Level Of Volatility Research Articles

When Vegas Comes to Wall Street: Associations Between Stock Price Volatility and Trading Frequency Amongst Gamblers

AbstractBoth gambling and trading involve risk-taking in exchange for potential financial gains. In particular, speculative high-risk high-frequency trading closely resembles disordered gambling behaviour by attracting the same individuals who tend to be overconfident, sensation-seekers, and attracted to quick large potential payoffs. We build on these studies via an incentivised experiment, in which we examine how manipulated levels of market volatility affected trading frequency. Gamblers (N=604) were screened based on the existence of household investments and recruited across the four categories of the Problem Gambling Severity Index. The volatility of stocks was manipulated between-participants (high vs. low). Participants traded fictitious stocks and were provided bonuses based on the results of their trading activity (M=US$4.77, range=[0, 16.99]). Participants traded more often in the high-volatility market, and this finding remained robust after controlling for financial literacy, overconfidence, age, and gender. Many investors trade more frequently than personal finance guides advise, and these results suggest that individuals are more likely to commit this error in more volatile markets. Exploratory analyses suggest that the effect of the volatility manipulation was strongest amongst gamblers who were at low-risk of experiencing gambling harms. As they might be otherwise considered low-risk, these individuals could be overlooked by protective gambling interventions yet nonetheless suffer unmitigated financial harms due to unchecked excessive trading.

High-Frequency Volatility Modeling of Chinese Agricultural Products Market Based on Garch-Type Models

The modeling of high-frequency volatility is of utmost importance in comprehending market dynamics and the characteristics of risk. The adoption of high-frequency volatility modeling in the agricultural sector has the potential to enhance risk management for food production enterprises through the utilization of hedging strategies to mitigate the impact of food price changes. The GARCH (Generalized Autoregressive Conditional Heteroskedasticity) class of models is well-suited for the analysis and prediction of volatility in financial time series. These models effectively capture the highest level of volatility observed in the time series data. Therefore, this research utilizes one-minute trade data of the Wind Agriculture Index (886045.WI) to examine the efficacy and predictability of several GARCH class volatility models. When it comes to fitting the data within the sample, the TGARCH model demonstrates superior performance compared to both the GARCH and EGARCH models. The fluctuations in the price changes of the Wind Agriculture Index exhibit characteristics of time variability and clustering, which can be attributed to the relatively low barriers for entry and exit in the agricultural planting sector. Simultaneously, within the agricultural market, an imbalance is observed whereby the influence of positive information on price volatility surpasses that of negative information.

Herding Behavior in The Asean Stock Market During The Covid-19 Pandemic

This study aims to detect indications of herding behavior that occurred in the ASEAN capital market throughout the COVID-19 pandemic. The research spanned from December 31, 2019, to June 30, 2021, encompassing the duration of the COVID-19 pandemic. This research used panel regression and rolling window regression methods on the dependent variable of Cross-Sectional Absolute Deviation (CSAD) to detect indications of herding behavior. This research also employed daily stock return data for all companies on several stock exchanges in five ASEAN countries. The panel regression model was then utilized in the analysis. The panel regression results demonstrated the absence of herding behavior on the stock exchanges in ASEAN. This was evident as the Rm2 coefficient consistently revealed a significant positive sign throughout the entire observation period. However, upon rechecking through rolling window regression, it was uncovered that, at some times, herding did occur on the ASEAN stock exchange during the COVID-19 pandemic. Furthermore, the government’s policy response by limiting short selling could reduce herding on the stock market. Simultaneously, the government’s policy response to the COVID-19 epidemic, proxied by the Stringency Index, apparently heightened investor anxiety and promoted herding behavior. Lastly, the heightened levels of anxiety and volatility experienced during the pandemic (variable IVI) led to a rise in herd behavior among the stock markets of ASEAN countries.

Export product quality and inclusivity in developing countries

AbstractImproving the quality of export products is critical for promoting economic development, including for the achievement of sustainable development goals. The present study has examined the effect of export product quality improvement on inclusivity in developing countries. Inclusivity reflects a concomitant increase in the real per capita income, reduction in within‐country income inequality, and poverty reduction. The analysis covers 101 developing countries over the period from 1980 to 2014, and uses primarily the two‐step system Generalized Method of Moments estimator. It shows that export product quality improvement results in greater inclusivity, especially in countries that face high levels of economic growth volatility, including large magnitudes of external shocks. Likewise, export product quality improvement leads to greater inclusivity in countries that experience high levels of export product concentration. The analysis sheds light on the positive contribution of export product quality improvement to inclusivity in developing countries.

Family income volatility among Chinese children, 2010–2018

A stable economic environment in a family lays the foundation for children’s healthy development, and income volatility is a key indicator of family economic (in)stability. Using longitudinal data from the China Family Panel Studies (CFPS), this study investigates exposure to family income volatility and its social determinants for Chinese children during the period 2010–2018. The results show that Chinese children experienced high levels of family income volatility during 2010–2018, and childhood exposure to income volatility in China is closely related to both family socioeconomic characteristics and structural factors. Specifically, children from low-income families, with less-educated and non-state-sector-employed parents, and holding a rural hukou (household registration) are more likely to experience childhood economic instability. Given that children from socioeconomically disadvantaged families already face challenges associated with constrained socioeconomic resources, the fact that they are also more likely to live in a precarious economic environment may put them at a double disadvantage in early life.

Comparative Analysis of Volatility Levels of 10 Stock Indices on the Indonesian Stock Exchange on 2 Economic Events Using Time Series Econometric Methods

The purpose of this study is to determine the level of volatility of the 10 Stock Indices on the IDX during COVID-19 Economic Crisis and in the Economic Recovery periods in order to understand the benefits and risks of investing in post-economic crisis The results of this study indicate that 6 of 10 stock indices have no difference in the level of volatility in the 2 events. Only 4 Stock Indices have different levels of volatility. In the post-crisis period, the volatility of the stock index is at a very low level, so it is very interesting to see the investment returns opportunities. Although in these 2 events; majority of volatility levels of stock indices are relatively low in terms of parameters; but there is a fairly large gab of volatility values. High levels of volatility can also occur not only during crisis but also when the economy has improved.

A probabilistic forecast methodology for volatile electricity prices in the Australian National Electricity Market

The South Australia region of the Australian National Electricity Market (NEM) displays some of the highest levels of price volatility observed in modern electricity markets. This paper outlines an approach to probabilistic forecasting under these extreme conditions, including spike filtration and several post-processing steps. We propose using quantile regression as an ensemble tool for probabilistic forecasting, with our combined forecasts achieving superior results compared to all constituent models. Within our ensemble framework, we demonstrate that averaging models with varying training-length periods leads to a more adaptive model and increased prediction accuracy. The applicability of the final model is evaluated by comparing our median forecasts with the point forecasts available from the Australian NEM operator, with our model outperforming these NEM forecasts by a significant margin.

Most suitable threshold method for extremes in financial data with different volatility levels

Most suitable threshold method for extremes in financial data with different volatility levels

Bitcoin’s Energy Consumption, Price Volatility, and Environmental Pollution: Contagion and Causality Dynamics Under Heteroskedasticity and Nonlinearity

Bitcoin’s (BT) energy demand due to the proof of work mining algorithm and transaction methods has been criticized in recent literature because of BT’s global energy consumption (EC) being equal to or even greater than that of some industrialized economies. This study explores the contagion and causality dynamics under nonlinearity and heteroskedasticity with Markovian‐type regime switches among BT price volatility, EC from BT, and its effects on the global carbon dioxide (CO2) emissions with a daily sample of January 2, 2012–December 24, 2023. In the empirical methodology, following the preliminary tests that indicated nonlinearity and heteroskedasticity, this study employed novel Markov‐switching‐based nonlinear volatility copula and causality models to capture the marginal distributions of BT, its EC and CO2 emissions, and their joint distributions for contagion and tail dependence and determine nonlinear and asymmetric causality relations in distinct regimes of high and low volatility governed by Markov chains. The empirical results indicate significant and positive copula parameters with high magnitudes demonstrating asymmetric contagion and tail dependence occurring at the extreme levels of BT price volatility in the upper and lower tails during both regimes. Novel Markov‐switching‐based copula causality tests designated unidirectional causality from BT to CO2, as well as from BT’s EC to CO2 emissions during each regime coupled with existent bidirectional causality confirming cyclical feedback effects between BT prices and its EC in both regimes. For robustness and comparison, single‐regime copula models were employed, and their findings confirmed the results concerning significant and positive tail dependence and contagion. However, the single‐regime approach led to a set of inconsistencies in causality results owing to omitting nonlinearity while capturing heteroskedasticity and confirming the nonrejection of causality between BT, EC, and CO2 emissions. Important policy recommendations include green alternatives to cryptocurrency‐mining algorithms.

Biostimulatory Activity of Pyroligneous Acid Enhanced Metabolites Accumulation in Grape Wine

Pyroligneous acid (PA) is a known biostimulant in agriculture, but its effects on metabolite accumulation in grape berries and wine are not well understood. This study investigated the impact of varying PA concentrations (0%, 2%, 4%, 8%, and 12%) on metabolite profiles in grape wine (Vitis vinifera cv. KWAD7-1). Using a randomized complete block design, PA was applied to grape leaves at 14-day intervals. Wine samples were analyzed using NMR spectroscopy, identifying 52 metabolites across seven compound groups. The 12% PA treatment resulted in the highest o Brix content: 0.14-fold higher than the control. This treatment significantly (p<0.05) altered the concentrations of organic acids, increasing most except for malic and acetic acids, which decreased by 0.16-fold and 0.60-fold, respectively. Notably, 12% PA increased total amino acid content by 5.96- fold compared to the control and enhanced glucose and fructose contents by 0.25- and 1.40-fold, respectively. A 0.53-fold increase in myo-inositol was also observed with 12% PA, suggesting potential improvements in nutritional value. Principal component analysis revealed distinct metabolic profiles for grapes treated with 12% PA, characterized by elevated levels of phenolics, alcohols, volatiles, and carbohydrates. These findings suggest that PA application can be used to manipulate grape wine metabolites, potentially enhancing sensory attributes and nutritional value. This study provides insights into the use of PA as a tool for modulating wine quality to meet consumer preferences.

The fluctuation of pig prices and the identification of major drivers in China.

In recent years, the domestic live pig price has risen and fallen rapidly and fluctuated frequently, which has greatly impacted the live pig industry. The level of price volatility in the pig market has served as a significant indication of the progress of the agricultural sector. Identifying the dominant factors affecting the fluctuation of pig prices has become more important. Based on the monthly data of the pig industry from February 2009 to December 2022, this paper constructs an index system of influencing factors of pig price from four aspects: uncertain impact, supply factors, demand factors, and macro-environment factors. By using the transfer model of the Markov regime (MS-VAR), we obtained the probability plot of zone transition for pig price volatility, the impulse response effect diagram of factors affecting pig price, and the cumulated impulse response effect diagram of factors affecting pig price, and analyze the reasons for the ups and downs of pig price according to the above results. The findings indicate prominent features of zone transition in the price fluctuation of China's pig market. From 2017 to 2022, the domestic pig price frequently switches between rising and falling zones, and the "falling pig price stage" and "rising pig price stage" in the non-stationary state last for a relatively short and discontinuous period. There is little probability that the price of live pigs will directly change from rising to falling, and there will be a smooth buffer stage in the price rise and fall process. Among the factors that affect the fluctuation of live pig prices, the dominant factor of frequent and large fluctuation of pig prices is the pig epidemic situation in external factors. Among the internal influencing factors, the changes in farming costs have the greatest significant influence on the fluctuation of pig prices. These results provide a decision-making reference for legislators to carry out epidemic risk prevention and control better, stabilize the market pig price, and provide empirical evidence for market participants to accurately avoid price risks through multiple channels and ways and ensure stable profitability.

Сентимент и стадное поведение частных инвесторов: кластерный анализ российского фондового рынка

In this paper, the sentiment of private investors and the divergence of opinions of users of online investment platforms are analyzed as factors in the emergence of herd behavior on the stock market in different clusters. This clusters are formed on the basis of stock exchange information on shares of Russian issuers. The relevance of the study lies in determining the significance of consensus periods and their impact on the behavior of different groups of investors on the Russian stock market depending on different levels of risk taking by investors. Based on the Russian stock market data for the period from 2019 to 2023, 66 discussed Russian stocks were analyzed. To conduct sentiment analysis, an algorithm was applied to collect and automatically classify textual data using machine learning methods. As proxies for private investor sentiment, metrics of logarithmic sentiment and market-wide divergence of opinions on the stocks under discussion were constructed. The testing of the research hypotheses was based on the implementation of clustering and quantile regression analysis methods. As a result, the significant role of divergence of opinions of Internet users in the formation of herd behavior of private investors on shares with lower average return and risk level was determined. It was also found that asset clustering helps to determine the opposite behavior of investors on stocks with higher volatility level. Consensus of opinions in the market is a signal to deviate from the general market trend. Finally, the test of the hypothesis about the significant influence of investor sentiment did not provide sufficient evidence that this kind of sentiment is important for determining herd behavior in the market.

COVID-19'UN İSLAMİ VE GELENEKSEL HİSSE SENEDİ ENDEKSLERİ ÜZERİNDEKİ ETKİSİ

The objectives of this research are to study Islamic stock indexes during the time of Covid-19 extensively and compare it with conventional stock indexes. Our research aims to analyse how stock returns indexes of Islamic and conventional have been affected by COVID-19. So, in other word, the research objected to analyse the effect of COVID-19 on the returns and volatility of Islamic and conventional stocks indexes by using ARIMA-X and EGARCH-X models. In keeping with this objective, the Islamic and conventional stocks indexes were used to estimate the financial stock return, and the dummy variable as of number of everyday cases of each country was used to estimate the effect of COVID-19.
 The research investigates the levels of uncertainty and volatility in Islamic and conventional stock indexes prior to and during the Covid-19 crisis. Additionally, it compares Islamic stock markets with others and analyses the impact of the pandemic on these markets. The findings reveal statistically significant results, with certain indexes demonstrating significance at levels of 10%, 5%, and 1%.
 By overall looking we can say that Nigerian indexes were affected the most among others which include the Islamic and the conventional, moreover the least affected by the uncertainty was in Indonesia and Malaysia and the Islamic indexes in general were better. We can conclude that Islamic stock indexes were affected like others by the COVID-19.

Cryptocurrency Price Prediction Using Machine Learning Techniques

Abstract: The goal of this project is to use machine learning to forecast cryptocurrency values. As a result of their high levels of volatility, cryptocurrencies are notoriously difficult to anticipate in terms of value. SARIMA (Seasonal Auto Regressive Integrated Moving Average) algorithm that we suggest using to capture the intricate dynamics of the bitcoin market. Our machine learning models will be trained using the gathered data, and they will then be utilised to forecast future cryptocurrency values. The project's final product is anticipated to be a useful tool for cryptocurrency traders, analysts, and investors, giving them a more precise way to make data-drive investment decisions.

Political Implication of Islamic Dinar for the Stability in ASEAN-5 Region Post COVID-19 Crises

The paper analyses the possibility of monetary integration in ASEAN-5 countries, Indonesia, Malaysia, Philippines, Thailand, and Singapore for improving the stability in the region. The idea for regional monetary integration is grounded by the process of monetary union theory within the member states. In terms of volatility, by using certain assessment, the ASEAN-5 currencies are suggested to peg their national currencies into Dinar-Dirham Islamic exchange system since it empirically brings the lowest level of volatility, both during normal and crisis periods. Therefore, Dinar-Dirham could be proposed as the anchor currency for ASEAN-5 countries. The results statistically suggested that all ASEAN-5 countries could be grouped as eligible to employ Dinar-Dirham as their currencies.

Optimizing Network Reliability in Dynamic or Rapidly-Changing Topologies

With communication, power and transportation networks facing ever-greater dynamics, optimizing reliability poses significant modeling and computational challenges. Most existing reliability optimization research assumes static topologies with predefined failure probabilities and connectivity demands. This work puts forth a time-varying mathematical program to maximize expected network reliability under shifting topological uncertainties. Sets, parameters, decision variables and constraints are defined as functions of time to capture variability in links, capacities, risks and demands. The formulation adapts to detected changes through re-optimization triggered by model error thresholds. Approximation techniques based on constraint sampling and decomposition address solve efficiency for large fluid networks. Evaluations on simulated dynamic test cases demonstrate superior reliability versus periodic and static optimization approaches while fulfilling budget limits. Accuracy metrics assess model fidelity over increasing volatility levels. Implementation case studies exhibit optimized resilience in software-defined communication architectures, smart grid reconfiguration, and adaptive transportation maintenance scenarios. The mathematical programming foundation provides a pathway to achieve connectivity resilience for critical infrastructure networks facing intensifying dynamics. The integration of optimization, prediction and adaptive response provides a paradigm for decision making under modern uncertain conditions.

Assessment of the impact of inflation on financial losses and real income of Russian enterprises

Aim. To assess the impact of inflation on financial losses and real income of Russian enterprises, as well as to develop recommendations to reduce this negative impact.Objectives. To determine the financial losses of the enterprises of the real sector of the economy from the impact of inflation and inflation risks; to study and evaluate the relationship between changes in inflation and exchange rates at different levels of market volatility; to give recommendations on the choice of the least sensitive to the impact of inflation technologies of settlement with the creditor and obtaining investment income.Methods. The author applied quantitative methods of time and inflation factors accounting, methods of system, logical, situational comparative analysis and financial risk management.Results. Analytical assessments of inflation impact on financial losses, real income and real profitability of Russian enterprises have been obtained. Practical recommendations on the choice of the least inflation-sensitive technologies of settlement with the creditor and obtaining investment income are given. According to the results of the research, evaluation of the interrelation between the changes in inflation and exchange rates, the conditions of application of the theory of purchasing power parity of the national currency of G. Cassel are determined and recommendations are given on forecasting inflationary and currency risks depending on the filling of commodity markets.Conclusions. The quantitative estimates obtained in this article and the developed recommendations are a theoretical and methodological basis for making adequate financial decisions in conditions of increased market volatility, as well as can serve as a basis for the development and implementation of preventive measures aimed at reducing the negative impact of inflation risks.

Who are the receivers and transmitters of volatility spillovers: oil, clean energy, and green and non-green cryptocurrency markets.

In the context of clean energy and green cryptocurrency development, the relationship between energy and cryptocurrency markets deserves further exploration. This study employs a quantile time-frequency connectedness approach to measure the dynamic connectedness and volatility propagation mechanisms between oil, clean energy, green cryptocurrency (GC), and non-green cryptocurrency (NGC) markets. Our findings suggest that, at median and low volatility levels, the oil and clean energy markets act as net receivers, taking on volatility spillovers from cryptocurrency markets. However, at high volatility levels, oil and clean energy markets transform into net transmitters. Most NGCs are volatility transmitters, while most GCs are volatility receivers in the median and extremely high volatility cases. We also observe that the total connectedness index (TCI) is heterogeneous over time and dependent on economic events. At median and low volatility levels, the short-run TCI makes the primary contribution. On the other hand, for high volatility levels, where short-term TCI does not have an absolute advantage, long-term TCI plays a greater role in many periods. Additionally, there is asymmetry in the TCI (including long-term and short-term TCI) at the quantile level. In the median and extreme scenarios, the COVID-19 has caused different levels of shock on oil, clean energy, GC, and NGC markets connectedness.

Complexity of the effects of pre-fermentation oxygenation, skin contact and use of pectolytic enzymes in white winemaking as revealed by comprehensive proteomics and volatilomics analysis

Ion exchange chromatography and SDS-PAGE followed by identification by MALDI-TOF/TOF, and two-dimensional gas chromatography with time-of-flight mass spectrometry (GC × GC-TOF-MS) were used for comprehensive proteomics and volatilomics evaluation of the effects of pre-fermentative oxygenation, skin contact and use of pectolytic enzymes in production of Malvazija istarska white wine, respectively. Many protein species and an unprecedented number of volatiles have been identified and (semi)quantified, revealing high complexity of the observed effects. Compared to a standard control wine, oxygenation treatment modulated the protein composition and resulted with a volatilome characterized by decreased levels of several important volatiles. Skin contact treatments, especially in combination with pectolytic enzymes, significantly increased the levels of a large number of proteins, but were also deprived of particular protein species found in other wines. Wines obtained by skin contact with exogenous enzymes exhibited the most complex volatile composition with increased levels of many key monoterpenoids, alcohols and esters.

Research on Forecasting the Yield of China's Commodity Futures Market Based on the Intraday Momentum Effect

By exploring the intraday momentum effect of the Chinese commodity futures market, it was found that there is a significant intraday momentum effect in the Chinese commodity futures market. The first half hour return of night opening and the first half hour return of day opening can significantly predict the first half hour return of closing within and outside the sample. Furthermore, it was found that the momentum effect of commodity futures is more significant at high trading volume and high volatility levels. The momentum strategy constructed based on the intraday momentum effect can significantly outperform the benchmark strategy, providing investors with a robust investment strategy.