Level Of Volatility Research Articles

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.

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.

Volatility and Transmission of Palm Oil Price in The Province of North Sumatera and Bengkulu Province

This study aims to analyze the level of volatility and transmission of palm oil prices in the Rotterdam and Belawan markets as well as the price of palm FFB in the markets of North Sumatera and Bengkulu Provinces. This study uses time series data for 120 months from January 2012 to December 2021. In this study, the data analysis method used to analyze the level of price volatility is Autoregressive Conditional Heteroscedasticity-Generalized Autoregressive Conditional Heteroscedasticity (ARCH-GARCH) and to analyze the level of price transmission is Autoregressive Distributed Lag (ARDL). The results showed that 1) the price volatility level in the Rotterdam and Belawan palm oil markets was high, 2) palm oil price transmission which is perfectly transmitted both in the short term and in the long term only occurs between the Rotterdam palm oil market and the Belawan palm oil market.

Political affiliation, cash flow volatility, and debt maturity in China

Using a large dataset with over half a million observations for 161 thousand Chinese firms, we examine the role of political affiliation on the debt maturity of firms experiencing varying levels of financial volatility. Our findings indicate that politically affiliated firms have reduced capacity to adjust their debt maturity when firm-specific risk rises. Furthermore, we demonstrate that this effect is stronger for firms affiliated with local levels of government relative to central levels. We argue that political affiliation can play a disciplining role in environments where credit is controlled by the state and politicians’ careers depend on local economic performance.

Volatility-dependent probability weighting and the dynamics of the pricing kernel puzzle

In order to estimate volatility-dependent probability weighting functions, we obtain risk neutral and physical densities from the Pan (J Financ Econ 63(1):3–50, 2002. https://doi.org/10.1016/S0304-405X(01)00088-5) stochastic volatility and jumps model. Across volatility levels, we find pronounced inverse S-shapes, i.e. small probabilities are overweighted, and probability weighting almost monotonically increases in volatility, indicating higher skewness preferences and crash aversion in volatile market environments. Moreover, by estimating probabilistic risk attitudes, equivalent to the share of risk aversion related to probability weighting, we shed further light on the pricing kernel puzzle. While pricing kernels estimated from the Pan (J Financ Econ 63(1):3–50, 2002. https://doi.org/10.1016/S0304-405X(01)00088-5) model display the typical U-shape as documented in the literature, pricing kernels—net of probability weighting—are strictly monotonically decreasing and thus in line with economic theory. Equivalently, we find risk aversion to be positive across wealth levels. Our results are robust to alternative maturities, wealth percentiles, alternative functional forms, a nonparametric empirical setting and variations of the Pan (J Financ Econ 63(1):3–50, 2002. https://doi.org/10.1016/S0304-405X(01)00088-5) coefficient estimates.

Quantile dependencies between exchange rate volatility and sectoral stock returns in South Africa

ABSTRACT This study assesses the effect of USD-Rand exchange rate volatility on South African sectoral stock returns between 29 March 1996 and 28 July 2022. Using the quantile-on-quantile regression (QQR) technique, we uncover that, although each of the sectors exhibit important idiosyncrasies, there exists a single overarching relationship between exchange rate volatility and stock returns that persists across all sectors. We find that low levels of volatility in the exchange rate have a negative impact on stock returns. Conversely, high levels of exchange rate volatility have a positive impact on stock returns. The most important implication of the findings of this study is that investors should have a keen awareness of movements in both the exchange rate and stock markets.

The volatilome signatures of Plasmodium falciparum parasites during the intraerythrocytic development cycle in vitro under exposure to artemisinin drug

Volatile organic compounds (VOCs) comprise a diverse range of metabolites with high vapour pressure and low boiling points. Although they have received attention, they are a largely unexplored part of the metabolome. Previous studies have shown that malaria infections produce characteristic, definitive, and detectable volatile signatures. Many transcriptional and metabolic differences are observed at different stages of the parasite Intraerythrocytic Developmental Cycle (IDC) as well as when artemisinin-resistant parasites are put under drug pressure. This prompted our research to characterize whether these responses are reflected at a volatile level in malaria during the IDC stages using gas chromatography-mass spectrometry. We investigated whether the resistant P. falciparum parasites would produce their own characteristic volatilome profile compared to near-isogenic wild-type parasite in vitro; firstly at three different stages of the IDC and secondly in the presence or absence of artemisinin drug treatment. Finally, we explored the VOC profiles from two media environments (Human serum and Albumax) of recently lab-adapted field parasite isolates, from Southeast Asia and West/East Africa, compared to long-term lab-adapted parasites. Recognizable differences were observed between IDC stages, with schizonts having the largest difference between wild type and resistant parasites, and with cyclohexanol and 2,5,5-trimethylheptane only present for resistant schizonts. Artemisinin treatment had little effect on the resistant parasite VOC profile, whilst for the wild type parasites compounds ethylbenzene and nonanal were greatly affected. Lastly, differing culturing conditions had an observable impact on parasite VOC profile and clustering patterns of parasites were specific to geographic origin. The results presented here provide the foundation for future studies on VOC based characterization of P. falciparum strains differing in abilities to tolerate artemisinin.

Effect of pH, Reducing Sugars, and Protein on Roasted Sunflower Seed Aroma Volatiles.

Sunflower seeds are a popular snack in many countries, such as the United States, China, and Spain. Sunflower seeds are typically roasted to create desirable aromas before being eaten. The desirable aromas are created by the Maillard and lipid oxidation reactions. Increasing the volatiles created by these reactions can create a more desirable product, increasing consumer acceptance of sunflower seeds. Seeds were soaked in solutions at pH 4, 7, and 9 and with added glucose, fructose, whey protein isolate, or whey protein concentrate before roasting. The resulting seeds were evaluated by selected-ion flow tube mass spectrometry to determine the volatile concentrations and by an untrained panel of consumers to determine acceptability. Increasing the pH increased the pyrazines but did not affect other volatiles. Adding reducing sugars or whey protein increased most volatiles. The fructose increased dimethylpyrazines, 2-methylpyrazine, and trimethylpyrazine concentrations more than glucose. However, the glucose increased furfural concentration more than fructose. The whey protein concentrate increased volatile levels more than any other treatment. The total Maillard volatiles and Browning index were increased by the same treatments. Sensory indicated that fructose increased desirable aroma the most, followed by whey protein treatments, and both were liked more than the pH 7 control. Optimizing roasting conditions by increasing the pH and reducing sugar and protein content can favor the Maillard reaction conditions, increasing the positive aromas associated with roasted sunflower seeds.

Presidential Debates and Electoral Preferences in Weakly Institutionalised Democracies: Evidence From 32 Latin American Elections

Previous research has shown that presidential debates have “minimal effects” on aggregate electoral preferences because they mainly reinforce people's pre-existing political preferences. However, most of what we know about the behavioural effects of debates comes from research conducted in the United States and other institutionalised democracies. We re-evaluate the effects of debates on electoral preferences by focusing on Latin American elections. Given higher levels of electoral volatility, weaker partisan brands, lower partisanship, and more personalised voter linkages, we expect that debates play a significant role in shaping vote choice in Latin America. We test these expectations by conducting an analysis of presidential debates on aggregated vote preferences in thirty-two elections across fourteen Latin American countries from 2002 to 2019. Our results show that presidential debates shape electoral preferences in countries with weakly institutionalised party systems.

Risk characteristics and connectedness in cryptocurrency markets: New evidence from a non-linear framework

Existing research pays less attention to the risk characteristics and connectedness of higher moments of cryptocurrencies. We dynamically analyze the risk characteristics of cryptocurrencies and their connectedness at four levels: return, volatility, skewness, and kurtosis. First, there are price bubbles in five popular cryptocurrencies, with long bubble periods during the COVID-19 epidemic. The volatility, skewness and kurtosis of cryptocurrencies are characterized by high persistence, with the exception of BNB. Second, the connectedness between the five cryptocurrencies is significant at higher moment conditions. The results for time-varying connectedness show that the total spillover of volatility, skewness and kurtosis varies over a wider range than returns, and that the total spillover of returns, volatility and skewness peaks during COVID-19. According to the pairwise results, there is strong connectedness between Ether and Bitcoin at the level of returns, volatility, skewness, and kurtosis, which is more significant at higher moments. Third, the role of cryptocurrencies changes not only over time, but also over order moments. The cryptocurrency market experienced significant volatility during 2018 and the first half of 2019, with Bitcoin being the most significant net exporter of returns and volatility spillovers. Cardano is a net exporter of both skewness and kurtosis spillover, and it is highly persistent with respect to volatility, skewness, and kurtosis. Overall, the leader in risk spillover at all order moments is Ether, and the net receiver is BNB.

Global Component of Sentiment in Futures Markets: Evidence from Covid-19 Pandemic

We examine the impact of the global component of sentiment on the price return and volatility of 25 major futures market indices across the globe, during the Covid-19 pandemic. The global component of sentiment causes investor overreactions. These overreactions accelerate the fall in prices and contribute to the rising volatility levels. The futures prices revert, though gradually, to their fundamental values as information from more reliable sources becomes available. This leads to price recovery and lower volatility levels.

A Dynamic Spatiotemporal Stochastic Volatility Model with an Application to Environmental Risks

A dynamic spatiotemporal stochastic volatility (SV) model is introduced, incorporating explicit terms accounting for spatial, temporal, and spatiotemporal spillover effects. Alongside these features, the model encompasses time-invariant site-specific factors, allowing for differentiation in volatility levels across locations. The statistical properties of an outcome variable within this model framework are examined, revealing the induction of spatial dependence in the outcome variable. Additionally, a Bayesian estimation procedure employing the Markov Chain Monte Carlo (MCMC) approach, complemented by a suitable data transformation, is presented. Simulation experiments are conducted to assess the performance of the proposed Bayesian estimator. Subsequently, the model is applied in the domain of environmental risk modeling, addressing the scarcity of empirical studies in this field. The significance of climate variation studies is emphasized, illustrated by an analysis of local air quality in Northern Italy during 2021, which underscores pronounced spatial and temporal clusters and increased uncertainties/risks during the winter season compared to the summer season.

Global financial cycle: Impact on Ukraine

Global financial fluctuations that arise in the financial centres – the US and the EU through international channels of monetary transmission affect the financial markets of peripheral countries, including Ukraine, where the assessment and consideration of such an impact is an important element of monetary policy. Therefore, the purpose of the article was to assess the impact of cyclical fluctuations of the global financial situation on the financial sector of Ukraine’s economy. As a result of correlation analysis, a direct relationship between leverage indicators in Ukraine and the Chicago Board Options Exchange Volatility Index and Euro Stoxx Volatility Index has been revealed. At the same time, the direct relationship between these indices and the growth of domestic credit in Ukraine contradicts the concept of the global financial cycle and may indicate the presence of more important factors of domestic credit. Correlation analysis of the volatility index of the Chicago Board Options Exchange and capital flows in Ukraine showed that in the years of increased global uncertainty and volatility, the volume of capital flows decreased, especially for portfolio debt instruments. Analysis of the cyclical components of the global and domestic credit cycle in Ukraine obtained with the Godric-Prescott filter indicates a higher level of volatility in domestic lending. The obtained value of the concordance coefficient did not make it possible to draw an unequivocal conclusion about the counter- or procyclical nature of domestic credit fluctuations relative to the global volume of loans in the non-banking sector. It has been found that the global financial cycle was not a significant factor in domestic credit in Ukraine in the studied period. The practical significance of the results lies in the fact that the National Bank of Ukraine will be able to implement a monetary policy independent of the global financial fluctuations and limit the outflow of capital during the period of growing uncertainty in the international financial markets

Accounting data, overvaluation, and the cross-section of volatility: industry sector evidence

Purpose This study aims to investigate the variation in overvaluation proxies and volatility across industry sectors and time. Design/methodology/approach Using industry sector data from the S&P Capital IQ database, this study applies traditional cross-sectional regressions to investigate the relationship between overvaluation and volatility over the 2001–2020 time period. Findings This study finds that the most volatile industry sectors generally do not coincide with overvalued industry sectors in the cross-section, implying that there are limitations to price-multiple methods for forecasting future volatility. Rather, this study finds that historical volatility significantly increases the goodness-of-fit when modeling volatility in the cross section of industry sectors. The findings of this study imply that firms should increase disclosures and transparency about corporate practices to decrease downside risk that stems from bad news. In addition, the findings underline the consistency between market efficiency and high levels of volatility in periods of significant uncertainty. Originality/value This study proposes a novel approach to examining the cross section of volatility across time for industry sectors.