Nature Of Volatility Research Articles

Volatility in the Equity Stock Market in India from 2019 to 2024: An Empirical Analysis

This research investigates the volatility and fluctuations in the Indian equity stock market from 2019 to 2024, a period characterized by unprecedented global and domestic challenges. The Indian stock market, like many emerging markets, experienced heightened volatility due to a confluence of events, including the COVID-19 pandemic, geopolitical tensions, economic reforms, and shifts in global financial conditions. The study employs advanced econometric models, including GARCH (Generalized Autoregressive Conditional Heteroskedasticity) and event study analyses, to capture the time-varying nature of market volatility and to identify the key factors driving these fluctuations. Our findings reveal that the COVID-19 pandemic triggered the most significant spikes in volatility, particularly during the early months of 2020, as uncertainty regarding the pandemic's impact on the economy led to sharp market corrections and investor panic. Additionally, the study highlights the influence of macroeconomic variables such as GDP growth, inflation, and monetary policy changes on market behavior. The paper also explores the role of investor sentiment and market liquidity in exacerbating volatility during crisis periods. The results provide valuable insights into the dynamics of the Indian equity market, offering implications for investors seeking to manage risk and for policymakers aiming to enhance market stability. The study underscores the need for robust risk management strategies and proactive regulatory measures to mitigate the adverse effects of such volatility, especially in an increasingly interconnected global financial environment. Keywords: Equity Stock Market, Volatility, India, COVID-19, Economic Reforms, 2019-2024

Research on macroeconomic indicators and stock market correlation analysis based on machine learning

The stock market is known as the barometer of the national economy, and macroeconomic factors have an important impact on the volatility of the stock market. Therefore, considering the impact of macroeconomic factors on the sustainability of stock market volatility will help to capture the time-varying characteristics of volatility persistence, so as to significantly improve the estimation and forecasting effect of volatility. In this work, the generalized autoregressive conditional heteroskedasticity-mixing data sampling (GARCH-MIDAS) model is adopted, which combines the advantages of the GARCH model in short-term volatility modeling and the advantages of MIDAS regression in integrating macroeconomic variables of different frequencies. The GARCH model provides an accurate depiction of intraday volatility in financial markets by capturing the dynamic nature of short-term volatility. MIDAS regression introduces long-term macroeconomic factors into volatility modeling by integrating macroeconomic data with different sampling frequencies, thus making up for the shortcomings of traditional measurement methods in frequency matching. By combining these two methods, the GARCH-MIDAS model can more comprehensively reflect the dynamic changes of stock market volatility, considering both the impact of short-term market volatility and the role of long-term macroeconomic factors, thus providing a more accurate and in-depth analytical tool for volatility prediction and risk management. The results show that the GARCH-MIDAS model can significantly improve the accuracy of volatility forecasting, and provide more reliable decision support for investors, policymakers and economists.

Liberalization vis-à-vis non-liberalization trade policy: Exploring the impact of price volatility on producer share price and cocoa supply response in Nigeria and Ghana

Cocoa producers respond differently to volatile price shocks, but the nature of these responses remains unknown, especially in the liberalized and non-liberalized markets in Nigeria and Ghana, respectively. Therefore, we assess the extent to which price volatility affects the supply of cocoa while also analyzing the effect of price volatility on Nigeria's cocoa producer price share vis-à-vis Ghana's cocoa producer price share. We further analyze how producers react given the asymmetric nature of price volatility in Nigeria and Ghana. Annual secondary data were obtained from the World Development Index, National Bureau of Statistics, International Cocoa Organization, Central Bank of Ghana, Nigeria, and so on, from 1970 to 2019. We used the Ordinary Least Squares method, generalized autoregressive conditional heteroskedastic (GARCH) model, extensions, and the Vector Error Correction Model (VECM) for this study's analysis. Our results show the presence of volatility in price series and that volatility is asymmetric in nature. The results of the supply response show that price volatility has no significant relationship with cocoa supply in Ghana. In contrast, price volatility has a significantly positive relationship with the supply of cocoa in Nigeria at the 1 % level. The results of the VECM show that, in the long run, the cocoa producer price in Ghana will be negatively affected by both international price volatility and inflation rates at 5 % and 1 %, respectively. We suggest that cocoa farmers should have licensing to sell commodities to the international market directly without interference from the marketing board.

Functional central limit theorems for rough volatility

The non-Markovian nature of rough volatility makes Monte Carlo methods challenging, and it is in fact a major challenge to develop fast and accurate simulation algorithms. We provide an efficient one for stochastic Volterra processes, based on an extension of Donsker’s approximation of Brownian motion to the fractional Brownian case with arbitrary Hurst exponent H∈(0,1)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$H \\in (0,1)$\\end{document}. Some of the most relevant consequences of this ‘rough Donsker (rDonsker) theorem’ are functional weak convergence results in Skorokhod space for discrete approximations of a large class of rough stochastic volatility models. This justifies the validity of simple and easy-to-implement Monte Carlo methods, for which we provide detailed numerical recipes. We test these against the current benchmark hybrid scheme and find remarkable agreement (for a large range of values of H\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$H$\\end{document}). Our rDonsker theorem further provides a weak convergence proof for the hybrid scheme itself and allows constructing binomial trees for rough volatility models, the first available scheme (in the rough volatility context) for early exercise options such as American or Bermudan options.

Diversity Analysis of Catechol 2, 3-dioxygenase in POPs Metabolizing Bacteria using In-silico Approach

The persistent nature of persistent organic pollutants (POPs), lipophilicity, and volatility has resulted in their high concentration, not only in environmental resources but also in living organisms. Their complete removal is possible only by mineralization using enzyme-based strategies. Catechol 2, 3-dioxygenase is reportedly involved in the degradation of a wide variety of POPs. This study was designed to determine the diversity of this enzyme among highly efficient bioremediating bacteria. Seven bacteria belonging to genera acinetobacter, pseudomonas, burkholderia, stutzerimonas, and paraburkholderia were targeted. Sequences of the enzyme were retrieved from Uniprot database and analyzed via ProtParam, CELLU, and SOPMA tools and AlphaFold database. The enzyme was found to be cytoplasmic. The physicochemical properties of the enzyme were recorded as pI 4.75 – 5.50, aliphatic index (73.41 – 88.55), instability index (24.98 – 43.37), and GRAVY (-0.209 – 0.511). The secondary structure attributes were recorded to be α-helix (30.13 – 37.30), extended strand (18.27 – 21.54), β-turn (5.14 – 6.95), and random coil (38.33 – 42.95). All the proteins showed complex folding except in Pseudomonas sp strain EST1001. These properties may be exploited during the selection, purification, manipulation, and cloning of the enzyme for efficient bioremediation.

Modeling and Forecasting Exchange Rate Volatility in West Africa using GARCH models

Abstract This study empirically investigates the nature of exchange rate volatility in the context of West Africa. The study uses daily data on the exchange rates of the West African CFA franc (XOF) in terms of US Dollar. The empirical analysis has been carried out for the period from 13-11-2009 to 18-09-2023, for a total of 5058 observations. We excluded the last 25% of observations in order to evaluate the forecasting accuracy. The exchange rate volatility of the West African CFA franc against the US Dollar is estimated using GARCH models based on normal and student’s t-distribution of innovations. Results show that the ARMA(3,1)-GARCH(1,2) model with student-t distribution is well adequate model to capture the mean and the volatility process of USD-CFA exchange rate log returns. Keywords: Exchange rates, normal and student-t distribution, ARMA-GARCH.

Prediction of NEPSE Index Movement Using Technical Analysis

Due to the nature of the stock and index, predicting index or price movements is a challenging task. The nature of index volatility makes it impossible for investors to profit from stocks. To address this problem, investors need to make accurate predictions about future index or stock price changes. In this study, data has collected for 6 months (December 2022 to May 2023) and technical trading tools such as candlesticks and Google Form Questionnaire Surveys were used to determine the movement of the NEPSE index. A questionnaire survey is completed with 62 answers. Given the Nepalese context and due to the global economic crisis and uncertain political environment, as a result, in a bearish market, the candlestick stick pattern produces mixed results while the trader can benefit from it in the very short term, but sometimes does not provide the right and appropriate movement.

Does being different from others increase extremeness and volatility in restaurant firm performance?

Considering the sensitivity of the restaurant industry – its volatility to economic changes and its competitive environment – it is imperative for restaurant operators to manage business risk. Based on a risk management perspective, this paper seeks to draw performance implications from firms’ strategic disconformity, which can help explain the inherent nature of uncertainty and volatility. The results of the study indicate that restaurant firms’ strategic disconformity is positively associated with performance extremeness and performance volatility. Moreover, the relationship between a restaurant firm’s strategic disconformity and extremeness and volatility of firm performance is negatively moderated by environmental complexity. The findings of this study enrich the restaurant literature by incorporating this important aspect of the risky nature of strategic disconformity in understanding a firm’s performance extremeness and volatility. This study also implies that restaurant operators need to engage in strategic disconformity with care, depending on their current environmental conditions.

Less is more? New evidence from stock market volatility predictability

The complex nature of stock market volatility has motivated researchers to apply a variety of predictors to obtain reliable predictive information for precise forecasting. This study seeks to examine the effectiveness of the novel Global Financial Uncertainty (GFU) indices, comprising of only five sub-indices, in predicting stock market volatility using the widely used mixed-data sampling (MIDAS) model. The results demonstrate the remarkable and stable predictive power of GFU, even during crises and global financial uncertainty shocks. Specifically, the financial uncertainty index from Europe plays a significant role in our analysis. Importantly, we find that the GFU index outperforms a large number of other indicators in stock volatility forecasting. The statistical and economic significance of the predictive power of GFU is remarkable. Our study provides significant insights for market participants and policymakers that highlight the need to prioritize global financial uncertainty.

Mixed‐frequency predictive regressions with parameter learning

AbstractWe explore the performance of mixed‐frequency predictive regressions for stock returns from the perspective of a Bayesian investor. We develop a constrained parameter learning approach for sequential estimation allowing for belief revisions. Empirically, we find that mixed‐frequency models improve predictability, not only because of the combination of predictors with different frequencies but also due to the preservation of high‐frequency features such as time‐varying volatility. Temporally aggregated models misspecify the evolution frequency of the volatility dynamics, resulting in poor volatility timing and worse portfolio performance than the mixed‐frequency specification. These results highlight the importance of preserving the potential mixed‐frequency nature of predictors and volatility in predictive regressions.

Nonparametric Test for Volatility in Clustered Multiple Time Series

Contagion arising from clustering of multiple time series like those in the stock market indicators can further complicate the nature of volatility, rendering a parametric test (relying on asymptotic distribution) to suffer from issues on size and power. We propose a test on volatility based on the bootstrap method for multiple time series, intended to account for possible presence of contagion effect. While the test is fairly robust to distributional assumptions, it depends on the nature of volatility. The test is correctly sized even in cases where the time series are almost nonstationary (i.e., autocorrelation coefficient ≈1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\approx 1$$\\end{document}). The test is also powerful specially when the time series are stationary in mean and that volatility are contained only in fewer clusters. We illustrate the method in global stock prices data.

THE ROEBLING RECORDS: LABOR MARKETS AND JOB VOLATILITY BETWEEN 1920-50

In recent years, the Trent House Museum uncovered the Roebling Records — a detailed account of worker life in Trenton’s largest industrial factory. The thousands of records offer a window into what employment in mid-20th-century New Jersey looked like. This Paper analyzes the Roebling Records, scoping in on worker layoffs, quits, and terminations. It contributes to the underexplored scholarly discussion of mid-20th-century labor markets by locating variables that influence the nature and severity of job volatility. In picking apart the records, this Paper describes the destabilizing effects of job insecurity on the average American worker. Labor conditions in the 1920-50s are of interest to any scholars studying modern labor markets. They offer vital lessons about institutional arrangements, cyclical economic shifts, and labor market behavior. Applying the historical analysis, this Essay ultimately answers a loaded question: Is modern-day America in for yet another round of job volatility?

Dual nature of volatility on drop wetting dynamics of acetone–isopropanol mixtures on ultrathin smooth oil films

Volatile drop impacts are commonplace among various industrial and natural processes and are most often studied under Leidenfrost conditions, where a vaporized film sustains the drop weight or reverses drop momentum. The vapor thrust generated is, therefore, a function of many variables such as the enthalpy of vaporization, the superheat, the specific heat capacity of the vapor, vapor thermal diffusivity, and the vapor film thickness. In this study, volatile drop impact and wetting dynamics of acetone and isopropanol mixtures at a normal temperature and pressure were shown to generate enough thrust from evaporation alone during the impact process and allow for unique contact dynamics. Volatility was controlled by varying the acetone concentration in isopropanol mixtures [O(1–10 kPa)] to keep surface tension relatively constant, while the vapor pressure and viscosity increased. Wetting onset was tracked using a high-speed optical total internal reflection microscopy technique where an increase in the vapor pressure enhanced wetting onset (i.e., pure acetone). However, the concentrations between ≈ 49% and 66% isopropanol, which have vapor pressures of ≈11.3–14.8 kPa, respectively, caused drops to rebound at We up to We ≈ 21, beyond the classic disjoining pressure dominant regime of We > 10.

VOLATILITAS HARGA BAWANG DI JAWA BARAT DENGAN METODE ARCH/GARCH

Food commodities such as shallots and garlic are still a concern for the Indonesian people, this is because prices are not fixed and fluctuate for each period. It is these price fluctuations that cause high price volatility. An illustration of the magnitude of the risks that will be faced by economic actors in the future can be illustrated by the magnitude of price changes that indicate market fluctuations in a period of time. The model that can be used to analyze the nature of price volatility is Autoregressive Conditional Heteroscedasticity (ARCH) and Generalized Autoregressive Conditional Heteroscedasticity (GARCH). The purpose of this study was to determine the price volatility of shallots and garlic in West Java Province for the period January 2013 to December 2021. The results showed that retail prices for garlic were heteroscedastic, so the model that was more suitable was ARCH/GARCH. As for the commodity of shallots that are homoscedastic, the model that is carried out is only up to the ARIMA model analysis. The volatility model for garlic is the ARCH(1) model. The results of the model estimation show that garlic price volatility is quite high

Nature, Trends and Drivers of Food Price Volatility in Nigeria

The volatility of food prices is an important risk factor which constitutes serious threat to the welfare of millions of people around the world, particularly in developing countries like Nigeria. The study therefore investigated the pattern and drivers of food price volatility in Nigeria using annual and monthly time series data from January,2000 to December, 2020. Data analysis was done using descriptive statistics, Coefficient of Variation, Auto-Regressive Conditional Heteroscedasticity (ARCH) model, Generalized Auto-Regressive Conditional Heteroscedasticity (GARCH) model, and Exponential GARCH (EGARCH). The study reveal volatility clustering between time period 2000 and 2012 but afterwards, the fluctuation observed in food prices was almost muted. Prices of most food items witnessed a forward leap between the periods 2000-2006 and 2007-2012, with the price of rice almost experiencing a threefold rise. Beyond this period, food prices have remained high, clearly surpassing their initial levels in 2013-2020. Returns on consumer price index, lending rates, exchange rate, and food price rate are important drivers of food price volatility in Nigeria. The study therefore recommends the need for an effective and sustainable price stabilization mechanism which involves holding strategic or buffer stock to protect the interest of producers against unstable prices.

On the volatility of WTI crude oil prices: A time-varying approach with stochastic volatility

This study investigates the impacts of crude oil-market-specific fundamental factors and financial indicators on the realized volatility of West Texas Intermediate (WTI) crude oil price. A time-varying parameter vector autoregression model with stochastic volatility (TVP-VAR-SV) is applied to weekly data series spanning January 2008 to October 2021. It is found that the WTI oil price volatility responds positively to a shock in oil production, oil inventories, the US dollar index, and VIX but negatively to a shock in the US economic activity. The response to the EPU index was initially positive and then turned slightly negative before fading away. The VIX index has the most significant effect. Furthermore, the time-varying nature of the response of the WTI realized oil price volatility is evident. Extreme effects materialize during economic recessions and crises, especially during the COVID-19 pandemic. The findings can improve our understanding of the time-varying nature and determinants of WTI oil price volatility.

Understanding the Impact of COVID-19 on the Volatility Dynamism of Brics Stock Market

The current study is aimed at examining the impact of COVID-19 on the volatility of stock market returns in the BRICS economies known as trading partners. Due to the pandemic situation all over the world, it had influence on the working of the stock markets as the investors were vigilant of their investment position. Since the World Bank declared the first confirmed case of covid-19 on 31st December 2019, hence the daily data of the stock indices of the each of the BRICS economies has been fetch ranging from 1st January 2020 to 31st march2021. The impact of Covid-19 has been studied by taking the number of daily Covid-19 cases in the respective economies. The study employed GARCH family of models such as GARCH(1,1), EGARCH(1,1) and MGARCH(1,1) in order to understand the nature and dynamics of volatility of the stock markets of BRICS economies so that investors can make decisions regarding portfolio management while investing in such economies acting as global trading partners. The study found that except China and South Africa, the rising number of cases has significant impact on BRICS economies volatility. The level of persistence is found to be high, revealing that it takes time to volatility to wipe out from the market, indicating that the BRICS stock market remained volatile for a longer period of time due to the arrival of Covid news.

An Empirical Study of Volatility in Cryptocurrency Market

Cryptocurrencies have gained a lot of attraction across the globe. Most observers of the cryptocurrency market will agree that crypto volatility is in a different league altogether. There has been a growing need to understand the nature of volatility in cryptocurrency. This paper analyzes the performance of four mostly traded, different cryptocurrencies in terms of their risk and return. The relationship between the return and returns volatility among different currencies has been examined considering the daily closing prices from 1 January 2017 to 30 June 2022, using the family of the GARCH model. The study has explored the spillover and asymmetric effect of volatility by using the DCC GARCH model and EGARCH model, respectively. The causal behavior among different cryptocurrencies has also been examined using Granger causality. There has been a strong spillover effect among different cryptocurrencies, Bitcoin and Ether, which are the top two cryptocurrencies with the highest market capitalization which have exhibited an asymmetric impact in their volatility as compared to the other two currencies, which are Litecoin and XRP.

Individual earnings and family income: Dynamics and distribution

We review research on the dynamics and distribution of individual earnings and family income. We start with univariate earnings models, which dominate the literature and are often used as the exogenous component of family income in structural models of saving. We present a version of the linear model that nests most of the specifications that have been used in the literature, and then discuss recent papers that stress nonnormal shocks, nonlinear and age-dependent processes, and heterogeneous model parameters. The recent work provides a much richer description of the nature of earnings volatility than the basic model. We then turn to models of individual earnings that are based on wages, employment, job mobility, and hours. These multivariate models permit measuring the sources of permanent differences in earnings and distinguishing among shocks that influence earnings through employment, job mobility, general productivity, or hours. Finally, we consider models of lifetime family income that integrate individual earnings, marriage (accounting for marital sorting), and earnings of a spouse, if present. We conclude by discussing directions for future work.

Detecting Financial Crisis in Nigerian Capital Markets Using Markov Switching Generalized Autoregressive Conditional Heteroscedasticity

Financial crisis is often a thing of major concern in many countries. It has led so many experts to be interested in identifying the causes and the general statistical pattern associated with them. Based on the causes, pattern and characteristics exhibited by the financial crisis, it is expected that experts should be able to develop a model to detect the financial crisis. The financial crisis in Nigerian stock markets can be observed as a macro-economic indicator. Therefore, the aim of this study is to develop a model using a macro-economic indicator (All share index) that can explain the combined nature of volatility and switching characteristics associated with Nigerian capital markets. The data used for the study is the All Share Index (ASI) of Nigerian stock markets spanned from the month of January, 1985 – January 2021. The series is fitted to conditionally compound monthly return and the result obtained was used in fitting volatility and Markov switching GARCH models. Based on the AIC and SIC, the EGARCH model was found to be the appropriate model for modeling Nigerian stock markets. However, structural changes in the model were tested using the Chow breakpoint test. The results obtained based on the test of structural changes, found that there was a structural change in the real exchange rate in the period between February 1998 and July 2010. For MSGARCH (3,1.1), the results show that the conditional probability of surviving in the high volatility state in the next period is 0.984. Also, the conditional probability of surviving in a medium volatility state in the next period is 0.617 and the conditional probability of surviving in a low volatility state in the next period is 0.410. The results showed that the MSGARCH (3,1,1) model could explain that the period from February 1997 until July 2020 had probability values of more than 0.6. This reveals high volatility confirming the occurrence of a crisis