Volatility Factors Research Articles

Unveiling the Influencing Factors of Cryptocurrency Return Volatility

The blossoming of cryptocurrencies during the last decade has largely influenced both the financial and the technological world. Bitcoin emerged on the edge of the financial crisis in 2008, signaling the very beginning of a financial and technological innovation, which in continuance would eventually create a lot of questions and debate previously unforeseeable. This paper aims to explore the impact of factors such as trading volume, information demand, stock returns, and exchange rates on the volatility of returns for decentralized and unbacked cryptocurrencies from 2016 to 2022 by employing the GARCH model. Based on each coin’s innate functional characteristics and market performance quantified by their respective market capitalization, the selection included Bitcoin, Ether, and XRP as representative crypto coins for the category of decentralized and unbacked cryptocurrencies. The implementation of correlation analysis and the use of the GARCH model on influencing factors for each coin revealed that decentralized and unbacked cryptocurrencies are positively related to trading volume, information demand, and exchange rates while being indifferent to a certain extent to the stock market returns of the world stock index MSCI ACWI. The results of this study provide further insight into the behavior of cryptocurrency return volatility in the new, ever-changing, and highly unpredictable crypto market as well as aid investors in their decision-making process concerning portfolio optimization.

Ontological modeling of the psychological field of a play

Ontological modeling of the psychological field of a play

Volatility Analysis of Indian Banking Sector using Bollinger Bands

This research paper aims to analyze the bank volatility in India by examining various factors that contribute to fluctuations in the Indian banking sector. The study investigates the impact of regulatory reforms, macroeconomic indicators, financial stability, and global factors on bank volatility. The research utilizes a comprehensive dataset covering a period of one year i.e (01-04-2022 to 31-03-2023) to provide an in-depth understanding of the dynamics of bank volatility in India. We have used Bollinger bands to understand the volatility of three premier banks in India namely, HDFC Bank, ICICI Bank and STATE BANK OF INDIA in Indian stock market. The findings of this study will contribute to the existing literature on banking in emerging markets, understand the factor of volatility in present times and will also provide valuable insights for policymakers and stakeholders in the Indian banking sector.

Central Bank Credibility’s Effect on Stock Exchange Returns’ Volatility: Evidence from OECD Countries

Central bank characteristics are important determinants of stock market returns and their volatility. While the literature has examined the effects of transparency and independence, no research has been conducted so far on the effect of central bank credibility on stock market returns’ volatility. A panel regression using financial and macroeconomic data from 45 OECD member countries over the period of 1998–2022 tested the hypothesis that central bank credibility determines stock exchange returns’ volatility. The results indicated that credibility reduces stock returns’ volatility, remaining robust and statistically significant across models. Economic growth also decreases stock market volatility, while money-market interest rates’ volatility, the stock market’s turnover ratio, and economic/financial crises act as amplifying factors of stock market volatility. All variables, except for economic growth, exhibit unidirectional causality, leading to changes in stock market volatility.

CRYPTOCURRENCY PRICE PREDICTION: A HYBRID LONG SHORT-TERM MEMORY MODEL WITH GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY

Cryptocurrency is a virtual payment instrument currently popular as an investment alternative. One type of cryptocurrency widely used as an investment is Bitcoin due to its high-profit potential and risk due to unstable exchange rate fluctuations. This high exchange rate fluctuation makes trading transactions in the crypto market speculative and highly volatile. To overcome this volatility factor, this research used the Generalized Autoregressive Conditional Heteroscedasticity forecasting method to describe the heteroscedasticity factor, as well as a Recurrent Neural Network (RNN) with long-short-term memory that has feedback in modeling sequential data for time series analysis. The two methods are combined to overcome the dependency of time series data in the long term and the heteroscedastic effect of the volatility of price changes. The results of the GARCH-LSTM hybrid model in this study show a Mean Absolute Percentage Error (MAPE) value of 15.69%. The accuracy value is obtained from the division of training data by 80% and testing data by 20%, with the number of neurons as many as three and epochs of 100 using the Adam optimizer. The MAPE accuracy results show a good prediction in predicting the value.

Bayesian Network Demand-Forecasting Model Based on Modified Particle Swarm Optimization

With the increasing variety of products, the increasing substitutability of products, and the trend of customized products, the volatility of market demand is increasing, which poses a challenge to make accurate demand forecasting. The Bayesian method is particularly promising and appealing when the data fluctuate greatly. This paper proposes a product-demand forecasting model based on multilayer Bayesian network, which introduces hidden layer variables and volatility factors to meet the time series connection and volatility of the demand data. However, most studies use sampling methods to estimate the parameters. We use Bayesian maximum a posteriori estimation to estimate the model parameters and introduce an improved particle swarm optimization algorithm (MPSO) to optimize the objective function. In order to increase the diversity of the particle population and accelerate the convergence, an adaptive particle velocity, position updating strategy, and nonlinear changing inertia weight are introduced in the algorithm. Finally, RMSE (Root Mean Square Error) and MAPE (Mean Absolute Percentage Error) are used as the evaluation criterion to conduct experiments on six different datasets, and the experimental results are compared with the results of the ARIMA (autoregressive integrated moving average model) method and PSO algorithm. The experimental results show that the method has a good prediction effect. It provides a new idea for demand forecasting in the supply chain.

ПРОГРАММЫ ЛОЯЛЬНОСТИ КАК ИНСТРУМЕНТ РАЗВИТИЯ ESG-ПРАКТИК НА ПРЕДПРИЯТИЯХ ГОСТЕПРИИМСТВА В СИТУАЦИИ ТРАНСФОРМАЦИИ РЫНКА

The article is devoted to the consideration of the importance of developing loyalty programs as a tool for the development of ESG practices at a hospitality enterprise. The authors focus on the study of factors of economic instability and market volatility and note that hospitality enterprises are forced to look for new ways to attract and retain customers in order to stand out from competitors and ensure their sustainability and growth. Special attention is paid to the principles of ESG as an integral part of modern business. It is concluded that the integration of ESG practices allows enterprises to increase their attractiveness to investors, improve their reputation and reduce the risks associated with climate change and social problems.

The incremental information in the yield curve about future interest rate risk

Using high-frequency intraday futures prices to measure yield volatility at selected maturities, we find that daily yield curves carry incremental information about future interest rate risk at the long end, relative to that contained in the time series of historical volatilities. Some of the information in the yield curves is not captured by standard affine models. Our results point to the existence of an unspanned stochastic volatility factor. Both time series and yield curve based forecasts provide utility to a risk averse investor, relative to a random walk. Information from the two sources can be combined to enhance yield volatility forecasting performance.

Islamic Fund Managers' Market Timing Abilities and their Impact on Fund Performances

This study examines Shariah-compliant fund's financial performance compared to conventional funds based on managerial timing abilities. Compared with traditional fund managers' timing skills, the study focuses on the most neglected area of Shariah-compliant manager timing abilities, liquidity, and volatility timing abilities. The study uses 69 funds whose significant exposure is the equity between January 2010 and June 2022, consisting of three investment styles: equity, asset allocation, and balanced funds. The study incorporates volatility factors based on Busse (1999) and liquidity factors from Amihud (2002) into the Carhart (1997) four-factor model. The study found that Islamic fund managers have better liquidity and volatility timing skills than conventional fund managers. The results are robust for analyzing individual funds based on different liquidity measures. There is no substantial evidence that Islamic and traditional fund managers can predict market returns.

Some factors of energy aid volatility across developing countries: A special focus on renewable sector

Energy aid effectiveness is paramount for the timely achievement of Sustainable Development Goals (SDG)-7. However, the volatility of energy aid reduces its effectiveness. Given this, the paper empirically investigates the determinants of energy aid volatility and its renewable energy generation (REG) component for 76 aid-recipient countries during 2002–2019. By considering recipient and donor-specific factors, we also explore the linkages of all these factors with the direction of volatility, i.e., aid shortfalls and windfalls. Findings reveal that variations in energy/REG aid volatility reflect the recipient's macroeconomic and institutional characteristics more than donor-specific factors. Moreover, the impacts of all these factors vary across aid shortfalls and windfalls. Specifically, economic prosperity and aid dependency increase energy/REG aid volatility, infused by different directions of aid shocks. Contrarily, oil rents, political stability, and strong democracy reduce aid volatility. Multilateral donors provide more stable funding compared to bilateral donors. We also observe that donors provide stable REG aid in countries with inadequate renewable policies and clean development mechanism funding, playing a catalyst role in supporting institutionally weak countries in the renewable sector. We draw certain policy implications for donors and recipients to manage and mitigate the adverse consequences of volatile energy aid flows.

Oil prices uncertainty, endogenous regime switching, and inflation anchoring

SummaryUsing a novel approach to model regime switching with dynamic feedback and interactions, we extract latent mean and volatility factors in oil price changes. We illustrate how the volatility factor constitutes a useful measure of oil market risk (or oil price uncertainty) for policy makers and analysts as it captures uncertainty not reflected in other economic/financial uncertainty measures. Then, in the context of a VAR, we investigate the role of oil price uncertainty in driving inflation expectations and inflation anchoring. We show that shocks to the mean factor lead to higher expected inflation and inflation disagreement among professional forecasters and households. In contrast, shocks to the volatility factor act as aggregate demand shocks in that they result in lower expected inflation, yet they do increase disagreement about future inflation among professional forecasters and, especially, among households. We also provide econometric evidence suggesting the proposed endogenous volatility switching model can outperform other regime switching models.

Comparative Research of Internal and Border Regions: Analyzing the Differences in the Cyclical Dynamics of Industries for Industrial Policy and Territorial Development

The differentiation in the development of regions remains a major challenge for the working out-of-state industrial and regional policies aimed at balanced and sustainable development. In theory, regional differences between internal and border regions can be explained by differences in natural resources, and economic and industrial potential, as well as by the existence of external boundaries. Border regions have higher risks in ensuring the geo-political sustainability of an industry. External boundaries, as well as differences in industry dynamics between regions, cycle stages, and industry trends, are often overlooked in industrial policy making, which in itself can be a factor of volatility. In this research based upon the Russian economy, we test the hypothesis that it is possible to define the industrial cycle with the help of the index of production. The analysis is based on the official Russian statistics from January 2005 to December 2021. To test the hypothesis, an original 12-step method of analysis was used, which allows such a mathematical model to be selected that will best describe the industry cycle and allows the trend to be estimated. The cyclic dynamics were assessed with the help of structural and parametric identification of modeling and the forecasting of trajectories of evolving dynamics based upon econophysics methodology, the use of median trends, and wavelet analysis. The comparative study was made based on the example of four sectors: the food, chemical, pharmaceutical (production of medicines and materials used for medical purposes), and automotive industries. The results show, first, that there are significant differences in the dynamics of industry cycles in both the internal and the border regions, which need to be taken into account to implement the progressive economic structure and specialization strategies of a region. Secondly, the group of border regions in the food, chemical, and pharmaceutical industries is growing at a higher rate.

Semi-Open Multi-Distribution Center Path Planning with Time Windows

A well-planned robot dispatching platform reduces costs and increases efficiency for companies while also reducing carbon emissions and achieving sustainable development. At the moment, the solution to the difficulty of warehouse logistics is use of multiple distribution centers with autonomous mobile robots (AMR). To solve this problem, this paper establishes a semi-closed model of multiple distribution centers, considering the number of cycles and the number of vehicles. An improved ant colony algorithm is proposed to improve the heuristic function based on the node distance relationship to improve the quality of path search. Dynamic variable pheromone concentration and volatility factors are set to accelerate the convergence speed of the algorithm while effectively reducing the problem of the premature algorithm. The traditional ant colony algorithm and the improved ant colony algorithm are used to solve the established model. In addition, the results show that the traditional ant colony algorithm has a certain rate of dominance in the single-day cost of the closed distribution model, but the overall comprehensive cost is lower than that of the improved ant colony algorithm. The single-day cost of the semi-open multi-distribution center logistics and distribution model is lower than that of the closed multi-distribution center logistics and distribution model, and the 7 day average cost is reduced by 12%. The improved ant colony algorithm can save about 119 kWh of electricity under the same target volume requirement, which achieves the company’s goals of cost reduction and increased efficiency, as well as green and sustainable development.

Three-dimensional continuous picking path planning based on ant colony optimization algorithm.

Fruit-picking robots are one of the important means to promote agricultural modernization and improve agricultural efficiency. With the development of artificial intelligence technology, people are demanding higher picking efficiency from fruit-picking robots. And a good fruit-picking path determines the efficiency of fruit-picking. Currently, most picking path planning is a point-to-point approach, which means that the path needs to be re-planned after each completed path planning. If the picking path planning method of the fruit-picking robot is changed from a point-to-point approach to a continuous picking method, it will significantly improve its picking efficiency. The optimal sequential ant colony optimization algorithm(OSACO) is proposed for the path planning problem of continuous fruit-picking. The algorithm adopts a new pheromone update method. It introduces a reward and punishment mechanism and a pheromone volatility factor adaptive adjustment mechanism to ensure the global search capability of the algorithm, while solving the premature and local convergence problems in the solution process. And the multi-variable bit adaptive genetic algorithm is used to optimize its initial parameters so that the parameter selection does not depend on empirical and the combination of parameters can be intelligently adjusted according to different scales, thus bringing out the best performance of the ant colony algorithm. The results show that OSACO algorithms have better global search capability, higher quality of convergence to the optimal solution, shorter generated path lengths, and greater robustness than other variants of the ant colony algorithm.

Mean-variance investing with factor tilting

Factor analysis proposes an alternative approach to standard portfolio theory: the latter is optimisation based, while the former is estimation based. Also, in standard portfolio theory, returns are only explained by the portfolio volatility factor, while factor analysis proposes a multiplicity of factors, which the managers can choose from to tilt their portfolios. In attempting to reconcile these alternative worlds, we propose a penalised utility function, incorporating both the Markowitzian risk-return trade-off and the manager’s preferences towards factors, and discriminating among losses and gains relative to a reference asset. The penalisation affects the optimisation process, favouring the selection of portfolios with less variance and more tilted towards the chosen risk factors. Penalty levels set by the manager generalise the traditional notion of risk aversion. We test our model by building an investment portfolio based on a combination of asset classes and selected investing factors, focussed on the eurozone. To identify the optimal portfolio, we adopt a set of three metaheuristic optimisation algorithms: the fitness function stochastic maximization using genetic algorithms, differential evolution algorithm for global optimisation, and the particle swarm optimisation, and dynamically choose the best solution. In this way, we can improve the Markowitzian optimisation by tilting the asset allocation with managers’ expectations and desired exposures towards designated factors.

Optimal investment with correlated stochastic volatility factors

AbstractThe problem of portfolio allocation in the context of stocks evolving in random environments, that is with volatility and returns depending on random factors, has attracted a lot of attention. The problem of maximizing a power utility at a terminal time with only one random factor can be linearized thanks to a classical distortion transformation. In the present paper, we address the situation with several factors using a perturbation technique around the case where these factors are perfectly correlated reducing the problem to the case with a single factor. Our proposed approximation requires to solve numerically two linear equations in lower dimension instead of a fully nonlinear HJB equation. A rigorous accuracy result is derived by constructing sub‐ and super‐solutions so that their difference is at the desired order of accuracy. We illustrate our result with a particular model for which we have explicit formulas for the approximation. In order to keep the notations as explicit as possible, we treat the case with one stock and two factors and we describe an extension to the case with two stocks and two factors.

All for One and One (Space) for All!

Global equity is the common pursuit of most people all over the world. Although fairness is not absolute, we should try our best to achieve global fairness in resource allocation and opportunity supply. But it’s hard to make the distribution of the resources and development opportunities fair enough with the limited resources on the earth. With the depletion of the earth's resources, more and more people realize that the earth's resources are not enough for people to use permanently, so scientists have created a great idea of asteroid mining. With the limitation of technology and the economy, there is still a long way to go. After solving the problems above, it’s still hard to achieve global equity while allocating mineral resources. Aiming at rationalizing the allocation of resources mined on the asteroids, we try to establish a global equity model. At the same time, we will also study the feasibility and vision of asteroid mining. The impact of variability and volatility factors on the model will be analyzed.

A hybrid sentiment based stock price prediction model using machine learning

Accurate stock market prediction is highly desirable to corporations and investors. In this study a deep learning model based on LSTM, BiLSTM with attention mechanism used to predict stocks closing price for next 30 days of two banks listed in Pakistan Stock Exchange. For accurate stock price prediction, it is necessary to consider volatile factors such as news sentiments along with historical data. This study covers that aspect by incorporating news sentiments along with historical stock data that is distributed over a span of ten years from Jan 2011 to July 2021. Preprocessing and sentiment analysis of data was performed using python NLTK module. After that we built a univariate deep learning model based on four layers of LSTM and one dense layer to combine all layers and performed a prediction on train and test data followed by a multivariate deep learning model based on BiLSTM with self-attention mechanism and found out that incorporation of news sentiments really improved the prediction accuracy by reducing the values of mean squared error. Finally, we did the prediction for next 30 days of stock closing price of two banks and compared those predicted prices with actual prices and got quite accurate results.

Volatile factors in the Australian dollar exchange rates

Volatile factors in the Australian dollar exchange rates

Homologue-based Estimations of Short-Chain Chlorinated Paraffin Emissions and Concentrations in Japan

Short-chain chlorinated paraffins (SCCPs) are persistent organic pollutants. In Japan, SCCPs are widely detected in the environment although the production and use of SCCPs have been banned. It is essential to estimate the amount and sources of SCCPs to implement countermeasures for SCCP emission. In this study, we estimated the emission and environmental concentrations of SCCP homologues in Japan from 1950 to 2050. Initially, the material flow of total SCCPs was estimated considering the material recycling process. The emission from each process during the entire life cycle of products containing SCCPs was estimated for each homologue. Volatilization factors of long-term-use products were specifically estimated from release factors and surface area, weight, and SCCP concentration of products containing SCCPs. The environmental concentrations were then estimated using the environmental fate model. In the material flow and emission estimation, parameter uncertainties, such as emission factors and SCCP application distribution, were considered, assuming each parameter follows a uniform distribution. The results of emission estimation suggested a decreasing trend of SCCP emission in recent years and continuation of this trend in the future. However, some emissions from long-term-use products may persist. In the future, products that have a long lifetime with closed-loop recycling, such as polyvinyl chloride wire-coating materials, are expected to significantly contribute to atmospheric emission. Moreover, recycled products may contain SCCPs. Based on the estimated concentration, the estimated water and sediment concentrations were within the range of the observed concentrations. Conversely, we could reduce the gap between the estimated and observed atmospheric SCCP concentrations compared with those in previous studies that did not consider the volatilization factor for each SCCP-containing product and material recycling process. However, a difference between the estimated and observed concentrations suggests that some unconsidered emission sources may exist that emit SCCPs into the atmosphere, such as SCCPs as impurities in medium-chain chlorinated paraffins, imported products containing SCCPs, and atmospheric advection from other countries. Hence, it is essential to calculate emissions from sources other than products that intentionally contain SCCPs produced in the past.