Volatility Trends Research Articles

Relationship between sand-dust weather and water dynamics of desert areas in the middle reaches of Heihe River

Sand-dust weather has become an international social-environmental issue of common concern, and constitutes a serious threat to human lives and economic development. In order to explore the responses of natural desert sand and dust to the dynamics of water in desertification, we extracted long-term monitoring data related to precipitation, soil water, groundwater, and sand-dust weather. These data originated from the test stations for desertification control in desert areas of the middle reaches of the Heihe River. We used an algorithm of characteristic parameters, correlations, and multiple regression analysis to establish a regression model for the duration of sand-dust weather. The response characteristics of the natural desert sand and dust and changes of the water inter-annual and annual variance were also examined. Our results showed: (1) From 2006 to 2014 the frequency, duration, and volatility trends of sand-dust weather obviously increased, but the change amplitudes of precipitation, soil water, and groundwater level grew smaller. (2) In the vegetative growth seasons from March to November, the annual variance rates of the soil moisture content in each of four studied layers of soil samples were similar, and the changes in the frequency and duration of sand-dust weather were similar. (3) Our new regression equation for the duration of sand-dust weather passed the R test, F test, and t test. By this regression model we could predict the duration of sand-dust weather with an accuracy of 42.9%. This study can thus provide technological support and reference data for water resource management and research regarding sand-dust weather mechanisms.

Formation and thermochemical properties of oxychlorides of niobium (Nb) and tantalum (Ta): Towards the gas-phase investigation of dubnium (Db) oxychloride

The formation of NbOCl3 and TaOCl3 and their adsorption behavior on quartz surfaces was explored by applying an isothermal gas-chromatographic method. Trace amounts of short-lived Nb and Ta isotopes were used. Adsorption enthalpy values (ΔHads) at zero surface coverage of –ΔHads(NbOCl3) = 102 ± 4 kJ/mol and –ΔHads(TaOCl3) = 128 ± 5 kJ/mol were determined by analyzing the chromatographic behavior of the Nb and Ta complexes with a Monte-Carlo simulation method based on an adsorption–desorption kinetic model. By applying an empirical correlation, the experimental ΔHads values were successively related to the macroscopic standard sublimation enthalpy, ΔH°subl, as a measure of the volatility of each substance. The inferred sublimation enthalpies are in agreement with tabulated thermochemical values. Thus, the linear empirical correlation between ΔHads and ΔH°subl for metal-oxychlorides was updated with the inclusion of the present data. According to the predicted ΔH°subl(DbOCl3), a ΔHads(DbOCl3) value of 135 ± 2 kJ/mol was extrapolated. The future accomplishment of comparative studies with DbOCl3 under the same experimental conditions will provide valuable information on the volatility trend in Group-5 elements, together with an indication on the magnitude of relativistic effects on the electronic structure of dubnium.

Non-Affine Stochastic Volatility With Seasonal Trends

We propose a new stochastic volatility model for pricing options on assets that exhibit seasonal trends in volatility. Such assets are prevalent among commodities, with futures on grains and energy being an example. The model is based on the 3/2 stochastic volatility model, but includes a cyclical long-run volatility component. The model yields a closed-form characteristic function which can be used to rapidly calibrate the model to benchmark options. We test the model on market data and show that the model proposed here allows for significantly better empirical fit to option prices on corn and wheat futures than the unmodified 3/2 model.

Analysis on elemental volatilization behavior of titanium alloys during electron beam smelting

Electron beam smelting is an effective technology for removal of inclusions from titanium alloys and improvement of metallurgical quality. However, since different element has different volatilization capacity, resulting in a large deviation in alloy composition, which affects the performance of titanium alloys. In this paper, the volatility trend of the components in a Ti-Al-Cr-Nb quaternary model alloy was evaluated and a calculation model for alloy composition change was established based on Langmuir's volatilization rate theory. The results show that, for the other three elements in the alloy, the order of relative volatility coefficient is Al > Cr > Nb. The mass fraction of Al in the alloy will be significantly reduced after smelting, which plays an important role in control of alloy composition. The effect of temperature on the change of alloy composition is greater than that of smelting time. When the temperature is over 2100 K, the composition changes too much in a very short time. The experimental results are consistent with the theoretical results, indicating the applicability of the model.

Statistical volatility duration and complexity of financial dynamics on Sierpinski gasket lattice percolation

A novel agent-based financial dynamics is established based on the percolation system on Sierpinski gasket lattice to reproduce the statistical characteristics of financial markets. Sierpinski gasket lattice is a fractal-like graph which corresponds to the fractal well-known Sierpinski gasket. Modeling a financial dynamics on Sierpinski gasket lattice is a new approach in financial micro-mechanism construction. In an attempt to investigate the statistical properties of returns and prove the feasibility of the proposed model, two new nonlinear statistics maximum and average monotonous volatility duration are introduced in this work for the first time, which describe the trend of volatility of return time series in financial markets. Furthermore, a new method CMCID is proposed to measure the synchronization and similarity behaviors of return volatility duration in multiscale, the results show that the pairs of real data and simulated data have synchronization, and the synchronization becomes stronger when the scale increases And the power-law distribution is employed to investigate the corresponding statistical properties, which shows that the proposed model shares a common power-law distribution with regard to monotonous volatility duration. The empirical results show that the simulated series from the financial model and the historical data share common statistical properties, which indicate that the proposed model is reasonable in terms of volatility behavior.

Follow the leader! The peer effect in aid supply decisions

AbstractThis study assesses the existence of imitative behaviour among donors in terms of their aid supply. The urgency in addressing this subject is motivated by an increasing degree of aid volatility and unpredictability which may be linked to this imitative behaviour.Our results highlight that while any connection among donor peers is a potential channel for the transmission of aid volatility, the extent of such volatility decreases significantly in the presence of established imitative behaviour.This result suggests that the promotion of donor imitative behaviour would contribute to containing the current and growing trend of volatility in aid supply.

Comparison of Semi-Parametric and Benchmark Value-At-Risk Models in Several Time Periods with Different Volatility Levels

Abstract In the literature, there is no consensus as to which Value-at-Risk forecasting model is the best for measuring market risk in banks. In the study an analysis of Value-at-Risk forecasting model quality over varying economic stability periods for main indices from stock exchanges was conducted. The VaR forecasts from GARCH(1,1), GARCH-t(1,1), GARCH-st(1,1), QML-GARCH(1,1), CAViaR and historical simulation models in periods with contrasting volatility trends (increasing, constantly high and decreasing) for countries economically developed (the USA – S&P 500, Germany - DAX and Japan – Nikkei 225) and economically developing (China – SSE COMP, Poland – WIG20 and Turkey – XU100) were compared. The data samples used in the analysis were selected from the period 01.01.1999 – 24.03.2017. To assess the VaR forecast quality: excess ratio, Basel traffic light test, coverage tests (Kupiec test, Christoffersen test), Dynamic Quantile test, cost functions and Diebold-Marino test were used. Obtained results show that the quality of Value-at-Risk forecasts for the models varies depending on a volatility trend. However, GARCH-st (1,1) and QML-GARCH(1,1) were found to be the most robust models in the different volatility periods. The results show as well that the CAViaR model forecasts were less appropriate in the increasing volatility period. Moreover, no significant differences for the VaR forecast quality were found for the developed and developing countries.

Price Transmission and Volatility Spillover in Food Grain Market: Experience from Indian vis-à-vis World Market

In the backdrop of liberalised trade of agricultural commodities in the twenty- first century, world food prices have risen at a faster pace since 2007. The Indian economy has largely been able to insulate itself from the price transmission mechanism of the world market because of low exposure of the economy to global factors. This paper analyses the trend of volatility in the price of rice and wheat in the world market vis-à-vis regional markets in India during 2000-15. In addition, an econometric analysis was conducted to measure the price transmission mechanism in understanding the process of volatility spillover from international to domestic markets.

New Evidence on Earnings Volatility in Survey and Administrative Data

Despite the rise in cross-sectional inequality since the late 1990s, there is little consensus on trends in earnings volatility during this period. Using consistent samples and methods in administrative earnings data matched to the Survey of Income and Program Participation (SIPP GSF) and survey data from the Panel Study of Income Dynamics (PSID), we examine earnings volatility for men from 1978 through 2011. In contrast to the apparent inconsistency in trends across administrative and survey data in the existing literature, we find recent increases in volatility in the SIPP GSF and the PSID, though increases are larger in the PSID.

Applying Organizational Ambidexterity in strategic management under a “VUCA” environment: Evidence from high tech companies in China

High tech organizations confront dual demands of exploration and exploitation, particularly in today’ s competitive environment marked by a trend of volatility, uncertainty, complexity, and ambiguity. Although there is general consensus about the relationship between ambidexterity and firm performance, few studies have probed into the mechanism of how it is applied in the strategic management process. Considering the premise of environmental change, this research considers a multilevel perspective on the nature of organizational ambidexterity and its functioning in the whole strategic management process. Through a comparative case study of the two most famous high tech companies in China, a model is provided to reflect the functioning of organizational ambidexterity where a cognitive pattern of top management teamsplays a critical role in the ability that the organizations demonstrate. This research also contributes to the literature of entrepreneurship by extending innovation from a demand-side perspective, where the core of product/market exploration/exploitation activities could be expanded into customer value creation. Undertaking a careful analysis of the mechanism of organizational ambidexterity, this research sheds new light on the literature of strategic management that operates in a more turbulent environment.

Response Relationship between the Seasonal Freezing-Thawing Process of Soil and Spatial Factor Changes in the Dayekou Basin of the Qilian Mountains

Objective: In this study, the influence and response relationship between the seasonal freezing-thawing process of soil and the spatial factor changes in the management and utilization of water resource processes were explored. Methods: The monitoring equipment in this study was arranged at different altitudes, gradients, and slope directions, such as the typical forest sample area in the Dayekou Basin of the Qilian Mountains. The spatial variation characteristics of the seasonal freezing-thawing process of the soil were analyzed, and a regression model was established. Results: 1) The results of this study determined that the rate of the soil’s freezing increased with the altitude in a trend of volatility. However, the rate of the thawing of the frozen soil was found to have an opposite trend. The variation degree of the freezing-thawing process increased with the altitude in a trend of volatility. The end time of the approximate soil freezing with altitude increased in a volatility trend ahead of schedule. However, the opposite was observed in the thawing rate of the frozen soil; 2) The rate of the soil’s freezing under the mosses of the spruce forest at an altitude of 3028 m was found to be the lowest. However, in the sub-alpine scrub forest at an altitude of 3300 m, a maximum in the spatial ordering was observed, with an average of 1.9 cm·d-1. The thawing rate of the frozen soil in scrub-spruce forest at an altitude of 3300 m was found to be minimal. However, in the sunny slope grassland at an altitude of 2946 m, a maximum in the spatial ordering was observed, with an average of 1.5 cm·d-1. In the spatial ordering of the variation degree of the process of freezing-thawing with an average of 1.2, the scrub-grassland at an altitude of 2518 m was found to be the lowest, and the scrub-spruce forest at an altitude of 3195 m was also low; 3) The soil freezing began on approximately October 20th, and the rate of soil freezing gradually became reduced. The arrival time of the frozen soil of up to 150 cm in depth in sub-alpine scrub forest was first observed at an altitude of 3028 m. However, the scrub-spruce forest at an altitude of 3100 m did not become frozen until approximately January 12th on average. Then, the thawing rate of the frozen soil increased gradually. The end time of the thawing was earliest observed in the sunny slope grassland at an altitude of 2946 m. However, the scrub-spruce forest at an altitude of 3100 m was found to be the last to thaw, and averaged approximately July 27th. The average durations of the freezing and thawing of the soil were 77 and 121 days, respectively, and the average duration of the entire process of freezing-thawing was 199 days; 4) This study’s established regression models of the duration time of frozen soil’s thaw, and the rate of frozen soil’s thaw, all passed the R test of goodness of fit, F test of variance, and t test. Conclusions: The characteristics of the seasonal freezing-thawing process of the soil with the spatial changes were seasonal. However, the characteristics under the different spatial factor influences were not the same.

A Temporal Analysis of Intraday Volatility of Nifty Futures on the National Stock Exchange

This paper aims to establish trends in intraday volatility in context of the Indian stock market and analyze the impact of development in the Indian economy on its stock market volatility. One minute tick data of Nifty 50 futures from Jan 1, 2011 to Aug 31, 2018 was used for the purpose of this research. Volatility was computed for each day of week and various time intervals. Our analysis shows evidence of the expected U-shaped pattern of intraday volatility (higher at the beginning and end of the day). We also observed a decline in the hourly volatility over the time period studied. However, sufficient evidence to determine the impact of development in the Indian economy on volatility in the stock market was not found.

Price volatility trends and price transmission for major staples in India

India is one of the largest producers as well as exporters of some major food commodities, and is often criticized for its protectionist measures to curb transmission of price shocks from international markets. This paper examines if such policies are necessary to protect domestic consumers from price fluctuations and what are their implications on international prices. To understand this, in this paper we have examined the patterns, trends and volatility in domestic and international prices of rice and wheat, and found that although both the international and domestic prices are volatile, the degree of volatility is higher in the international prices. The volatility in domestic prices is mainly due to internal production shocks and is not influenced much by the international prices. This disconnect is attributed to domestic policy measures, such as market support to farmers and public stockholding of food grains for public distribution and price stabilization.

Indirect Inference for Locally Stationary Models

We propose the use of indirect inference estimation to conduct inference in complex locally stationary models. We develop a local indirect inference algorithm and establish the asymptotic properties of the proposed estimator. Due to the nonparametric nature of locally stationary models, the resulting indirect inference estimator exhibits nonparametric rates of convergence. We validate our methodology with simulation studies in the confines of a locally stationary moving average model and a new locally stationary multiplicative stochastic volatility model. Using this indirect inference methodology and the new locally stationary volatility model, we obtain evidence of non-linear, time-varying volatility trends for monthly returns on several Fama-French portfolios.

A hybrid Regression and Deep Learning LSTM based Technique for Predicting Volatility Index (VIX) direction of change (Trend)

Objectives: The main of study is to predict the volatility trend with high accuracy. The improved accuracy can help profitable trades. Outliers in dataset can reduce prediction accuracy; an innovative dynamic approach is proposed to filter outliers from the dataset. Methods: In this study, an intelligent methodology is proposed to predict volatility trend with hybrid regression based outlier removal technique and advanced deep learning Long Short-Term Memory (LSTM) techniques. Regression techniques are used for prediction and classification problems, the same principle is applied here for identifying the outliers in the data. The random data set is trained and tested multiple trials with regression technique. The algorithm generates standard errors, residual error and ‘p’ value for all the predictions, these values are compared with standard threshold across all the trials. The similar errors with multiple occurrences are identified as outliers and removed from the dataset. The LSTM techniques trained and tested with different epochs and network configuration till the prediction results improve. Result: This study uses India Volatility Index (VIX) data for predicting next day volatility. The results show significant improvement in accuracy with the proposed approach. The results in this paper demonstrate the LSTM techniques out performs regression, decision trees, random forest, SVM, boosting techniques and neural network based techniques. Application: The paper also shows that usage of regression techniques for removal of outlier further improves the forecasting accuracy by 10%. The proposed approach can be applied for forex and option trading. The proposed approach can also used in other predictive modeling problems such as CRM, Healthcare, Credit risk and Auto insurance. Keywords: Deep Learning, learning Long Short-Term Memory (LSTM), Prediction, Regression, Trend, Volatility

Halide Effects on the Sublimation Temperature of X-Au-L Complexes: Implications for Their Use as Precursors in Vapor Phase Deposition Methods.

Trends in volatility with changes in the halide ligand were established for gold(I) complexes of the type X-Au-L [X = Cl, Br, I; L = CNtBu, CNMe, PMe3, P(NMe2)3, P(OCH2CF3)3] by determining the temperatures for the onset of sublimation (Tsub) at a fixed pressure. Within each series of isocyanide complexes, Tsub decreases with increasing atomic radius of the halide, making the iodide complex the most volatile. For the phosphine and phosphoramidate complexes, the chloride and bromide have similar Tsub values with the bromide slightly higher, but the iodide complex is again the most volatile of the three. The trends in volatility can be correlated to variation in Au-Au bond distances and aggregation patterns in the solid state structures. For the P(OCH2CF3)3 complexes, melting occurred before sublimation, but the iodide complex was still more volatile than the bromide. These trends have implications for the use of these complexes in electron beam induced deposition and chemical vapor deposition, for which precursor volatility is important.

Turbulence in the Currency Markets-What Does It Mean for International Business?

This article reflects on the recent increase in volatility in the foreign exchange market and discusses its implications for managers of multinational corporations. More specifically, it explores how currency volatility affects many aspects of international business including strategy, investment and finance, operations and sourcing, marketing, and risk management as well as strategies to mitigate, or even benefit from it. The article further discusses strategies managers of MNCs can employ to hedge currency volatility in the short and long run, and looks ahead at possible currency volatility trends.

Financial development and long-run volatility trends

Countries with more developed financial markets tend to have significantly lower aggregate volatility. This relationship is also highly non-linear—starting from a low level of financial development the reduction in aggregate volatility is far more significant with respect to financial deepening than when the financial market is more developed. We build a fully-fledged heterogeneous-agent model with an endogenous financial market of private credit and debt to rationalize these stylized facts. We show how financial development that promotes better credit allocations under more relaxed borrowing constraints can reduce the impact of non-financial shocks (such as TFP shocks, government spending shocks, preference shocks) on aggregate output and investment, and why this volatility-reducing effect diminishes with continuing financial liberalizations. Our simple model also sheds light on a number of other important issues, such as the “Great Moderation” and the simultaneously rising trends of dispersions in sales growth and stock returns for publicly traded firms.

The volatility of returns from commodity futures: evidence from India

BackgroundThis paper examines the pattern of the volatility of the daily return of select commodity futures in India and explores the extent to which the select commodity futures satisfy the Samuelson hypothesis.MethodsOne commodity future from each group of futures is chosen for the analysis. The select commodities are potato, gold, crude oil, and mentha oil. The data are collected from MCX India over the period 2004–2012. This study uses several econometric techniques for the analysis. The GARCH model is introduced for examining the volatility of commodity futures. One of the key contributions of the paper is the use of the β term of the GARCH model to address the Samuelson hypothesis.ResultThe Samuelson hypothesis, when tested by daily returns and using standard deviation as a crude measure of volatility, is supported for gold futures only, as per the value of β (the GARCH effect). The values of the rolling standard deviation, used as a measure of the trend in the volatility of daily returns, exhibits a decreasing volatility trend for potato futures and an increasing volatility trend for gold futures in all contract cycles. The result of the GARCH (1,1) model suggests the presence of persistent volatility and the prevalence of long memory for the select commodity futures, except potato futures.ConclusionsThe study sheds light on significant characteristics of the daily return volatility of the commodity futures under analysis. The results suggest the existence of a developed market for the gold and crude oil futures (with volatility clustering) and show that the maturity effect is only valid for the gold futures.

The association between stock price volatility and financial news – a sentiment analysis approach

PurposeThe purpose of this study is to develop the dictionary with grammar and multiword structure has to be used in conjunction with sentiment analysis to investigate the relationship between financial news and stock market volatility.Design/methodology/approachAn algorithm has been developed for calculating the sentiment orientation and score of data with added information, and the results of calculation have been integrated to construct an empirical model for calculating stock market volatility.FindingsThe experimental results reveal a statistically significant relationship between financial news and stock market volatility. Moreover, positive (negative) news is found to be positively (negatively) correlated with positive stock returns, and the score of added information of the news is positively correlated with stock returns. Model verification and stock market volatility predictions are verified over four time periods (monthly, quarterly, semiannually and annually). The results show that the prediction accuracy of the models approaches 66% and stock market volatility with a particular trend-predicting effect in specific periods by using moving window evaluation.Research limitations/implicationsOnly one news source is used and the research period is only two years; thus, future studies should incorporate several data sources and use a longer period to conduct a more in-depth analysis.Practical implicationsUnderstanding trends in stock market volatility can decrease risk and increase profit from investment. Therefore, individuals or businesses can feasibly engage in investment activities for profit by understanding volatility trends in capital markets.Originality/valueThe ability to exploit textual information could potentially increase the quality of the data. Few scholars have applied sentiment analysis in investigating interdisciplinary topics that cover information management technology, accounting and finance. Furthermore, few studies have provided support for structured and unstructured data. In this paper, the efficiency of providing the algorithm, the model and the trend in stock market volatility has been demonstrated.