aim is to develop dynamic pricing formulations for hybrid equity warrants by incorporating stochastic interest rates from the Cox-Ingersoll-Ross (CIR) model, along with stochastic volatility from the Heston model. The development of the model involves the derivations of stochastic differential equations that govern the model dynamics. The resulting equations which involve Cauchy problem and heat equations are then solved using partial differential equation approaches. The analytical pricing formulas obtained in this study comply with the form of analytical expressions embedded in the Black-Scholes model and other existing pricing models for equity warrants. This facilitates the practicality of this proposed formula for comparison purposes and further empirical study.

Keywords: Cox-Ingersoll-Ross model, equity warrants, Heston model, hybrid models, stochastic

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Authors: Terence Soule, Tami Al Ghamdi

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To study how to transfer knowledge from multiple source problems to the target problem, we modeled the Transfer Learning (TL) process using Genetic Algorithms as the model solver. TL is the process that aims to transfer learned data from one problem to another problem. The TL process aims to help Machine Learning (ML) algorithms find a solution to the problems. The Genetic Algorithms (GA) give researchers access to information that we have about how the old problem is solved. In this paper, we have five different source problems, and we transfer the knowledge to the target problem. We studied different scenarios of the target problem. The results showed combined knowledge from multiple source problems improves the GA performance. Also, the process of combining knowledge from several problems results in promoting diversity of the transferred population.

Keywords: transfer learning, genetic algorithm, evolutionary computation, source and target

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Authors: Jaime A. Palma-Mendoza

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A supply chain consists of different processes and when conducting supply chain re-design is necessary to identify the relevant processes and select a target for re-design. A solution was developed which consists to identify first the relevant processes using the Supply Chain Operations Reference (SCOR) model, then to use Analytical Hierarchy Process (AHP) for target process selection. An application was conducted in an Airline MRO supply chain re-design project which shows this combination can clearly aid the identification of relevant supply chain processes and the selection of a target process for re-design.

Keywords: decision support systems, multiple criteria analysis, supply chain management

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Authors: Mohammad Izadkhah, Mojtaba Hoseini, Alireza Khalili Tehrani

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In this paper we presented a new method for tracking flying targets in color video sequences based on contour and kernel. The aim of this work is to overcome the problem of losing target in changing light, large displacement, changing speed, and occlusion. The proposed method is made in three steps, estimate the target location by particle filter, segmentation target region using neural network and find the exact contours by greedy snake algorithm. In the proposed method we have used both region and contour information to create target candidate model and this model is dynamically updated during tracking. To avoid the accumulation of errors when updating, target region given to a perceptron neural network to separate the target from background. Then its output used for exact calculation of size and center of the target. Also it is used as the initial contour for the greedy snake algorithm to find the exact target's edge. The proposed algorithm has been tested on a database which contains a lot of challenges such as high speed and agility of aircrafts, background clutter, occlusions, camera movement, and so on. The experimental results show that the use of neural network increases the accuracy of tracking and segmentation.

Keywords: video tracking, particle filter, greedy snake, neural network

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Authors: Christine M. Corrales, Gloria G. Delan, Rachel Luz V. Rica, Alfonso S. Piquero

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Marine protected areas (MPAs) in the study sites were established 8-13 years ago and are presently operational. This study was conducted to gather baseline information on the diversity, density and biomass of coral reef fishes inside and outside the four marine protected areas (MPAs) of Cawayan, Dalaguete; Daan-Lungsod Guiwang, Alcoy; North Granada, Boljoon and Sta. Cruz, Ronda. Coral reef fishes in the MPAs were identified using Fish Visual Census Method. Results of the t-test showed that the mean diversity (fish species/250m2) of target and non-target reef fish species found inside and outside the MPAs were significantly different. Density (ind./1,000m2) of target species inside and outside the MPAs showed no significant difference. Similarly, density of non-target species inside and outside the MPAs also showed no significant difference. This is an indication that fish density inside and outside the MPAs were more or less of the same condition. The mean biomass (kg/1,000m2) of target species inside and outside the MPAs showed a significant difference in contrast with non-target species inside and outside the MPAs which showed a no significant difference. Higher biomass of target fish species belonging to family Caesonidae (fusiliers) and Scaridae (parrotfishes) were commonly observed inside the MPAs. Results showed that fish species were more diverse with higher density and biomass inside the MPAs than the outside area. However, fish diversity and density were mostly contributed by non-target species. Hence, long term protection and management of MPAs is needed to effectively increase fish diversity, density and biomass specifically on target fish species.

Keywords: biomass, density, diversity, marine protected area, target fish species

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Authors: Dequn Teng, Tianshuo Yang, Mingrui Wang, Qiuyu Chen, Xiao Wang, Katie Atkinson

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Currently, many students are kind of at a loss in the university due to the complex environment within the campus, where every information within the campus is isolated with fewer interactions with each other. However, if the on-campus resources are gathered and combined with the artificial intelligence modelling techniques, there will be a bridge for not only students in understanding themselves, and the teachers will understand students in providing a much efficient approach in education. The objective of this paper is to provide a competency level analysis method, apparatus, electronic equipment, and storage medium. It uses a user’s target competency level analysis model from a plurality of predefined candidate competency level analysis models by obtaining a user’s promotion target parameters, promotion target parameters including at least one of the following parameters: target profession, target industry, and the target company, according to the promotion target parameters. According to the parameters, the model analyzes the user’s ability level, determines the user’s ability level, realizes the quantitative and personalized analysis of the user’s ability level, and helps the user to objectively position his ability level.

Keywords: artificial intelligence, model, university, education, recommendation system, evaluation, job hunting

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Authors: Eda Orhun

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Purpose: This paper analyzes a target firm’s decision to voluntarily disclose information during a takeover event and the effect of such disclosures on the outcome of the takeover. Such voluntary disclosures especially in the form of earnings forecasts made around takeover events may affect shareholders’ decisions about the target firm’s value and in return takeover result. This study aims to shed light on this question. Design/methodology/approach: The paper tries to understand the role of voluntary disclosures by target firms during a takeover event in the likelihood of takeover success both theoretically and empirically. A game-theoretical model is set up to analyze the voluntary disclosure decision of a target firm to inform the shareholders about its real worth. The empirical implication of model is tested by employing binary outcome models where the disclosure variable is obtained by identifying the target firms in the sample that provide positive news by issuing increasing management earnings forecasts. Findings: The model predicts that a voluntary disclosure of positive information by the target decreases the likelihood that the takeover succeeds. The empirical analysis confirms this prediction by showing that positive earnings forecasts by target firms during takeover events increase the probability of takeover failure. Overall, it is shown that information dissemination through voluntary disclosures by target firms is an important factor affecting takeover outcomes. Originality/Value: This study is the first to the author's knowledge that studies the impact of voluntary disclosures by the target firm during a takeover event on the likelihood of takeover success. The results contribute to information economics, corporate finance and M&As literatures.

Keywords: takeovers, target firm, voluntary disclosures, earnings forecasts, takeover success

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Authors: Maria Debora Braga, Luigi Riso, Maria Grazia Zoia

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Risk-based approaches to asset allocation are portfolio construction methods that do not rely on the input of expected returns for the asset classes in the investment universe and only use risk information. They include the Minimum Variance Strategy (MV strategy), the traditional (volatility-based) Risk Parity Strategy (SRP strategy), the Most Diversified Portfolio Strategy (MDP strategy) and, for many, the Equally Weighted Strategy (EW strategy). All the mentioned approaches were based on portfolio volatility as a reference risk measure but in 2023, the Kurtosis-based Risk Parity strategy (KRP strategy) and the Minimum Kurtosis strategy (MK strategy) were introduced. Understandably, they used the fourth root of the portfolio-fourth moment as a proxy for portfolio kurtosis to work with a homogeneous function of degree one. This paper contributes mainly theoretically and methodologically to the framework of risk-based asset allocation approaches with two steps forward. First, a new and more flexible objective function considering a linear combination (with positive coefficients that sum to one) of portfolio volatility and portfolio kurtosis is used to alternatively serve a risk minimization goal or a homogeneous risk distribution goal. Hence, the new basic idea consists in extending the achievement of typical risk-based approaches’ goals to a combined risk measure. To give the rationale behind operating with such a risk measure, it is worth remembering that volatility and kurtosis are expressions of uncertainty, to be read as dispersion of returns around the mean and that both preserve adherence to a symmetric framework and consideration for the entire returns distribution as well, but also that they differ from each other in that the former captures the “normal” / “ordinary” dispersion of returns, while the latter is able to catch the huge dispersion. Therefore, the combined risk metric that uses two individual metrics focused on the same phenomena but differently sensitive to its intensity allows the asset manager to express, in the context of an objective function by varying the “relevance coefficient” associated with the individual metrics, alternatively, a wide set of plausible investment goals for the portfolio construction process while serving investors differently concerned with tail risk and traditional risk. Since this is the first study that also implements risk-based approaches using a combined risk measure, it becomes of fundamental importance to investigate the portfolio effects triggered by this innovation. The paper also offers a second contribution. Until the recent advent of the MK strategy and the KRP strategy, efforts to highlight interesting properties of risk-based approaches were inevitably directed towards the traditional MV strategy and SRP strategy. Previous literature established an increasing order in terms of portfolio volatility, starting from the MV strategy, through the SRP strategy, arriving at the EQ strategy and provided the mathematical proof for the “equalization effect” concerning marginal risks when the MV strategy is considered, and concerning risk contributions when the SRP strategy is considered. Regarding the validity of similar conclusions when referring to the MK strategy and KRP strategy, the development of a theoretical demonstration is still pending. This paper fills this gap.

Keywords: risk parity, portfolio kurtosis, risk diversification, asset allocation

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Authors: Othmane Zarhali, Cécilia Aubrun, Emmanuel Bacry, Jean-Philippe Bouchaud, Jean-François Muzy

Abstract:

The Nested factor model was introduced by Bouchaud and al., where the asset return fluctuations are explained by common factors representing the market economic sectors and residuals (noises) sharing with the factors a common dominant volatility mode in addition to the idiosyncratic mode proper to each residual. This construction infers that the factors-residuals log volatilities are correlated. Here, we consider the case of a single factor where the only dominant common mode is a S-fbm process (introduced by Peng, Bacry and Muzy) with Hurst exponent H around 0.11 and the residuals having in addition to the previous common mode idiosyncratic components with Hurst exponents H around 0. The reason for considering this configuration is twofold: preserve the Nested factor model’s characteristics introduced by Bouchaud and al. and propose a framework through which the stylized fact reported by Peng and al. is reproduced, where it has been observed that the Hurst exponents of stock indices are large as compared to those of individual stocks. In this work, we show that the Log S-fbm Nested factor model’s construction leads to a Hurst exponent of single stocks being the ones of the idiosyncratic volatility modes and the Hurst exponent of the index being the one of the common volatility modes. Furthermore, we propose a statistical procedure to estimate the Hurst factor exponent from the stock returns dynamics together with theoretical guarantees, with good results in the limit where the number of stocks N goes to infinity. Last but not least, we show that the factor can be seen as an index constructed from the single stocks weighted by specific coefficients.

Keywords: hurst exponent, log S-fbm model, nested factor model, small intermittency approximation

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Authors: Muhammad Ilham Agus Salim, Mohammad Ali

Abstract:

In this article, we have elaborated a study over the nature of Islamic financial transaction by comparing between Dinar and IDR currency in Indonesia. We have found the interesting issue among scholars and practitioners in which Dinar would be a single currency ASEAN Economic Community (AEC), then becoming motivation and added value research. The assessment among dinar volatility analysis for three years ago and IDR fluctuation as well as outlook qualitative test regarding dinar are components of analysis that weak Indonesian currency should be altered to be better coinage. The value of dinar more stable than IDR and also eligible as a currency e.g. limited quantities, easy to carry, durable, easy to saved, and has the same quality. On the other hand, the existing of IDR has defeated by inflation. The EViews program explained that Dinar at current level still fluctuate, but in the first different have fixed variant. The result of analysis describing that dinar has potential as the medium exchange, because the material of dinar is relevant and feasible since 14 century until present. Therefore, dinar should be considered to solve Indonesia crisis today.

Keywords: medium of exchange, dinar & IDR currency, volatility analysis, EViews program

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Authors: Baoubadi Atozou, Koffi Akakpo

Abstract:

Since the decade 2000, the use of foodstuffs such as corn, wheat, and soybeans in biofuel production has been growing sharply in the United States, Canada, and Europe. Thus, prices for these agricultural products are rising in the world market. These cereals are the most important source of calorific energy for West African Economic and Monetary Union (WAEMU) countries members’ population. These countries are highly dependent on imports of most of these products. Thereby, rising prices can have an important impact on import levels and consequently on food security in these countries. This study aims to analyze the interrelationship between the prices of these commodities and their volatilities, and their effects on imports of these agricultural products by each WAEMU ’country member. The Autoregressive Distributed Lag (ARDL) model, the GARCH Multivariate model, and the Granger Causality Test are used in this investigation. The results show that import levels are highly and significantly sensitive to price changes as well as their volatility. In the short term as well as in the long term, there is a significant relationship between the prices of these products. There is a positive relationship in general between price volatility. And these volatilities have negative effects on the level of imports. The market characteristics affect food security in these countries, especially access to food for vulnerable and low-income populations. The policies makers must adopt viable strategies to increase agricultural production and limit their dependence on imports.

Keywords: price volatility, import of agricultural products, food safety, WAEMU

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Authors: Changhong Guo, Shaomei Fang, Yong He

Abstract:

In this paper, fractional Black-Scholes models for the European option pricing were established based on the fractional G-Brownian motion (fGBm), which generalizes the concepts of the classical Brownian motion, fractional Brownian motion and the G-Brownian motion, and that can be used to be a tool for considering the long range dependence and uncertain volatility for the financial markets simultaneously. A generalized fractional Black-Scholes equation (FBSE) was derived by using the Taylor’s series of fractional order and the theory of absence of arbitrage. Finally, some explicit option pricing formulas for the European call option and put option under the FBSE were also solved, which extended the classical option pricing formulas given by F. Black and M. Scholes.

Keywords: European option pricing, fractional Black-Scholes equations, fractional g-Brownian motion, Taylor's series of fractional order, uncertain volatility

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Authors: Abdul Kader

Abstract:

This paper attempts to investigate several contributory factors to recent volatility in the Islamic Banking sector of Bangladesh. In particular, the study explores corporate governance, credit management, credit regulations, inept board of directors, using religious sentiment as a means to deceive general people, and the degree of political interference as potential contributory factors. To find the correlation among different variables, semi-structured questionnaires were distributed among the clients, bank managers, some Banking scholars and ex-members of the board of directors of three Islamic Banks in Bangladesh. Later, ten interviews were collected from key informants to gain in-depth information about the present mismanagement of Islamic Banks in Bangladesh. After then, data were analyzed using statistical software and substantiated by secondary sources like newspapers, reports and investigative reports aired in screen media. The paper found a correlation between almost all contributory factors and recent unstable conditions in the Islamic banking sector. After performing regression analysis, this paper found a more significant relationship between some of the contributory factors with Banking volatility than others. For instance, credit management, inept board of directors, depriving customers of proving no profit in the name of business—no interest-- and political interference have a strong significant positive correlation with the present poor condition of Islamic Banking. This paper concludes that while internal management is important in recovering the losses, the government needs to ensure framing better policy for the Islamic Banking system, Central Bank needs to supervise and monitor all Islamic banks meticulously and loan receivers must go through the impartial evaluation and approved by the representatives of the Central Shariah Board. This paper also recommends that there is a need to strengthen the auditing system and improve regulatory oversight of the Islamic Banks in Bangladesh. Policy recommendations that this paper put forward could provide an outline for dealing with the existing challenging condition of Islamic Banks and these could be applied to similar problems in other countries where the Islamic Banking model exists.

Keywords: Islamic bank, volatility in banking sector, shariah law, credit management, political interference

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