Volatility Trends Research Articles

An innovative method for short-term forecasting of blockchain cryptocurrency price

Cryptocurrency market sentiment is relatively unstable, which makes cryptocurrency price an attribute of high volatility. Accurate forecasting methods help to clarify the volatility trend of the cryptocurrency price, thereby reducing the investment risk of participants in the cryptocurrency market. Therefore, this research proposed a new method for short-term forecasting of the cryptocurrency price based on a small sample. This study took three typical blockchain cryptocurrencies (Bitcoin, Ethereum, Litecoin) as experimental objects, chose data intervals with different volatility trends in the U.S. stock indices between 2022 and 2023 as sample data, and used grey correlation analysis to select core affecting variables. Furthermore, this study built a grey multivariate convolution model with prioritized accumulating novel information for conducting prediction experiments on blockchain cryptocurrency price. The research findings demonstrate that the proposed model achieves high prediction accuracy in all experiments, and the model accuracy is superior to the comparison models. This study proposes a scientific prediction approach for blockchain cryptocurrency price, which can guide financial investors in developing and analyzing quantitative financial trading strategies to a certain extent. Meanwhile, this study provides a specific reference for relevant government departments to strengthen cryptocurrency regulation, prevent financial risks, and maintain financial stability.

A Novel Deep Learning Model for Stock Prediction Integrating Historical Prices, Core Volatility Trends and Financial News

Accurate stock price forecasting is crucial for investors and analysts aiming to make informed decisions and mitigate risks in financial markets. While deep learning models have proven effective in identifying complex patterns in quantitative data, they often struggle to account for non-quantitative factors such as corporate events, market sentiment, and regulatory changes that can cause unpredictable stock price movements. To address this challenge, an ensemble model is introduced that integrates historical price data, financial news, and stock fundamentals to predict next-day stock prices. The model combines one-dimensional Convolutional Neural Networks (1D CNNs), Long Short-Term Memory networks (LSTMs), and Gated Recurrent Units (GRUs), applied independently to each data stream and concurrently as a unified ensemble, to capture both short-term and long-term market behaviors. NVIDIA (NVDA) stock was selected for testing due to its high volatility and rapid price fluctuations, presenting a challenging case for predictive modeling. Using data spanning from April 2014 to March 2024, the model achieved a coefficient of determination (R²) of 0.983 and a mean absolute error (MAE) of $12.72. These results suggest that the ensemble approach effectively captures both quantitative and qualitative factors, making it a promising tool for improving stock price prediction in volatile market conditions.

Research on the volatility forecasting model of KOSPI index returns using AR(M)-GARCH(P,Q) model

In this study, we estimated the volatility of the KOSPI index returns and analyzed volatility trends. The data used in the study consisted of monthly observations from January 2005 to December 2022, and the KOSPI index raw data was transformed into log returns. The volatility estimation model used the AR(m)-GARCH(p,q) model, which combines the autoregressive error model and the GARCH model that explains the persistence of volatility at low orders. The goodness of fit of the model was confirmed using the Portmanteau Q-test and LM-test. Applying the autoregressive error model revealed significant autocorrelation in the log returns of the KOSPI index at lags 3 and 6. Residual analysis indicated that the residuals followed white noise, but the squared residuals exhibited heteroscedasticity. Therefore, after fitting the autoregressive error model, we applied the GARCH model and conducted residual analysis, finding both the residuals and squared residuals significant at a 5% significance level. The volatility forecasting results indicated a continuous increase in volatility. The findings of this study are expected to provide important implications for policymakers responsible for risk management in the Korean stock market.

Kangaroo Market Phenomenon: Identification and Impact on Indonesia’s Economic Growth

The stock market holds a crucial role in a country's economy, including Indonesia. As an indicator, the Jakarta Composite Index (JCI) experiences volatility alongside changes in the Indonesian economy. The presence of a kangaroo market in JCI returns can pose a risk of future economic slowdown, making it crucial for monetary authorities to intervene to stabilize the stock market. Therefore, this study identifies the kangaroo market phenomenon, structural economic changes, and the impact of the kangaroo market phenomenon on Indonesia's economic growth. The study focuses on many variables, including JCI returns, kangaroo market, and economic growth from Q3 1995 to Q4 2023. By using the Hodrick-Prescott Filter to define a kangaroo market as JCI returns that lack a deterministic trend, have high volatility, and deviate by one standard deviation from their volatility trend, the results show that most kangaroo phenomena occur during undervaluation. Following the kangaroo market phenomenon, Indonesia's economy underwent five structural changes based on the multiple breakpoint test. Using dynamic linear regression, the overvaluation kangaroo phenomenon negatively affects Indonesia's economic growth, while the undervaluation kangaroo phenomenon has no impact but shows a positive direction.

Relationship between CEO Facial Expressions and Stock Price Volatility Trends

Relationship between CEO Facial Expressions and Stock Price Volatility Trends

Empirical analysis of crude oil dynamics using affine vs. non-affine jump-diffusion models

This paper investigates the dynamics of the United States oil (USO) exchange traded fund (ETF). Daily USO returns are modelled using stochastic volatility (SV) frameworks derived from three different model classes: SV models with contemporaneous jumps in returns and volatility (SVCJ); SV model with jumps in returns only (SVJ); and a pure SV model class without jumps. Six affine and non-affine models are considered within each model class that depend on specification of the drift and the diffusion terms in the variance process, resulting in a total of 18 models that are estimated using particle Markov Chain Monte Carlo (PMCMC) approach. Model evaluation is conducted using the Deviance Information Criterion (DIC), Bayes factors, probability plots, and deviation measures to assess the discrepancy between the estimated volatility and key benchmarks, the crude oil ETF volatility index (OVX) and the realised volatility (RV). Our analysis indicates that models incorporating jumps, particularly the SVCJ-PLY-0.5 and SVCJ-PLY-1.0, more accurately capture USO dynamics than standard SV models. The SVCJ-PLY-0.5 model ranks highest based on DIC statistics and Bayes factors, and both models excel in aligning their estimated volatility with the OVX and RV benchmarks. Overall, the statistical criteria employed in our comparison favour models with jumps over the standard SV model class, suggesting that models incorporating jumps in both return and variance processes (SVCJ) are superior to those with jumps solely in the return process (SVJ). The affine models SVJ-LIN-0.5 and SVCJ-LIN-0.5 with linear variance drift and square root diffusion that are particularly interesting for theoretical finance applications are highly ranked among considered frameworks, outperforming several non-affine alternatives. Our analysis of the regression model for volatility forecasting reveals a significant predictive accuracy in the evaluated models, demonstrating their effectiveness in anticipating future volatility trends.

Iron alloys of volatile elements in the deep Earth's interior.

Investigations into the compositional model of the Earth, particularly the atypical concentrations of volatile elements within the silicate portion of the early Earth, have attracted significant interest due to their pivotal role in elucidating the planet's evolution and dynamics. To understand the behavior of such volatile elements, an established 'volatility trend' has been used to explain the observed depletion of certain volatile elements. However, elements such as Se and Br remain notably over-depleted in the silicate Earth. Here we show the results from first-principles simulations that explore the potential for these elements to integrate into hcp-Fe through the formation of substitutional alloys, long presumed to be predominant constituents of the Earth's core. Based on our findings, the thermodynamic stability of these alloys suggests that these volatile elements might indeed be partially sequestered within the Earth's core. We suggest potential reservoirs for volatile elements within the deep Earth, augmenting our understanding of the deep Earth's composition.

Earth's “Missing” Chlorine May Be in the Core

AbstractThe budget of volatile halogens in the bulk silicate Earth, fluorine and chlorine, differs distinctly from that of chondritic meteorites. Arguably, the bulk silicate Earth shows a low abundance of Cl, while the F budget is in line with the expected volatility trend. One hypothesis for the missing Cl is its sequestration into Earth's core during planetary segregation, but experimental data on partitioning between silicate and metallic melts are limited in pressure and remain inconclusive. Here we use computational quantum mechanical methods to study F and Cl geochemistry during core–mantle differentiation over a wide pressure and temperature range. Our calculations reveal that with increasing pressure and temperature, chlorine shows an enhanced affinity for iron metal. The Cl metal–silicate partition coefficient increase from −1.89 ± 0.84 at 10 GPa and 3000 K to 1.62 ± 0.80 at 130 GPa and 5000 K, while corresponding calculations for F show minimal variations in metal–silicate partitioning across the pressure–temperature conditions investigated, yielding values between −3.61 ± 0.81 and −3.37 ± 0.80. The shift in shows a transition from lithophile to siderophile behavior. Further calculations on isotopic partitioning show negligible fractionation of Cl isotopes (37Cl/35Cl) between the mantle and core. Our results suggest that metallic iron within Earth's mantle and core may serve as an important Cl reservoir, potentially accounting for up to 40% of Earth's overall Cl inventory.

The Role of Corporate Governance in Firm’s Stock Performance

The Increasing trend of globalization and stock volatility attracts academic researchers to investigate the causes. As the economic globalization has radically increased, there is a need to know more about corporate governance. The shareholder's pattern of firms gives an understanding of corporate control and good governance. These aspects provide the researcher with an explanation of how shareholder behavior may influence the market volatility and valuation. To analyze the impact of the firm’s ownership structure on market performance of firm, the ownership structure is decomposed by the percentage of equity. Whereas market to book ratio and stock volatility for market performance. We use the sample of 192 companies, from the period of 2006 to 2018. The study used different suitable statistical technique such as “correlation, multiple regression analysis”. The statistical analysis indicates that foreign ownership significantly reduces the stock volatility and enhances the firm’s stock valuation compared to its book value. Director ownership is a cause of rising stock volatility and it could not play a significant role in enhancing firm’s stock valuation. This research will also provide the critical recommendation for academics and for economic growth.

A Study on KOSPI Return Volatility Estimation Using Heterscedastic Time Series Model

In this study, return volatility prediction model was estimated by converting composite stock index into return variables. As for the heteroscedasticity test for the return, the Q-test and the LM-test showed that heteroscedasticity existed at the 2nd and 7th lags. Therefore, the order was determined using the SBC statistic, the parameters were estimated using the ARCH (2) model, and the model fit test was conducted. The parameter estimates of the ARCH(2) model were statistically significant, but the residual analysis showed that autocorrelation existed and did not satisfy the normality test. In the results of applying the GARCH (1,1) model, the parameter estimates, residual analysis, and normality were found to be satisfactory. Therefore, the GARCH (1,1) model was determined as the KOSPI return volatility estimation model and volatility was predicted. Volatility was predicted to show high volatility in the period of January and February 2023, and to stay calm after March for a long-term. Continuing a calm state means that the probability of large volatility is high. Therefore, those in charge of government agencies need to check system improvement or policy establishment and make efforts to identify trends in volatility by market.

Stock Index Prediction Based on ARIMA Model

Stock index price forecasting is a very important thing for financial markets. Stock indices select the most representative stocks in the stock market, which are the most favorable representatives of the industry, sector or market. Successful prediction of them can guide investors to a good payoff and allow researchers to understand the workings of the market economy. Time series models are widely used in stock price forecasting precisely because they have the advantage of being able to predict in complex environments such as large shocks. Therefore, this paper introduces the process of using time series model - ARIMA model to establish the prediction of index price fluctuation. And the five-year-long stock index data from May 2017 to April 2022 of the CSI 300 index is used as the research sample to successfully predict the volatility trend of the CSI 300 index in May 2022. The outcomes demonstrate that the ARIMA model has excellent capability for short-term forecasting. and is capable of forecasting complex situations that cannot be accomplished by linear models.

The Impact of Financial Market Volatility on Investments in China and Southeast Asia

This paper aims to explore the impact of financial market volatility on China's investments in Southeast Asia. Through literature review and analysis, it delves into the mechanisms of the impact of financial market volatility, investor response strategies, influencing factors, and future prospects. Firstly, it analyzes the influence of financial market volatility on portfolio value and investor behavior. Secondly, it examines diverse investment strategies available to investors, the use of financial instruments, and the trade-off between long-term investment strategies and active trading strategies. Furthermore, it discusses the impact of various factors such as politics, economics, and technology on financial market volatility. Lastly, it forecasts future trends in financial market volatility, encompassing the growth of emerging markets, the influence of emerging technologies, and considerations of environmental and social risks. This study provides investors with strategies to cope with market volatility and risk management advice, while also pointing out directions for future research, including emerging technologies, political factors, and market sentiment and behavioral analysis. Despite limitations in data and time scope, this study offers valuable insights into understanding the effects of financial market volatility on investments.

The role of higher moments in predicting China's oil futures volatility: Evidence from machine learning models

This paper expands the emerging literature on volatility forecasting for China's oil market by exploring the predictive ability of higher-order moments (skewness, kurtosis, hyperskewness, and hyperkurtosis) based on high-frequency data. Our investigation is originally based on the heterogeneous autoregressive (HAR) framework, but considering the possible multicollinearity and nonlinearity, it is extended to various machine learning (ML) models and combination forecasting models. The results reveal that higher-order moments, including the two highest moments, always significantly improve predictive performance for the COVID-19 crisis. We further examine the interpretability of ML models and each factor's contribution to the prediction, finding that odd and even moments contain short- and long-term prediction information, respectively. This paper also highlights the effectiveness of ML models for capturing trends in oil futures volatility with higher-order moments and the satisfactory performance of combination forecasting models. Finally, we investigate the predictability of asymmetric risk patterns and obtain identical results. Our study has important implications for financial risk management, asset pricing, and portfolio allocation.

Retracted: High-Order Moment Contagion of the Carbon Market: A Heterogeneity Analysis of Market Volatility Trend

Retracted: High-Order Moment Contagion of the Carbon Market: A Heterogeneity Analysis of Market Volatility Trend

An Efficient GAN-Based Multi-classification Approach for Financial Time Series Volatility Trend Prediction

Deep learning has achieved tremendous success in various applications owing to its robust feature representations of complex high-dimensional nonlinear data. Financial time-series prediction is no exception. Hence, the volatility trend prediction in financial time series (FTS) has been an active topic for several decades. Inspired by generative adversarial networks (GAN), which have been studied extensively in image processing and have achieved excellent results, we present the ordinal regression GAN for financial volatility trends (ORGAN-FVT) method for the end-to-end multi-classification task of FTS. An improved generative model based on convolutional long short-term memory (ConvLSTM) and multilayer perceptron (MLP) is proposed to capture temporal features effectively and mine the data distribution of volatility trends (short, neutral, and long) from given FTS data. Meanwhile, ordinal regression is leveraged for the discriminator to improve the multi-classification performance, making the model more practical. Finally, we empirically compare ORGAN-FVT with several state-of-the-art approaches on three real-world stock datasets: MICROSOFT(MSFT), Tesla(TSLA), and The People’s Insurance Company of China(PAICC). ORGAN-FVT demonstrated significantly better AUC and F1 scores, at most 20.81% higher than its competitors.

Understanding income volatility in urban China

Higher income volatility is associated with a higher risk and a reduction in well-being. Income volatility is widely studied in many advanced economies, yet little is known about income volatility in China. In this paper we document trends in earnings volatility and household income volatility in urban China and examine what are the driving forces. Using the China Urban Household Survey, we find that male earnings volatility increased by 89% between 1992 and 2009. Earnings volatility is higher for older, less educated, and those who work in private service sector and non-SOEs. Decomposition analysis suggests that couple’s earnings shocks are positively correlated, while transfer income and other income help smooth out head earnings shocks. In addition, transfer income and other non-transfer income plays a larger role in smoothing out head earnings shocks for poorer households.

Family Structure and Cohort Trends in Childhood Family Income Volatility

The authors examine cohort trends in childhood income volatility among U.S. children born between 1970 and 1990. In contrast to previous studies that focused mainly on period trends, the authors adopt a cohort-life-course perspective and measure children’s exposure to income volatility from birth to age 17, which provides a more adequate account of the economic environment during early life stages. Using data from the Panel Study of Income Dynamics, the authors investigate (1) how income volatility among U.S. children has changed across cohorts, (2) how cohort trends in income volatility differ by family structure, and (3) the extent to which the increasing prevalence of single-headed families and the growth of income volatility among single-headed families contribute to the overall trend in childhood income volatility. The results show that (1) income volatility in childhood has increased over time; (2) children who lived in single-headed families experienced greater increase in childhood income volatility than those from two-headed families; and (3) both the increasing prevalence of single-headed families (“composition effects”) and the fact that single-headed families’ incomes have become less stable (“volatility effects”) in the past decades account for a significant proportion of the increasing income volatility for U.S. children, yet their relative contributions differ between White and Black families.

IMPACT OF THE RUSSIA-UKRAINE CRISIS ON FOOD INFLATION AND STOCK MARKETS IN ASIAN ECONOMIES: WITH REFERENCE TO INDIA, CHINA, JAPAN AND THAILAND

The current geopolitical risk posed by the Russia-Ukraine war has significant implications on the stock markets and the commodities markets of several countries. Using monthly data from October 2020 to the present time period, we analyse the impact of Russia-Ukraine crisis on food inflation and stock market return volatility in the context of Asian countries, namely, India, China, Japan and Thailand with the help of regression analysis based on the OLS technique. Our main findings show us the impact of the Russia-Ukraine war on the food inflation to be visible in China and Japan whereas the war's effect on the stock markets of all the countries selected is shown to be insignificant. We observe the highest and lowest monthly food inflation in China, the highest monthly stock return volatility in Thailand and the lowest monthly stock return volatility in Japan among the selected countries. For the case of India, the war doesn't have much effect on its food inflation and stock market return volatility. We suggest reducing food import dependency by strengthening domestic production and going for alternative food suppliers so as to not be affected by the international food prices that are subjected to the turmoil of war. It is imperative to re-diversify portfolios based on the volatility trend of stock market returns in order to shield oneself from the perils of stock market volatility caused by wars.

Spatial Differences in Rice Price Volatility: A Case Study of Pakistan 1994–2011

Prices of agricultural commodities tend to be more volatile in comparison to other commodities. Volatility can result in inefficient allocation of the resources by the farmers, traders and consumers. Rice is the second major staple and export item of Pakistan. This study presents the trends in volatility of regional rice markets of Pakistan and analyses spatial differences in volatility across regional rice markets in Pakistan from 1994 to 2011, and also draws comparison of volatility with the international market. ARCH-LM tests are applied to check the presence of volatility and volatility clustering is found in all the markets. Tests for equality of variance and dynamic conditional correlations (DCC) GARCH model are employed to analyse the spatial differences across the regional rice markets of Pakistan. The results indicate the presence of spatial differences in volatility. Positive conditional correlations in the dynamic conditional correlations (DCC) GARCH model are found which indicate positive association of volatility across markets. Spatial differences in volatility and its persistence reflect the differences in market forces, infrastructure and information flow which leads to varying degree of risk across markets and some regions are exposed to higher risk. The study found out that Hyderabad and Sukkur are the most volatile markets and their volatility levels are highly persistent and require highest time to return to its long-term mean which makes them the riskiest rice markets. Investments in infrastructure, particularly in transportation and controlling the market power of middlemen may reduce price risk across markets particularly in the most risky markets. JEL Classification: C22, C32, Q11, Q13, Q18 Keywords: Rice Prices Volatility, Regional Markets, Pakistan. DCC-GARCH-models

Deep reinforcement learning based on transformer and U-Net framework for stock trading

An effective stock-trading strategy offers investors as much profit and as little risk as possible. Capturing volatility trends from historical stock prices and determining trading strategies is extremely challenging. This study proposes an end-to-end model called DRL-UTrans for learning a single stock trading strategy that combines deep reinforcement learning, transformer layers, and a U-Net architecture. In particular, the transformer layer captures complex dynamic patterns in financial markets. The model structure based on the U-Net architecture contains multiple skip connections used to combine long- and short-term features. The input of the model is a windowed stock price sequence, and the output consists of a trading action and action weight. The benefit of having two outputs is that the agent can control the share of buys and sells to reduce investment risk. In addition, a reward function that is sensitive to market volatility is proposed to feed back the market state. Finally, trading data for 10 stocks is extracted from a real financial market to validate the proposed model. The results show that DRL-UTrans has a higher profitability compared with the seven baseline approaches; further, it is effective in sensing market volatility and hedging market risk when encountering stock crashes.