Volatile Patterns Research Articles

The Volatility of Sugar and Ethanol Prices: A Tale of Two Markets Using Wavelet Analysis

This study ventures into the dynamic and interconnected realms of sugar and ethanol prices, unveiling their volatility patterns using the powerful tool of wavelet analysis. The first part of the study establishes a link between Brazil's sugar price surge and significant economic events, including the National Alcohol Program (Proálcool), the military dictatorship, and the implementation of the Real Plan. The second part zooms out to global factors, such as concerns about the greenhouse effect, flex-fuel engine technology, and recent challenges like droughts and COVID-19. The analysis uncovers distinct patterns in the volatility of sugar and ethanol prices. Factors like global supply and demand, climate-induced crop variations, and government policies influence sugar prices. In contrast, ethanol prices are more directly influenced by energy market dynamics, particularly crude oil prices, due to its role as a biofuel component. Furthermore, the study reveals time-frequency correlations between these two markets, shedding light on moments of synchronization and divergence. In this narrative of the two markets, wavelet analysis emerges as a powerful tool for deciphering past volatility and predicting future trends. It allows both estimating the degree of correlation and verifying the unfolding of these relationships over time. The findings of this study contribute as a bridge between theory and practice for decision-making, such as resource allocation and risk management in a constantly evolving economic scenario, highlighting the importance of analysis in this context.

Profiles of Killer Systems and Volatile Organic Compounds of Rowanberry and Rosehip-Inhabiting Yeasts Substantiate Implications for Biocontrol.

Yeasts produce numerous antimicrobial agents such as killer toxins, volatile organic compounds (VOCs), and other secondary metabolites, establishing themselves in developing natural and sustainable biocontrol strategies for agriculture and food preservation. This study addressed the biocontrol potential of yeasts, isolated from spontaneous fermentations of rosehips (Rosa canina L.) and rowanberries (Sorbus aucuparia L.), focusing on their killer phenotypes and VOCs production. Yeasts were isolated using spontaneous fermentations with Hanseniaspora uvarum and Metschnikowia pulcherrima identified as the dominant species, comprising approximately 70% of the yeast population. Among 163 isolated strains, 20% demonstrated killing activity, with Saccharomyces cerevisiae exhibiting the strongest killing efficiency, as well as Pichia anomala and M. pulcherrima showing broad-spectrum antagonistic activity. This study identified dsRNA-encoded killer phenotypes in S. cerevisiae, S. paradoxus, and Torulaspora delbrueckii, revealing multiple distinct killer toxin types. The biocontrol potential of wild berry-inhabiting yeasts was demonstrated in a real food system, grape juice, where the S. cerevisiae K2-type killer strain significantly reduced fungal contaminants. The selected H. uvarum, M. pulcherrima, S. cerevisiae, and S. paradoxus yeast strains representing both berries were applied for VOC analysis and identification by gas chromatography-linked mass spectrometry. It was revealed that the patterns of emitted volatiles are yeast species-specific. Statistically significant differences between the individual VOCs were observed among killing phenotype-possessing vs. non-killer S. paradoxus yeasts, thus revealing the involvement of killer systems in multi-level biocontrol enablement. The performed studies deepen our understanding of potential yeast biocontrol mechanisms, highlight the importance of produced antimicrobials and volatiles in ensuring antagonistic efficacy, and prove the relevance of isolated biocontrol yeasts for improving food safety.

Characterization and Prediction of the Ghana Stock Exchange Composite Index Utilizing Bayesian Stochastic Volatility Models

This study delves into the dynamics of the Ghana Stock Exchange Composite Index (GSE-CI) over the period from 2011 to 2022, a symbolic emerging market index that presents unique challenges and opportunities for financial analysis. We characterize the GSE-CI using advanced analytical tools such as the Hurst exponent and R/S analysis to uncover its fractal properties and complex dynamics. The paper then advances to predictive modeling, employing an innovative approach with four variations of Stochastic Volatility (SV) models: SV with linear regressors, SV with Student’s t errors, SV with leverage effects, and a hybrid model combining Student’s t errors with leverage. Each model offers a unique perspective on forecasting the behavior of the GSE-CI, with the SV model incorporating Student’s t errors emerging as the most effective, as evidenced by the lowest Root Mean Square Error (RMSE) in our comparative evaluation. The integration of these models highlights their robustness in capturing the intricate volatility patterns of the GSE-CI, making a compelling case for their applicability to similar financial markets in other emerging economies. This research also paves the way for future investigations into other market indices and assets within and beyond the borders of emerging markets.

PEMODELAN DAN PREDIKSI CURAH HUJAN MENGGUNAKAN IMBAL HASIL HETEROSKEDASTIS DI KABUPATEN BANDUNG

Bandung Regency located in West Java Province, Indonesia, has a tropical climate with high rainfall variation. This variable rainfall pattern is one of the main characteristics of tropical regions, where seasonal changes such as the rainy and dry seasons significantly impact annual rainfall patterns. In addition, high rainfall is often a trigger for natural disasters, such as floods and landslides, which can cause economic and social losses. Therefore, the ability to predict rainfall accurately is crucial, both for disaster risk mitigation and as a basis for decision-making in various sectors. This study aims to predict rainfall in Bandung Regency for the next year using a time series model approach, specifically a heteroscedastic model. The main focus of the study is to examine rainfall returns, or changes in rainfall over time while considering the nature of heteroscedasticity. In this condition, the variance of the data is not constant over time. The phenomenon of heteroscedasticity is common in rainfall data due to significant changes between seasons or extreme weather events. The study results indicate that the best model for predicting rainfall imbal hasil s is the Autoregressive Conditional Heteroskedasticity (ARCH) model. The selection of this model is based on the Akaike Information Criterion (AIC), which shows that the ARCH model outperforms the GARCH(1,1) model in handling variance instability in rainfall data. The advantage of the ARCH model lies in its ability to dynamically model variance instability, resulting in more accurate predictions. This model is not only capable of capturing high volatility patterns but also provides a more realistic estimate of large fluctuations that could occur in the future.

Improving realised volatility forecast for emerging markets

AbstractAccurate forecasting of realised volatility is essential for financial risk management and investment decision-making in emerging markets, taking the South African financial market as a benchmark. This study examines the predictive performance of four prominent models: HAR (Heterogeneous AutoRegressive), realised GARCH (Generalized AutoRegressive Conditional Heteroscedasticity), Recurrent Conditional Heteroskedasticity (RECH), and the Rough Fractional Stochastic Volatility (RFSV) models. These models are specifically tailored to capture the complex dynamics and long-range dependence observed in financial time series. We illustrate the challenges and limitations of these models outside the context of established markets. Our empirical findings reveal unique strengths for each model. The HAR model excels in capturing long-term volatility patterns, while realised GARCH models effectively capture volatility clustering and persistence. RECH model showcases their ability to forecast Value-at-Risk, while the RFSV model successfully captures irregular and long-memory characteristics. We provide empirical evidence that the South African financial market is rough. Moreover, this study provides valuable insights into forecasting realised volatility in the South African market, and the findings can assist practitioners and investors in making informed decisions and developing robust risk management strategies.

BACKCASTING BITCOIN VOLATILITY: ARCH AND GARCH APPROACHES

Purpose- The primary purpose of this study is to model Bitcoin price volatility and forecast its future price returns using advanced econometric models such as ARCH and GARCH. The study aims to enhance risk management strategies and support informed investment decisions by addressing the time-varying nature of Bitcoin’s volatility. The research explores the persistence of volatility shocks and the clustering of price movements to provide insights into market dynamics. Methodology- This research examines daily Bitcoin closing prices over the period from January 2020 to October 2024. The data was preprocessed to ensure reliability, including applying logarithmic transformations to standardize the data and eliminate trends. Stationarity tests, such as the Augmented Dickey-Fuller (ADF), Phillips-Perron (PP), and KPSS tests, were conducted to confirm the series' stationarity. The ARCH-LM test was utilized to detect volatility clustering which is essential for validating the use of ARCH and GARCH models. Following this, ARIMA models were employed to define mean equations and GARCH models were used to estimate conditional variance and capture volatility dynamics. The dataset was split into training and validation subsets with data from July to October 2024 reserved for validation. Findings- The findings demonstrate that Bitcoin’s price movements exhibit significant volatility clustering and persistence of shocks which are key characteristics effectively captured by ARCH and GARCH models. These models provide valuable insights into the volatility patterns of Bitcoin, supporting their application in cryptocurrency analysis. Despite their robustness, the models face limitations in precise return forecasting during highly volatile periods, suggesting the need for further refinement or integration with advanced approaches. Conclusion- The research concludes that ARCH and GARCH models are effective tools for understanding and forecasting Bitcoin’s volatility. The study underscores the importance of acknowledging volatility persistence and clustering effects when analyzing cryptocurrency price behavior. However, it also highlights areas for improvement in econometric modelling by including the exploration of hybrid models and the integration of macroeconomic factors to enhance forecasting accuracy. Keywords: Bitcoin, ARCH models, GARCH Models, forecasting, ARIMA models JEL Codes: C58, G10, G12

Predicting Bank Nifty Movements Based on ICICI, HDFC, and Axis Bank Returns

This study investigates the link between the returns of ICICI Bank, HDFC Bank, and Axis Bank and the overall performance of the Bank Nifty index, which is a crucial measure of the Indian banking sector's health on the National Stock Exchange. Using five years of historical data, regression analysis and descriptive statistics evaluate trends, correlations, and prediction accuracy in anticipating Bank Nifty returns based on the performance of these large banks. The study finds that while Bank Nifty displays stability as it aggregates multiple bank stocks, individual banks like Axis Bank and HDFC exhibit distinct volatility patterns, with Axis Bank showing significant fluctuations due to firm-specific and external factors.Regression results show a little association between individual bank returns and the Bank Nifty, indicating that the index has limited predictive value for single equities. Nonetheless, knowing these patterns may help investors manage risk, anticipate market changes, and develop portfolio diversification methods. The findings emphasize the relevance of sector-specific patterns, implying that although Bank Nifty captures broad market movements, other elements have a distinct influence on each bank's performance, providing insights for more sophisticated investment and risk management methods.

Dynamic Linkages Between U.S and Indian Equity Markets: An Empirical Study

In this study, we investigate the dynamic linkages between US and Indian equity markets by examining the daily Total Return Index values of NIFTY 50 and NASDAQ Composite over a decade ending October 2024. While the indices are not found to be cointegrated, suggesting potential long-term diversification benefits, they exhibit significant short-term correlations. Through various statistical analyses including regression, correlation, and variance tests, we find strong positive associations between the markets, with NASDAQ Composite movements explaining approximately 89% of variations in NIFTY 50 Total Return Index (TRI). The study reveals distinct volatility patterns, with the Indian market showing higher price fluctuations but stronger absolute growth compared to its US counterpart. Despite the strong correlation, both markets maintain unique characteristics influenced by their respective economic fundamentals. The findings demonstrate the evolving nature of market integration between developed and emerging economies, offering valuable insights for international portfolio diversification strategies and risk management in an increasingly interconnected global financial system. Keywords: Market Integration, Volatility Spillover, Portfolio Diversification, Stock Market Returns, Emerging Markets, Cross-market Correlation

Predicting Portfolios Using Fuzzy Logic and Optimization Techniques: A Case Study of BSE-IT Listed Companies

Stock price trends were considered in this study, the companies in the BSE-IT index, with a view to optimizing portfolio allocation based on risk tolerance. The GARCH model has been applied here to allow an investigation into the volatility of the stock returns and to set up different clusters using K-Means clustering that group together companies owning the same attributes related to volatility for strategic classification. Data collection and processing occur between April 1, 2014, and March 31, 2024, for reasons of reliability and relevance. Descriptive statistics report on the variance of stock performance across companies; different risk-return profiles are thus exposed. The GARCH model measures each stock's long-term variance, along with sensitivity to past shocks, and overall volatility persistence, making this an excellent inventory for risk assessment. K-Means clustering groups companies based on similar volatility patterns, thereby informing portfolio construction. Findings present how clustering with econometric modeling may assist in the production of optimal portfolios that depend on specified risk capacities, thus efficiently managing and implementing an IT investment strategy.

Asymmetric Effects and Volatility Clustering in NSE NIFTY 50: A Comparative Analysis of GARCH Models

This study aims to analyze the volatility patterns, Clustering, and asymmetric effects in the NSE NIFTY 50 index. It involves using daily returns data from the NSE NIFTY 50 from 01 Jan 2010 to 31 Dec 2023. Daily closing prices are obtained from the official NSE website, and returns are calculated based on these prices. EGARCH (1, 1), TARCH (1, 1), GARCH (1, 1), GARCH-M (1,1), and models are utilized to predict volatility, capturing volatility clustering and leverage effects. Using both the Akaike and Schwarz criteria, EGARCH (1,1) was demonstrated to be the best model. The findings reveal that Fluctuations in the Indian stock market, particularly shown in the NIFTY index of the NSE were the highest in the year 2020 due to the COVID-19 pandemic. Past NIFTY returns demonstrate a GARCH effect on today’s NIFTY returns. The results of the research suggest that the impact of negative and positive shocks on the stock returns series varies. Volatility is largely caused by negative news compared to positive news. It is suggested that the model exhibits a leverage effect and that different types of information produce various types of shock to the volatility of the Nifty index return. The findings show that investors of NSE, particularly those involved with the NIFTY50, face heightened volatility and increased risk, especially during periods of significant events.

Development of New Electricity System Marginal Price Forecasting Models Using Statistical and Artificial Intelligence Methods

The System Marginal Price (SMP) is the cost of the last unit of electricity supplied to the grid, reflecting the supply–demand equilibrium and serving as a key indicator of market conditions. Accurate SMP forecasting is essential for ensuring market stability and economic efficiency. This study addresses the challenges of SMP prediction in Turkey by proposing a comprehensive forecasting framework that integrates machine learning, deep learning, and statistical models. Advanced feature selection techniques, such as Minimum Redundancy Maximum Relevance (mRMR) and Maximum Likelihood Feature Selector (MLFS), are employed to refine model inputs. The framework incorporates time series methods like Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Convolutional LSTM (ConvLSTM) to capture complex temporal patterns, alongside models such as Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Extreme Learning Machine (ELM) for modeling non-linear relationships. Model performance was evaluated using the Mean Absolute Percentage Error (MAPE) across regular weekdays, weekends, and public holidays. XGBoost combined with MLFS consistently achieved the lowest MAPE values, demonstrating exceptional accuracy and robustness. Among all of the models, XGBoost combined with MLFS consistently achieved the lowest MAPE values, demonstrating superior accuracy and robustness. The results highlight the inadequacy of traditional models like ARIMA and SARIMA in capturing non-linear and highly volatile patterns, reinforcing the necessity of using advanced techniques for effective SMP forecasting. Overall, this study presents a novel and comprehensive approach tailored for complex electricity markets, significantly enhancing predictive reliability by incorporating economic indicators and sophisticated feature selection methods.

High-frequency realized stochastic volatility model

High-frequency realized stochastic volatility model

Beyond the Event Window: Earnings Horizon and the Informativeness of Earnings Announcements

ABSTRACT The impact of earnings announcements (EAs) on investor uncertainty depends not only on how much new information they contain but also on how long it would take comparable information to arrive in the future through alternative sources, which I term “earnings horizon.” Using a structural model of periodic EAs, I show that earnings horizon is not captured by standard empirical measures of earnings informativeness or timeliness based on the event-study approach. However, earnings horizon can be estimated using patterns in return volatility over firms’ reporting cycles, which indicate that EAs have a short horizon and thus reduce investor uncertainty by one-third the amount suggested by event studies. Moreover, these patterns indicate that it takes investors considerably longer than the three- to five-day windows commonly applied in event studies to fully process EAs and that more frequent financial reporting may significantly enhance EAs’ informativeness. Data Availability: Data are available from the public sources cited in the text. JEL Classifications: C58; D82; D84; G10; G12; G14; G30.

Nexus Between Stock Return and Market Volatility in Indian Perspective

This research seeks to explore the volatility patterns for the SENSEX index to figure out the characteristics of volatility in Indian stock trading and to investigate the relationship between returns and volatility of the Indian market for the last ten years. The research adds to the existing knowledge about stock market fluctuations, their implications for investors, and their impact on developing the country's economy. The study involves using daily returns data from BSE SENSEX from 01 Jan 2014 to 31 Dec 2023. Daily closing prices are obtained from the official website of BSE, and returns are calculated based on these prices. The Dickey-Fuller and Phillips-perron are taken to make the time series static. ARCH, GARCH, and GARCH-M tests are utilized to capture volatility clustering, return and volatility relationship. The results reveal that Fluctuations in the Indian stock market, particularly shown in the SENSEX index of the BSE, were highest in the year 2020. Findings suggest that GARCH-M does not show any relation between expected returns and market volatility. The risk-premium parameter is positive but statistically insignificant. If you want to hedge against risk, the risk premium is not very high. It means that taking risks does not give you more returns. Durbin Watson's value of 1.981401 further supports the model's suitability.Copyright© 2024 The Author(s). This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.

Stock Volatility Modelling With Augmented Garch Model For Voltas Ltd

This research paper seeks to examine the efficiency of the augmented GARCH models in estimating the stock volatility. GARCH models that were introduced by Bollerslev (1986) became popular for the modelling of time varying volatility in the financial markets. However, due to the specificity of financial markets and variation of factors that define it, extra parameters may be required to improve the model. To investigate further, the study uses an extended GARCH model that integrates other features including leverage effects, exogenous variables and non-linearities. These components seek to establish features giving clearer volatility patterns of stock markets. In this paper, empirical comparison of the extended GARCH model with the basic forms of the models is done. The outcomes are expected to be of interest to financial investors, advisors and risk managers

Mineral policy and sustainable development goals: Volatility forecasting in the Global South's minerals market

Mineral policy and sustainable development goals: Volatility forecasting in the Global South's minerals market

Financial Interdependencies: Analyzing the Volatility Linkages between Real Estate Investment Trusts, Sukuk, and Oil in GCC Countries

This study investigates the financial interconnections among Real Estate Investment Trusts (REITs), sukuk (Islamic bonds), and oil in Gulf Cooperation Council (GCC) nations. The study sample comprises S&P GCC Composite Equity Real Estate Investment Trusts (REITs) Shariah, the S&P GCC Bond and Sukuk Index, and the OPEC crude oil basket on a daily basis. The duration of coverage spans from 2014 until the beginning of 2024. The TVP-VAR methodology is utilized to examine the interrelationship among the assets. The results indicate that Real Estate Investment Trusts (REITs) and oil are sources of volatility transmission, whereas sukuk is a recipient of volatility within the network. Examining the net pairwise directional linkages of two assets, namely REITs and oil markets, reveals that they transfer their volatility to the sukuk market. Moreover, a reciprocal relationship exists between REITs and oil regarding volatility spillover. It means that REITs act as transmitters to the oil markets during specific periods, while the influence is reversed at other times. This study implies that portfolio managers and investors can discern the volatility patterns of assets in order to enhance their risk-management techniques. For policymakers, comprehending the interdependence of certain asset classes provides valuable knowledge for formulating regulations that might stabilize the financial system and foster economic growth. From a research and academic perspective, this study enhances understanding of the interconnections between different financial asset classes and pricing dynamics in financial markets.

The Effect of Exchange Rate Volatility on Foreign Tourist Visits in Indonesia: GARCH Analysis

In this era of globalization, currency exchange rates are one of the crucial factors affecting a country's economy. The exchange rate of a country's currency plays an important role in economic stability and international trade, especially in the tourism sector. This study aims to conduct an in-depth analysis to identify patterns of rupiah exchange rate volatility using the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model, and then examine whether these patterns have a significant correlation with the number of foreign tourist arrivals in Indonesia. In analyzing the data, researchers conducted several classical assumption tests, namely the normality test and the heteroscedasticity test. The test results show that the data used, namely the rupiah exchange rate data and the number of foreign tourist visits, are not normally distributed and heteroscedastic. In this case, it resulted in researchers not use the Pearson correlation test so they used the Kendall-Tau correlation test and Granger Causality test which resulted in the two variables being correlated. Furthermore, GARCH modeling is carried out which provides forecasting on both data to increase in 2024. The results of this study are expected to provide a basis for consideration for stakeholders to determine policies, especially in the financial and tourism sectors.

Analysis The Effect Of Volatility On Potential Losses Mutual Fund Investments Using The ES-GARCH Method

Investing in mutual funds has become a popular choice for investor who looking to participate in the capital markets with more diversified risk. However, the success of mutual fund investments depends on investors understanding the potential losses and opportunities that may arise during the investment period. Analyzing the risk of mutual fund investments is fundamental in helping investors comprehend potential losses. Therefore, research is conducted to understand potential losses by estimating asset price volatility and determining the maximum possible losses. The Expected Shortfall (ES) method proves useful in measuring downside risk and extreme loss potential in investments, but it is less effective in addressing nonlinear trends and the complexity of volatility patterns. Hence, a combination of the Expected Shortfall (ES) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) methods is employed to measure the risk of mutual fund investments. The research findings indicate that volatility has a positive impact on Value at Risk (VaR), and the potential maximum losses (ES) increase with higher volatility, indicating a greater risk.

Influence of the malting conditions on the modification and variation in the physicochemical properties and volatile composition of barley (Hordeum vulgare L.), rye (Secale cereale L.), and quinoa (Chenopodium quinoa Willd.) malts

The traditional malted cereal used primarily for beverages is barley (Hordeum vulgare L.), while rye (Secale cereale L.) is mainly used in baked goods. In contrast, quinoa (Chenopodium quinoa Willd.) is a gluten-free pseudocereal, rich in starch and high-quality proteins, that can be used similarly to cereals. Their physicochemical properties and volatile compositions (e.g., aroma compounds) directly influence the finished food products. The sharp bitterness of unprocessed rye and the earthy aroma of native quinoa can interfere with the development and acceptance of food products. Malting is known to improve the processing and sensory properties of barley. A face-centered, central composite design was used to investigate the individual and interactive effects of three malting parameters (i.e., steep moisture (SM), germination temperature (T), and germination time (t)) on malt quality indicators (e.g., extract) and volatile formation (e.g., 3-methylbutanal) in rye and quinoa, and were compared to barley. The malt modification predictive models were then used to determine standard malting regimes for brewing quality malts. The malting parameters for the steeping and germination stages were: 43%, 15°C, and 6 d for barley; 45%, 12°C, and 8 d for rye; and 46%, 16°C, 6 d for quinoa. Malt modification and volatile formation were primarily associated with the interactive effect of germination temperature and time. Conversely, steep moisture had limited impact on malt modification but strongly regulated the formation of 34 known (pseudo)cereal volatile compounds. Principal component analysis (PCA) of the volatile data identified (pseudo)cereal specific volatile patterns. Aldehydes were characteristic in the cereal malts, particularly barley, whereas phenyl compounds and pyrazines were abundant in rye and quinoa malts, respectively. Controlling (pseudo)cereal modification and volatile development through the malting process could help deliver targeted sensory properties and improve the acceptance of malt-based food products.