Changes In Volatility Research Articles

Entropy of Financial Time Series Due to the Shock of War.

The concept of entropy is not uniquely relevant to the statistical mechanics but, among others, it can play pivotal role in the analysis of a time series, particularly the stock market data. In this area, sudden events are especially interesting as they describe abrupt data changes with potentially long-lasting effects. Here, we investigate the impact of such events on the entropy of financial time series. As a case study, we assume data of the Polish stock market, in the context of its main cumulative index, and discuss it for the finite time periods before and after outbreak of the 2022 Russian invasion of Ukraine. This analysis allows us to validate the entropy-based methodology in assessing changes in the market volatility, as driven by the extreme external factors. We show that some qualitative features of such market variations can be well captured in terms of the entropy. In particular, the discussed measure appears to highlight differences between data of the two considered timeframes in agreement with the character of their empirical distributions, which is not always the case in terms of the conventional standard deviation. Moreover, the entropy of cumulative index averages, qualitatively, the entropies of composing assets, suggesting capability for describing interdependencies between them. The entropy is also found to exhibit signatures of the upcoming extreme events. To this end, the role of recent war in shaping the current economic situation is briefly discussed.

TESTING FOR ANTICIPATED CHANGES IN SPOT VOLATILITY AT EVENT TIMES

We propose a test for anticipated changes in spot volatility, either due to continuous or discontinuous price moves, at the times of realization of event risk in the form of pre-scheduled releases of economic information such as earnings announcements by firms and macroeconomic news announcements. These events can generate nontrivial volatility in asset returns, which does not scale even locally in time. Our test is based on short-dated options written on an underlying asset subject to event risk, which takes place after the options’ observation time and prior to or after their expiration. We use options with different tenors to estimate the conditional (risk-neutral) characteristic functions of the underlying asset log-returns over the horizons of the options. Using these estimates and a relationship between the conditional characteristic functions with three different tenors, which holds true if and only if continuous and discontinuous spot volatility does not change at the event time, we design a test for this hypothesis. In an empirical application, we study anticipated individual stocks’ volatility changes following earnings announcements for a set of stocks with good option coverage.

Volatile changes associated with fresh‐cut jackfruit during storage

AbstractHeadspace solid‐phase microextraction and gas chromatography mass spectrometry (HS‐SPME‐GCMS) was performed to study the aroma volatile profile of fresh‐cut jackfruit under different storage temperature conditions (4 and 10°C). Total viable counts (TVC) and yeast and mould count (Y&M) were also simultaneously estimated. Sensory analysis revealed a loss in texture along with an increase in ripeness during storage at both the storage temperatures. It was observed that the volatile profile demonstrated significant changes at different storage temperatures during storage. Principal component analysis performed on volatile compounds allowed to differentiate fresh samples from stored samples, the differential pattern of correlation with volatiles was observed at 4 and 10°C. During storage the ester content increased linearly at both temperatures, however acetate esters predominated initially at 4°C, followed by other non‐acetate esters. In the case of 10°C storage, higher levels of ethyl ester derivatives along with other esters were obtained in the later period of storage due to enhanced microbial activity.

Chemosensory protein regulates the behavioural response of Frankliniella intonsa and Frankliniella occidentalis to tomato zonate spot virus-Infected pepper (Capsicum annuum).

Many herbivorous insects rely on plant volatiles to locate their host plants. Vector-borne viral infections induce changes in plant volatiles, which render infected plants more attractive to insect vectors. However, the detailed mechanisms underlying the olfactory responses of insect vectors induced by the volatiles produced by virus-infected plants are poorly understood. Here, we show that volatiles emitted by pepper (Capsicum annuum) plants infected with tomato zonate spot virus (TZSV), particularly the volatile cis-3-hexenal, which is recognized by chemosensory protein 1 of the thrips Frankliniella intonsa (FintCSP1), are more attractive to F. intonsa than the volatiles emitted by non-infected pepper plants. FintCSP1 is highly abundant in the antenna of F. intonsa. Silencing of FintCSP1 significantly decreased electroantennogram responses of F. intonsa antennae to cis-3-hexenal and impaired thrips' responses to TZSV-infected pepper plants and cis-3-hexenal, as assessed using a Y-tube olfactometer. Three-dimensional model predictions indicated that FintCSP1 consists of seven α-helixes and two disulfide bridges. Molecular docking analysis suggested that cis-3-hexenal is positioned deep inside the binding pocket of FintCSP1 and binds to residues of the protein. We combined site-directed mutagenesis and fluorescence binding assays and identified three hydrophilic residues, Lys26, Thr28, and Glu67, of FintCSP1 as being critical for cis-3-hexenal binding. Furthermore, CSP of F. occidentalis (FoccCSP) is also a key olfactory protein involved in modulating the behaviour of F. occidentalis to TZSV-infected pepper. This study revealed the specific binding characteristics of CSPs to cis-3-hexenal and confirmed the general hypothesis that virus infections induce changes in host volatiles, which can be recognized by the olfactory proteins of the insect vector to enhance vector attraction and this may facilitate viral spread and transmission.

A semiparametric approach to the detection of change-points in volatility dynamics of financial data

Volatility is one of the most important features of financial time series data. There are often structural changes in volatility over time, and an accurate estimation of the volatility of financial time series requires careful identification of change-points. A common approach to modeling the volatility of time series data is based on the well-known GARCH model. Although the problem of change-point estimation of volatility dynamics derived from the GARCH model has been considered in the literature, these approaches rely on parametric assumptions of the conditional error distribution, which are often violated in financial time series. This may lead to inaccuracies in change-point detection, resulting in unreliable GARCH volatility estimates. This article introduces a novel change-point detection algorithm based on a semiparametric GARCH model. The proposed method retains the structural advantages of the GARCH process while incorporating the flexibility of nonparametric conditional error distribution. The approach utilizes a penalized likelihood derived from a semiparametric GARCH model and an efficient binary segmentation algorithm. The results show that in terms of change-point estimation and detection accuracy, the semiparametric method outperforms the commonly used Quasi-MLE (QMLE) and other variations of the GARCH models in wide-ranging scenarios.

Leaf volatile and nonvolatile metabolites show different levels of specificity in response to herbivory.

Plants produce diverse chemical defenses with contrasting effects on different insect herbivores. Deploying herbivore-specific responses can help plants increase their defensive efficiency. Here, we explore how variation in induced plant responses correlates with herbivore species, order, feeding guild, and level of specialization. In a greenhouse experiment, we exposed 149 plants of Salix fragilis (Linnaeus, 1753) to 22 herbivore species naturally associated with this host. The insects belonged to four orders (Coleoptera, Lepidoptera, Hemiptera, and Hymenoptera), three feeding guilds (external leaf-chewers, leaf-tying chewers, and sap-sucking), and included both dietary specialists and generalists. Following herbivory, we quantified induced changes in volatiles and nonvolatile leaf metabolites. We performed multivariate analyses to assess the correlation between herbivore order, feeding guild, dietary specialization, chewing damage by herbivores, and induced responses. The volatile composition was best explained by chewing damage and insect order, with Coleoptera and Lepidoptera eliciting significantly different responses. Furthermore, we recorded significant differences in elicited volatiles among some species within the two orders. Variation in nonvolatile leaf metabolites was mainly explained by the presence of insects, as plants exposed to herbivores showed significantly different metabolites from controls. Herbivore order also played a role to some extent, with beetles eliciting different responses than other herbivores. The induction of volatile and nonvolatile leaf metabolites shows different levels of specificity. The specificity in volatiles could potentially serve as an important cue to specialized predators or parasitoids, increasing the efficacy of volatiles as indirect defenses. By contrast, the induction of nonvolatile leaf metabolites was largely unaffected by herbivore identity. Most nonvolatile metabolites were downregulated, possibly indicating that plants redirected their resources from leaves in response to herbivory. Our results demonstrate how diverse responses to herbivores can contribute to the diversity of plant defensive strategies.

Revealing the influence of microbiota on the flavor of kombucha during natural fermentation process by metagenomic and GC-MS analysis

In this work, raw Pu-erh tea (RAPT) was employed for kombucha preparation, and the microbial composition and volatile flavor compounds of the fermented tea had been investigated during natural fermentation process. The head space-solid phase microextraction-gas chromatograph mass spectrometry (HS-SPME-GC-MS) was performed for volatiles analysis of unfermented tea and kombucha fermented for 3 days (KF-3) and 6 days (KF-6). Meanwhile, the microbial community of KF-3 and KF-6 were evaluated by metagenomic analysis. A total of 72 volatile compounds were identified and obvious changes in volatiles were observed during the fermentation process based on the results of GC-MS and principal component analysis (PCA). Metagenomic sequencing analysis demonstrated that bacterium Komagataeibacter saccharivorans and unclassified-g-komagataeibacter and yeast Saccharomyces cerevisiae and Brettanomyces bruxellensis were the most common microbes contained in the sampled kombucha communities. Furthermore, the relevance among microbial community and volatile compounds was evaluated through correlation heatmap analysis. The results suggested that the main flavor volatiles of kombucha (i.e., acids, esters and terpenes) were closely related to species of genus Komagataeibacter, Gluconacetobacter, Saccharomyces, Brettanomyces, Acetobacter, Novacetimonas and Pichia microorganisms. The obtained results would help to better understand microbial communities and volatile compounds of kombucha, which could provide useful information for enhancing the flavor quality of kombucha products.

Changes in volatiles and essential oil composition of three organs (leaf, stem and flower) of purple basil (Ocimum basilicum L.) by GC–MS combined with multivariate statistical approach

The paper describes volatile compounds (in the organs for flower, leaf and stem) and essential oil contents in fresh or dried purple basil Ocimum basilicum L. grown in Turkey by using polar (TRB-WAX) or non-polar (DB-5MS) gas-chromatographic columns. dl-Limonene, 1,8-cineole, linalool, β-elemene, (Z)-methyl cinnamate and (E)-methyl cinnamate were major compounds of dried or fresh basil samples in polar column, while 1,8-cineole, linalyl acetate, l-linalool, α-terpinolene and (Z)-methyl cinnamate were major compounds of dried or fresh basil samples in non-polar column. 2-Pentanone, 1-pentanol, acetoin and methyl decadienoate were the volatiles detected only in dried basil samples, while 2,3-pentadione, p-xylene, delta-3-carene and o-xylene were found only in fresh basil samples. Furthermore, 1,8-cineole, linalool and methyl cinnamate were found to be the potent volatile compounds in purple basil essential oil with regardless of column type. Significant differences were also observed in the volatile components by using polar or non-polar columns. In conclusion, the multivariate statistical analysis and heat mapping showed that the plant organs contained different volatiles, and drying process significantly affected its concentrations in purple basil and its essential oil. Flower of the basil showed a distinct volatile composition with regardless of drying process and this was confirmed by both statistical approaches.

Dynamic Changes in Volatiles, Soluble Sugars, and Fatty Acids in Glutinous Rice during Cooking.

Cooking is an important process before rice is consumed and constitutes the key process for rice flavor formation. In this paper, dynamic changes in aroma- and sweetness-related compounds were tracked during the entire cooking process (including washing with water, presoaking, and hydrothermal cooking). The volatiles, fatty acids, and soluble sugars in raw rice, washed rice, presoaked rice, and cooked rice were compared. After being washed with water, the total volatiles decreased while aldehydes and unsaturated fatty acids increased. Meanwhile, oligosaccharides decreased and monosaccharides increased. The changes in fatty acids and soluble sugars caused by the presoaking process were similar to those in the water-washing process. However, different changes were observed for volatiles, especially aldehydes and ketone. After hydrothermal cooking, furans, aldehydes, alcohols, and esters increased while hydrocarbons and aromatics decreased. Moreover, all fatty acids increased; among these, oleic acids and linoleic acid increased most. Unlike with washing and presoaking, all soluble sugars except fructose increased after hydrothermal cooking. Principal component analysis showed that cooked rice possessed a volatile profile that was quite different from that of uncooked rice, while washed rice and presoaked rice possessed similar volatile profiles. These results indicated that hydrothermal cooking is the pivotal process for rice flavor formation.

Contemporaneous dependence between euro, crude oil, and gold returns and their respective implied volatility changes. Evidence from the local Gaussian correlation approach

PurposeThis study aims to assess the contemporaneous dependence between euro, crude oil and gold returns and their respective implied volatility changes.Design/methodology/approachThe empirical analysis relies on daily data for the period 2015–2022 and the local Gaussian correlation (LGC) approach that is suitable for estimating dependence between two stochastic processes at any point of their joint distribution.Findings(a) The global correlation coefficients are negative for the euro and crude oil and positive for gold, implying that in the first two markets’ traders are more concerned with sudden price downswings while in the third with sudden upswings. (b) The detailed local analysis, however, shows that traders 2019 attitudes may change with the underlying state of the market and that risk reversals are more likely to occur at the upper extremes of the joint distributions. (c) The pattern of dependence between price returns and implied volatility changes is asymmetric.Originality/valueTo the best of the author’s knowledge, this is the first work that uses the highly flexible LGC approach to analyze the link between price returns and implied volatility changes either in stock or in commodities futures markets. The empirical results provide useful insights into traders’ risk attitudes in different market states.

Detection and Estimation of Change Point in Volatility Function of Foreign Exchange Rate Returns

This work aims at detection and estimation of a change point in conditional variance function of a Nonparametric Auto-Regressive Conditional Heteroscedastic model. The conditional mean and conditional variance functions are not specified a priori but estimated using Nadaraya Watson kernel. This is because inferences based on nonparametric approaches are robust against misspecification of the conditional mean function and the conditional variance function of returns. The squared residuals obtained after estimating the regression function of the returns are used in estimating the conditional variance function. Further, the squared residuals are used in developing a test statistic for unknown abrupt change point in volatility of the exchange rate returns. The test statistic takes into consideration the conditional heteroskedasticity of the disturbances, dependence of the returns, heterogeneity and fourth moment of returns. This does not require prior knowledge of the marginal or the conditional densities of the returns as opposed to maximum likelihood estimation methods. The estimator for change point is considered as the augmented maximum of the test statistic. The consistency of the estimator is stated as a theorem. The asymptotic distribution associated with the test for unknown break points is the Bessel process distribution. The Bessel process distributions have no known simple closed-form expression for the distribution function which makes it difficult to compute exact p-values. Also, the Bessel process distributions depend on two parameters which makes it hard to tabulate the critical values hence one needs to simulate them. After simulating the critical values, hypothesis testing is done in the presence and absence of a change point in volatility of a simulated time series and the test is shown to reject the null hypothesis in the presence of a change point at alpha level of significance. Further, the test fails to reject the null hypothesis in the absence of a change point at alpha level of significance. An application to United States Dollar/Kenya Shilling historical exchange rates returns is made from 1^st January 2010 to 27^th November 2020 where the sample size n = 2839 is done. Through binary segmentation method, three change points are detected, estimated and accounted for. A significant improvement in describing a time series is expected if a point in time for volatility change has been detected and estimated.

Idiosyncratic Volatility, Growth Options, and the Cross-Section of Returns

Abstract The value effect and the idiosyncratic volatility (IVol) discount arise because growth firms and high IVol firms beat the CAPM during periods of increasing aggregate volatility (market volatility and average IVol), that makes their risk low. All else equal, growth options’ value increases with volatility, an effect that is stronger for high IVol firms, for which growth options take a larger fraction of the firm value and firm volatility responds more to aggregate volatility changes. The factor model with the market factor, the market volatility risk factor, and the average IVol factor explains the value effect and the IVol discount. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

Whose sentiment explains implied volatility change and smile?

This letter examines the relationship between investor sentiment and options-implied volatility (IV) dynamics using a high-quality intraday dataset. By classifying each trade by investor type and moneyness, we find that sentiment affects intraday IV changes and explains IV structures and anomalies. Domestic individuals’ sentiment is highly related to implied volatility changes and has a more significant effect on out-of-the-money options that noisy individual investors heavily trade.

Examining Individualistic Driving Volatility Changes During and One Year into the COVID-19 Pandemic Using Paneled Connected Vehicle Trajectories

The coronavirus disease 2019 (COVID-19) pandemic led to a substantial reduction in activity-travel resulting from a shift in the primary purpose of travel from work/study to shopping and essential trips. Further, several researchers noted changes to roadway safety during the early phases of the pandemic, such as a decrease in traffic crashes but an increase in traffic fatalities with respect to vehicle miles traveled. However, the existing literature is limited to holistic trends, with little inference to microscopic driving styles. This research utilizes a paneled approach to compare the freeway behavior of select passenger car drivers with respect to changes in driving volatility stemming from the lockdown period and one year into the COVID-19 pandemic. The methodological design employs a rich paneled dataset obtained from the connected vehicles pilot study in Tampa, Florida. Crash data visualization is also performed to identify crash hotspots within the study area. Volatility measures such as the standard deviation, coefficient of variation, and time-varying stochastic volatility ( TVSV) are then generated and fused with traffic and weather information. Hierarchical modeling is then performed using a panel approach to account for unobserved heterogeneity within multiple observations per individual driver. The results show that the series of jerk-related driving volatility measures and the TVSV of speed increase one year into the pandemic, suggesting a reduction in overall roadway safety along the study segment. Based on the observed individual volatility changes, policy recommendations such as enhanced traffic enforcement guidelines, data-driven safety campaigns, and systematic implementation of lockdown protocols were identified.

The Impact of Changes in Crude Oil price on the Yield and Volatility of Manufacturing: Evidence from the US and China

This study is based on the data of WTI international futures crude oil price, China manufacturing Index and the United States Dow Jones Manufacturing Index from November 23, 2021 to June 16, 2022. This paper uses VAR and ARMA-GARCH models to analyze the changes in yield and volatility of manufacturing in the US and China. The results show that the change of crude oil price under the situation of Russia-Ukraine conflict has a negative effect on the return rate of manufacturing industry in China and the United States, but the impact on the return rate of manufacturing industry in China is greater than that of Dow Jones, and the impact on the return rate of manufacturing industry in the United States will gradually disappear as time goes by. In order to reduce the adverse impact of oil price fluctuations on manufacturing industry, this study puts forward some suggestions, such as adopting diversified ways of oil import and export trade and actively developing and utilizing other energy sources.

A new perspective on air quality index time series forecasting: A ternary interval decomposition ensemble learning paradigm

Accurate forecasting of the air quality index (AQI) plays a crucial role in taking precautions against upcoming air pollution risks. However, air quality may fluctuate greatly in a certain period. Existing forecasting approaches always face the problem of losing valuable information on air quality status, even in the interval models of recent research. To address this issue, this paper suggests a new AQI forecasting perspective and paradigm built upon ternary interval-valued time series (TITS), multivariate variational mode decomposition (MVMD), multivariate relevance vector machine (MVRVM), mixed coding particle swarm optimization (MCPSO), and meteorological factors, which is able to capture the trend and volatility changes of AQI concurrently. The proposed paradigm involves four procedures: TITS construction in terms of the daily minimum, daily mean, and daily maximum AQI, multi-scale decomposition via MVMD, individual forecasting by MCPSO-optimized MVRVM, and ensemble learning forecasting using a simple addition approach. Experiments based on datasets collected from four municipalities in China demonstrated that the presented paradigm can hit higher accuracy than other comparable models, and the application analysis also shows that it has application potential in the AQI online forecasting system. To conclude, the proposed paradigm provides a promising alternative for AQI time series forecasting.

The diligent effect of investor relation officers in conference calls: Evidence from China

This study explores the determinants of investor relations (IR) officers’ diligence in conference calls and the impact of their diligence on capital markets. We apply IR officers' attendance in conference calls as a proxy variable for their diligence. We find that the age, gender, salary, and tenure of IR officers and the start time of conference calls are determinants of IR officers' diligence in conference calls. Their diligence significantly increases institutional ownership and reduces returns volatility. Further analysis shows that IR officers' diligence facilitates the growth of domestic institutional investors' ownership significantly more than that of foreign institutional investors. In addition, information transparency significantly facilitates the relationship between IR officers' diligence and return volatility. Finally, the change in institutional ownership and return volatility also varies with firm size and state ownership. In conclusion, we find that IR officers' diligence plays a positive role in IR management, as it significantly improves firms' institutional ownership and lowers return volatility.

On testing for bubbles during hyperinflations

Abstract We consider testing for the presence of rational bubbles during hyperinflations via an analysis of the non-stationarity properties of relevant observable time series. The test procedure is based on a Markov regime-switching model with independent stochastic changes in its intercept, error variance and autoregressive coefficients. This model formulation allow us to disentangle fundamentals-driven changes in the drift, bubble-driven explosiveness, and volatility changes that may be fundamentals-driven and/or bubble-driven. The testing methodology is illustrated by applying it to data from hyperinflations in Argentina, Brazil, Germany and Poland.

Why Does Volatility Uncertainty Predict Equity Option Returns?

Delta-hedged option returns consistently decrease in volatility of volatility changes (volatility uncertainty), for both implied and realized volatilities. We provide a thorough investigation of the underlying mechanisms including model-risk and gambling-preference channels. Uncertainty of both volatilities amplifies the model risk, leading to a higher option premium charged by dealers. Volatility of volatility-increases, rather than that of volatility-decreases, contributes to the effect of implied volatility uncertainty, supporting the gambling-preference channel. We further strengthen this channel by examining the effects of option end-users net demand and lottery-like features, and by decomposing implied volatility changes into systematic and idiosyncratic components.

Tác động của giá dầu, tỷ giá, tăng trưởng kinh tế đến lạm phát tại Việt Nam

This study provides new insight into the asymmetric relationship between oil price (OIL), the exchange rate (REX), economic growth (GDP), and inflation (INF) in the Vietnamese economy, using quantile-on-quantile regression developed by Sim and Zhou (2015) for the period 2000-2021. Our results demonstrate that there exists a strong and positive interaction between GDP, OIL, and INF over the sample period. Similarly, REX has both positive and negative impacts on INF in the middle and high quantiles ofINF’s distribution. These findings suggest that changes in oil price volatility, GDP, and exchange rate are sensitive to the inflation rate in Vietnam in different market conditions, which are significant policies for the Vietnamese government, policymakers, and market participants to reduce inflation in Vietnam.