Статті в журналах: "VOLATILITY MEASUREMENT"

1

Arsy, Izza Dinikal, and Dedi Rosadi. "MEASUREMENT OF SUPPORT VECTOR REGRESSION PERFORMANCE WITH CLUSTER ANALYSIS FOR STOCK PRICE MODELING." MEDIA STATISTIKA 15, no. 2 (April 6, 2023): 163–74. http://dx.doi.org/10.14710/medstat.15.2.163-174.

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Risk-averse investors will seek out stock investments with the minimum risk. One step that can be taken is to develop a model of stock prices and predict their fluctuations in the coming months. Significant studies on the modeling of stock movements have used the ARCH/GARCH method, but this method requires some assumptions. This paper will discuss the performance of stock modeling using Support Vector Regression. The performance is measured using the root mean square error value in two stock clusters based on its volatility value, e.g., stocks with large volatility and stocks with small volatility. This case study makes use of daily closing price data from 10 LQ-45 index shares from October 12, 2018 to October 11, 2019. In conclusion, SVR's performance on stocks with high volatility produces RMSE, which is considerably higher than SVR's performance on stocks with low volatility.

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2

Mellman, George S. "Improving Return Volatility Measurement and Presentation." CFA Digest 31, no. 4 (November 2001): 77–79. http://dx.doi.org/10.2469/dig.v31.n4.982.

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3

Freitas, Samuel V. D., Mariana B. Oliveira, Álvaro S. Lima, and João A. P. Coutinho. "Measurement and Prediction of Biodiesel Volatility." Energy & Fuels 26, no. 5 (April 24, 2012): 3048–53. http://dx.doi.org/10.1021/ef3004174.

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4

Karnezi, E., I. Riipinen, and S. N. Pandis. "Measuring the atmospheric organic aerosol volatility distribution: a theoretical analysis." Atmospheric Measurement Techniques Discussions 7, no. 1 (January 28, 2014): 859–93. http://dx.doi.org/10.5194/amtd-7-859-2014.

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Abstract. Organic compounds represent a significant fraction of submicrometer atmospheric aerosol mass. Even if most of these compounds are semi-volatile in atmospheric concentrations, the ambient organic aerosol volatility is quite uncertain. The most common volatility measurement method relies on the use of a thermodenuder (TD). The aerosol passes through a heated tube where its more volatile components evaporate leaving the less volatile behind in the particulate phase. The typical result of a~thermodenuder measurement is the mass fraction remaining (MFR), which depends among other factors on the organic aerosol (OA) vaporization enthalpy and the accommodation coefficient. We use a new method combining forward modeling, introduction of "experimental" error and inverse modeling with error minimization for the interpretation of TD measurements. The OA volatility distribution, its effective vaporization enthalpy, the mass accommodation coefficient and the corresponding uncertainty ranges are calculated. Our results indicate that existing TD-based approaches quite often cannot estimate reliably the OA volatility distribution, leading to large uncertainties, since there are many different combinations of the three properties that can lead to similar thermograms. We propose an improved experimental approach combining TD and isothermal dilution measurements. We evaluate this experimental approach using the same model and show that it is suitable for studies of OA volatility in the lab and the field.

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5

Lee, B. H., E. Kostenidou, L. Hildebrandt, I. Riipinen, G. J. Engelhart, C. Mohr, P. F. DeCarlo, et al. "Measurement of the ambient organic aerosol volatility distribution: application during the Finokalia Aerosol Measurement Experiment (FAME-2008)." Atmospheric Chemistry and Physics Discussions 10, no. 7 (July 20, 2010): 17435–66. http://dx.doi.org/10.5194/acpd-10-17435-2010.

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Abstract. A variable residence time thermodenuder (TD) was combined with an Aerodyne Aerosol Mass Spectrometer (AMS) and a Scanning Mobility Particle Sizer (SMPS) to measure the volatility distribution of aged organic aerosol in the Eastern Mediterranean during the Finokalia Aerosol Measurement Experiment in May of 2008 (FAME-2008). A new method for the quantification of the organic aerosol volatility distribution was developed combining measurements of all three instruments together with an aerosol dynamics model. Challenges in the interpretation of ambient thermodenuder-AMS measurements include the potential resistances to mass transfer during particle evaporation, the effects of particle size on the evaporated mass fraction, the changes in the AMS collection efficiency and particle density as the particles evaporate partially in the TD, and finally potential losses inside the TD. Our proposed measurement and data analysis method accounts for all of these problems combining the AMS and SMPS measurements. The AMS collection efficiency of the aerosol that passed through the TD was found to be approximately 10% lower than the collection efficiency of the aerosol that passed through the bypass. The organic aerosol measured at Finokalia is approximately 2 orders of magnitude less volatile than fresh laboratory-generated biogenic secondary organic aerosol. This low volatility is consistent with its highly oxygenated AMS mass spectrum. The results are found to be highly sensitive to the mass accommodation coefficient of the evaporating species.

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6

Karnezi, E., I. Riipinen, and S. N. Pandis. "Measuring the atmospheric organic aerosol volatility distribution: a theoretical analysis." Atmospheric Measurement Techniques 7, no. 9 (September 16, 2014): 2953–65. http://dx.doi.org/10.5194/amt-7-2953-2014.

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Abstract. Organic compounds represent a significant fraction of submicrometer atmospheric aerosol mass. Even if most of these compounds are semi-volatile in atmospheric concentrations, the ambient organic aerosol volatility is quite uncertain. The most common volatility measurement method relies on the use of a thermodenuder (TD). The aerosol passes through a heated tube where its more volatile components evaporate, leaving the less volatile components behind in the particulate phase. The typical result of a thermodenuder measurement is the mass fraction remaining (MFR), which depends, among other factors, on the organic aerosol (OA) vaporization enthalpy and the accommodation coefficient. We use a new method combining forward modeling, introduction of "experimental" error, and inverse modeling with error minimization for the interpretation of TD measurements. The OA volatility distribution, its effective vaporization enthalpy, the mass accommodation coefficient and the corresponding uncertainty ranges are calculated. Our results indicate that existing TD-based approaches quite often cannot estimate reliably the OA volatility distribution, leading to large uncertainties, since there are many different combinations of the three properties that can lead to similar thermograms. We propose an improved experimental approach combining TD and isothermal dilution measurements. We evaluate this experimental approach using the same model, and show that it is suitable for studies of OA volatility in the lab and the field.

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7

Lee, B. H., E. Kostenidou, L. Hildebrandt, I. Riipinen, G. J. Engelhart, C. Mohr, P. F. DeCarlo, et al. "Measurement of the ambient organic aerosol volatility distribution: application during the Finokalia Aerosol Measurement Experiment (FAME-2008)." Atmospheric Chemistry and Physics 10, no. 24 (December 21, 2010): 12149–60. http://dx.doi.org/10.5194/acp-10-12149-2010.

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Abstract. A variable residence time thermodenuder (TD) was combined with an Aerodyne Aerosol Mass Spectrometer (AMS) and a Scanning Mobility Particle Sizer (SMPS) to measure the volatility distribution of aged organic aerosol in the Eastern Mediterranean during the Finokalia Aerosol Measurement Experiment in May of 2008 (FAME-2008). A new method for the quantification of the organic aerosol volatility distribution was developed combining measurements of all three instruments together with an aerosol dynamics model. Challenges in the interpretation of ambient thermodenuder-AMS measurements include the potential resistances to mass transfer during particle evaporation, the effects of particle size on the evaporated mass fraction, the changes in the AMS collection efficiency and particle density as the particles evaporate partially in the TD, and finally potential losses inside the TD. Our proposed measurement and data analysis method accounts for all of these problems combining the AMS and SMPS measurements. The AMS collection efficiency of the aerosol that passed through the TD was found to be approximately 10% lower than the collection efficiency of the aerosol that passed through the bypass. The organic aerosol measured at Finokalia is approximately 2 or more orders of magnitude less volatile than fresh laboratory-generated monoterpene (α-pinene, β-pinene and limonene under low NOx conditions) secondary organic aerosol. This low volatility is consistent with its highly oxygenated AMS mass spectrum. The results are found to be highly sensitive to the mass accommodation coefficient of the evaporating species. This analysis is based on the assumption that there were no significant reactions taking place inside the thermodenuder.

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8

Rushworth, S. A., L. M. Smith, A. J. Kingsley, R. Odedra, R. Nickson, and P. Hughes. "Vapour pressure measurement of low volatility precursors." Microelectronics Reliability 45, no. 5-6 (May 2005): 1000–1002. http://dx.doi.org/10.1016/j.microrel.2004.11.007.

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9

Cipollini, Fabrizio, Giampiero M. Gallo, and Edoardo Otranto. "Realized volatility forecasting: Robustness to measurement errors." International Journal of Forecasting 37, no. 1 (January 2021): 44–57. http://dx.doi.org/10.1016/j.ijforecast.2020.02.009.

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10

Eom, Cheoljun, Taisei Kaizoj, Jong Won Park, and Enrico Scalas. "Realized FX Volatility : Statistical Properties and Applications." Journal of Derivatives and Quantitative Studies 26, no. 1 (February 28, 2018): 1–25. http://dx.doi.org/10.1108/jdqs-01-2018-b0001.

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This paper empirically examines the statistical properties of realized volatility and the relationships between volatility and correlation measurements of realized volatility by using intraday high-frequency foreign exchange (FX) rates. Results regarding the distributional and dynamic properties of realized volatility are in agreement with the findings of previous studies. However, the positive correlation present in previous studies is not found in the case of JPY. On trading days with low volatility in the FX market, realized correlation coefficients between JPY and other currencies have positive values, while realized correlation coefficients on trading days with high volatility show negative values. These results are due to the Japanese government's intervention in the FX market, particularly during trading days with high volatility. In this regard, our results suggest that the positive relationships between volatility and correlations verified in previous studies are not a general phenomenon in the case of government intervention and government intervention may distort the efficiency of the FX market. In addition, we show that the multivariate measurement of realized volatility based on intraday high-frequency data can be a useful tool for determining the occurrence of external intervention in the FX market.

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11

Guo, Ming Yuan. "Weighted Median Realized Volatility and Its Application in China’s Stock Market." Advanced Materials Research 403-408 (November 2011): 5235–38. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.5235.

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Median realized volatility is a new measure approach of volatility in high-frequency time series. Median realized volatility is model-free and can be computed easily. Weighted median realized volatility is a more efficient volatility measurement, which make median realized volatility become its special case. In this paper, we compare Median realized volatility and weighted Median realized volatility from four aspects: definition, bias, efficiency, calendar effect. Through the empirical study on the Shanghai stock market, this paper proves weighted Median realized volatility is superior to Median Realized Volatility.

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12

ABAD, PILAR, and SONIA BENITO. "ACCURATE OF VAR CALCULATED USING EMPIRICAL MODELS OF THE TERM STRUCTURE." International Journal of Theoretical and Applied Finance 12, no. 06 (September 2009): 811–32. http://dx.doi.org/10.1142/s0219024909005476.

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This work compares the accuracy of different measures of Value at Risk (VaR) of fixed income portfolios calculated on the basis of different multi-factor empirical models of the term structure of interest rates (TSIR). There are three models included in the comparison: (1) regression models, (2) principal component models, and (3) parametric models. In addition, the cartography system used by Riskmetrics is included. Since calculation of a VaR estimate with any of these models requires the use of a volatility measurement, this work uses three types of measurements: exponential moving averages, equal weight moving averages, and GARCH models. Consequently, the comparison of the accuracy of VaR estimates has two dimensions: the multi-factor model and the volatility measurement. With respect to multi-factor models, the presented evidence indicates that the Riskmetrics model or cartography system is the most accurate model when VaR estimates are calculated at a 5% confidence level. On the contrary, at a 1% confidence level, the parametric model (Nelson and Siegel model) is the one that yields more accurate VaR estimates. With respect to the volatility measurements, the results indicate that, as a general rule, no measurement works systematically better than the rest. All the results obtained are independent of the time horizon for which VaR is calculated, i.e. either one or ten days.

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13

Li, Fenglan, Jie Wang, Liyun Su, and Bao Yang. "Dynamic VaR Measurement of Gold Market with SV-T-MN Model." Discrete Dynamics in Nature and Society 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/5183914.

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VaR (Value at Risk) in the gold market was measured and predicted by combining stochastic volatility (SV) model with extreme value theory. Firstly, for the fat tail and volatility persistence characteristics in gold market return series, the gold price return volatility was modeled by SV-T-MN (SV-T with Mixture-of-Normal distribution) model based on state space. Secondly, future sample volatility prediction was realized by using approximate filtering algorithm. Finally, extreme value theory based on generalized Pareto distribution was applied to measure dynamic risk value (VaR) of gold market return. Through the proposed model on the price of gold, empirical analysis was investigated; the results show that presented combined model can measure and predict Value at Risk of the gold market reasonably and effectively and enable investors to further understand the extreme risk of gold market and take coping strategies actively.

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14

Kangasniemi, Oskari, Pauli Simonen, Jana Moldanová, Hilkka Timonen, Luis M. F. Barreira, Heidi Hellén, Jukka-Pekka Jalkanen, et al. "Volatility of a Ship’s Emissions in the Baltic Sea Using Modelling and Measurements in Real-World Conditions." Atmosphere 14, no. 7 (July 20, 2023): 1175. http://dx.doi.org/10.3390/atmos14071175.

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Shipping emissions are a major source of particulate matter in the atmosphere. The volatility of gaseous and particulate phase ship emissions are poorly known despite their potentially significant effect on the evolution of the emissions and their secondary organic aerosol (SOA) formation potential. An approach combining a genetic optimisation algorithm with volatility modelling was used on volatility measurement data to study the volatility distribution of a ship engine’s emissions in real-world conditions. The fuels used were marine gas oil (MGO) and methanol. The engine was operated with 50% and 70% loads with and without active NOx after-treatment with selective catalytic reduction (SCR). The volatility distributions were extended to higher volatilities by combining the speciation information of the gas phase volatile organic compounds with particle phase volatility distributions and organic carbon measurements. These measurements also provided the emission factors of the gas and particle phase emissions. The results for the particle phase volatility matched well with the existing results placing most of the volatile organic mass in the intermediate volatile organic compounds (IVOC). The IVOCs also dominated the speciated gas phase. Partitioning of the emissions in the gas and particle phases was affected significantly by the total organic mass concentration, underlining the importance of the effect of the dilution on the phase of the emissions.

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15

Cao, Li-Ming, Xiao-Feng Huang, Yuan-Yuan Li, Min Hu, and Ling-Yan He. "Volatility measurement of atmospheric submicron aerosols in an urban atmosphere in southern China." Atmospheric Chemistry and Physics 18, no. 3 (February 6, 2018): 1729–43. http://dx.doi.org/10.5194/acp-18-1729-2018.

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Abstract. Aerosol pollution has been a very serious environmental problem in China for many years. The volatility of aerosols can affect the distribution of compounds in the gas and aerosol phases, the atmospheric fates of the corresponding components, and the measurement of the concentration of aerosols. Compared to the characterization of chemical composition, few studies have focused on the volatility of aerosols in China. In this study, a thermodenuder aerosol mass spectrometer (TD-AMS) system was deployed to study the volatility of non-refractory submicron particulate matter (PM1) species during winter in Shenzhen. To our knowledge, this paper is the first report of the volatilities of aerosol chemical components based on a TD-AMS system in China. The average PM1 mass concentration during the experiment was 42.7±20.1 µg m−3, with organic aerosol (OA) being the most abundant component (43.2 % of the total mass). The volatility of chemical species measured by the AMS varied, with nitrate showing the highest volatility, with a mass fraction remaining (MFR) of 0.57 at 50 ∘C. Organics showed semi-volatile characteristics (the MFR was 0.88 at 50 ∘C), and the volatility had a relatively linear correlation with the TD temperature (from the ambient temperature to 200 ∘C), with an evaporation rate of 0.45 %∘C-1. Five subtypes of OA were resolved from total OA using positive matrix factorization (PMF) for data obtained under both ambient temperature and high temperatures through the TD, including a hydrocarbon-like OA (HOA, accounting for 13.5 %), a cooking OA (COA, 20.6 %), a biomass-burning OA (BBOA, 8.9 %), and two oxygenated OAs (OOAs): a less-oxidized OOA (LO-OOA, 39.1 %) and a more-oxidized OOA (MO-OOA, 17.9 %). Different OA factors presented different volatilities, and the volatility sequence of the OA factors at 50 ∘C was HOA (MFR of 0.56) > LO-OOA (0.70) > COA (0.85) ≈ BBOA (0.87) > MO-OOA (0.99), which was not completely consistent with the sequence of their O ∕ C ratios. The high volatility of HOA implied that it had a high potential to be oxidized to secondary species in the gas phase. The aerosol volatility measurement results in this study provide useful parameters for the modeling work of aerosol evolution in China and are also helpful in understanding the formation mechanisms of secondary aerosols.

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16

Andersson, Jonas, and Anders Ågren. "Volatility modeling in the presence of measurement errors." Journal of Risk 3, no. 4 (July 2001): 53–67. http://dx.doi.org/10.21314/jor.2001.051.

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17

Rahmi, Mustika, Nurul Azma, Aminullah Achmad Muttaqin, Thuba Jazil, and Mahfuzur Rahman. "Risk Volatility Measurement: Evidence from Indonesian Stock Market." Journal of Asian Finance, Economics and Business 3, no. 3 (August 30, 2016): 57–65. http://dx.doi.org/10.13106/jafeb.2016.vol3.no3.57.

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18

Ferson, Wayne, and Haitao Mo. "Performance measurement with selectivity, market and volatility timing." Journal of Financial Economics 121, no. 1 (July 2016): 93–110. http://dx.doi.org/10.1016/j.jfineco.2016.02.012.

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19

Mitra, Sovan. "Downside risk measurement in regime switching stochastic volatility." Journal of Computational and Applied Mathematics 378 (November 2020): 112845. http://dx.doi.org/10.1016/j.cam.2020.112845.

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20

Mohd Jefrie, Mohd Adza, Imbarine Bujang, Jasman Tuyon, and Debbra Toria Anak Nipo. "ESTIMATION AND MODELLING OF VOLATILITY IN THE MALAYSIAN STOCK MARKET." Malaysian Journal of Business and Economics (MJBE) 7, no. 1 (December 31, 2020): 85. http://dx.doi.org/10.51200/mjbe.vi.2695.

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Last three decades, the issues on the volatility of the stock market have attracted many researchers, academics and also the players in the financial market. In the stock market investors and researchers able to use the stock market index to measure the volatility. Volatility considered as the measurement for the uncertainty of fluctuation of stock price and measurement of risk. This paper intends to shed light the volatility behaviour via the persistency and leverage effect in the Malaysian stock market. The data of this paper starting from 2000 until 2018 and employ symmetric and asymmetric volatility model with a different distribution. The symmetric model can capture via Generalized Autoregressive Conditional Heteroscedasticity (GARCH) while asymmetric shock using Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH) and Threshold Generalized Autoregressive Conditional Heteroscedasticity (TGARCH). The GARCH model showed weekly data of FTSE BM KLCI, FTSE BM Top100, FTSE BM Mid70 and FTSE BM Small presence of volatility clustering and persistence effect on the stock market volatility. Besides, asymmetric models found that weekly data, only several indices found the leverage effect. The best fit model also provided in the results and discussion.

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21

Zeng, Qin Qing, Le Chen, Min Xie, Ya Qiong Fu, and Zhen Zhou. "Calibration of Thermostatic Bath Used on Electronic Thermometer Verification." Applied Mechanics and Materials 635-637 (September 2014): 819–23. http://dx.doi.org/10.4028/www.scientific.net/amm.635-637.819.

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In order to research the uniformity and volatility of thermostatic bath used on electronic thermometer verification, a method based on thermostat technical performance specifications is presented. Including the introduction of calibration device, the choice of standard device and experiment method. In the end the uncertainty analysis on the measurement of thermostatic bath uniformity and volatility is made. Experiment results show that the uniformity and volatility of this thermostatic bath are 0.0091°C and 0.0087°C/10min with the measurement uncertainty of 0.018°C, The results meet the requirements of electronic thermometer verification. It provides guarantee for the next electronic thermometer verification.

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22

Asri, Marselinus. "MODELING RISK MEASUREMENT IN EMERGING MARKET." Contemporary Journal on Business and Accounting 1, no. 1 (May 7, 2021): 1–22. http://dx.doi.org/10.58792/cjba.v1i1.10.

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Purpose – This study aims to make modeling measurement risk in capital market variables. Design/methodology/approach – Using Mathematical approaches to integrated a noticeable increase in the ﬁrm-level idiosyncratic risk, the volatility measure of coeficient is greater and has a stronger upward trend than the new idiosyncratic volatility measure. Findings – Using the the model decomposing total risk in market variance extended by Bali et.al, we integrated the model with initial model, Fama-French idiosyncratic risk Model, we sugested new model: Rit -RFt = ai + bi (R Mt R Ft) + var.HLt+ var.SBt +Var.MW +Var.RW+ Var.CMA + ei Originality – This paper introduces a variance measure of aggregate idiosyncratic risk, which does not require estimation of market betas or correlations and is based on the concept of gain from portofolio diversification. Keywords: Idiosyncratic Risk, New Model Paper Type Research Result

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23

Li, Zhicheng, and Haipeng Xing. "High-Frequency Quote Volatility Measurement Using a Change-Point Intensity Model." Mathematics 10, no. 4 (February 18, 2022): 634. http://dx.doi.org/10.3390/math10040634.

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Quote volatility is important in determining the cost of demand in a high frequency (HF) order market. This paper proposes a new model to measure quote volatility based on the point process and price-change duration. Specifically, we built a change-point intensity (CPI) model to describe the dynamics of price-change events for a given level of threshold. The instantaneous volatility of quote price can be calculated at any time according to price-change intensities. Based on this, we can quantify the cost of demanding liquidity for traders with different trading latency by using integrated variances. Furthermore, we use the autoregressive conditional intensity (ACI) model proposed by Russell (1999) as a benchmark comparison. The results suggest that our model has better performance of both in-sample fitness and out-of-sample prediction.

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24

Cain, Kerrigan P., and Spyros N. Pandis. "A technique for the measurement of organic aerosol hygroscopicity, oxidation level, and volatility distributions." Atmospheric Measurement Techniques 10, no. 12 (December 13, 2017): 4865–76. http://dx.doi.org/10.5194/amt-10-4865-2017.

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Abstract. Hygroscopicity, oxidation level, and volatility are three crucial properties of organic pollutants. This study assesses the feasibility of a novel measurement and analysis technique to determine these properties and establish their relationship. The proposed experimental setup utilizes a cloud condensation nuclei (CCN) counter to quantify hygroscopic activity, an aerosol mass spectrometer to measure the oxidation level, and a thermodenuder to evaluate the volatility. The setup was first tested with secondary organic aerosol (SOA) formed from the ozonolysis of α-pinene. The results of the first experiments indicated that, for this system, the less volatile SOA contained species that had on average lower O : C ratios and hygroscopicities. In this SOA system, both low- and high-volatility components can have comparable oxidation levels and hygroscopicities. The method developed here can be used to provide valuable insights about the relationships among organic aerosol hygroscopicity, oxidation level, and volatility.

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25

LO, C. F., P. H. YUEN, and C. H. HUI. "OPTION RISK MEASUREMENT WITH TIME-DEPENDENT PARAMETERS." International Journal of Theoretical and Applied Finance 03, no. 03 (July 2000): 581–89. http://dx.doi.org/10.1142/s0219024900000668.

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In value-at-risk (VaR) methodology of option risk measurement, the determination of market values of the current option positions under various market scenarios is critical. Under the full revaluation and factor sensitivity approach which are accepted by regulators, accurate revaluation and precise factor sensitivity calculation of options in response to significant moves in market variables are important for measuring option risks in terms of VaR figures. This paper provides a method for pricing equity options in the constant elasticity variance (CEV) model environment using the Lie-algebraic technique when the model parameters are time-dependent. Analytical solutions for option values incorporating time-dependent model parameters are obtained in various CEV processes. The numerical results, which are obtained by employing a very efficient computing algorithm similar to the one proposed by Schroder [11], indicate that the option values are sensitive to the time-dependent volatility term structures. It is also possible to generate further results using various functional forms for interest rate and dividend term structures. From the analytical option pricing formulae, one can achieve more accuracy to compute factor sensitivities using more realistic term-structures in volatility, interest rate and dividend yield. In view of the CEV model being empirically considered to be a better candidate in equity option pricing than the traditional Black–Scholes model, more precise risk management in equity options can be achieved by incorporating term-structures of interest rates, volatility and dividend into the CEV option valuation model.

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26

Nakajima and Toyoshima. "Measurement of Connectedness and Frequency Dynamics in Global Natural Gas Markets." Energies 12, no. 20 (October 16, 2019): 3927. http://dx.doi.org/10.3390/en12203927.

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We examine spillovers among the North American, European, and Asia–Pacific natural gas markets based on daily data. We use daily natural gas price indexes from 2 February 2009 to 28 February 2019 for the Henry Hub, National Balancing Point, Title Transfer Facility, and Japan Korea Marker. The results of spillover analyses indicate the total connectedness of the return and volatility series to be 22.9% and 32.8%, respectively. In other words, volatility is more highly integrated than returns. The results of the spectral analyses indicate the spillover effect of the return series can largely be explained by short-term factors, while that of the volatility series can be largely explained by long-term factors. The results of the dynamic analyses with moving window samples do not indicate that global gas market liquidity increases with the increasing spillover index. However, the results identify the spillover effect fluctuation caused by demand and supply.

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27

Wang, Yaw-Huei, and Yu-Jen Hsiao. "The Impact of Non-trading Periods on the Measurement of Volatility." Review of Pacific Basin Financial Markets and Policies 13, no. 04 (December 2010): 607–20. http://dx.doi.org/10.1142/s0219091510002098.

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Based upon the theory of the "arrival of news", the main purpose of this paper is to investigate the impact of non-trading periods on the measurement of volatility for the S&P 500, FTSE 100, and TAIEX indices. Using an adaptation of the GJR (1,1) model, we find that both weekday holiday periods and half-day trading periods have significant impacts on the estimation of volatility for the S&P 500 and FTSE 100 indices. On the other hand, weekends have significant impacts for the TAIEX index. Our findings imply that for the UK and US markets, much less relevant information is produced during weekends, while more relevant information continues to be produced during other types of non-trading periods. However, the weekend volatility of the Taiwan market is specially driven because the US macro news is announced on Fridays and the trading time of the US market is later than that of the Taiwan market without any overlapping.

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28

Muzzioli, Silvia, Luca Gambarelli, and Bernard De Baets. "Indices for Financial Market Volatility Obtained Through Fuzzy Regression." International Journal of Information Technology & Decision Making 17, no. 06 (November 2018): 1659–91. http://dx.doi.org/10.1142/s0219622018500335.

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The measurement of volatility is of fundamental importance in finance. Standard market practice adopted for volatility estimation from option prices leads to a considerable loss of information and the introduction of an element of arbitrariness in the volatility index computation. We propose to adopt fuzzy regression methods in order to include all the available information from option prices, and to obtain an informative volatility index. In fact, the obtained fuzzy volatility indices not only offer a most possible value, but also a lower and an upper bound for the interval of possible values, providing investors with an additional source of information. We also propose a defuzzification procedure to select a representative value within this interval. Moreover, we investigate the occurrence of truncation and discretization errors in volatility index computation by adopting an interpolation-extrapolation method. We also test the forecasting power of each volatility index on future realized volatility.

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29

Mitra, Sovan. "Political risk modelling and measurement from stochastic volatility models." International Journal of Sustainable Economy 11, no. 2 (2019): 184. http://dx.doi.org/10.1504/ijse.2019.099064.

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30

Mitra, Sovan. "Political risk modelling and measurement from stochastic volatility models." International Journal of Sustainable Economy 11, no. 2 (2019): 184. http://dx.doi.org/10.1504/ijse.2019.10020049.

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31

Malliavin, Paul, and Maria Elvira Mancino. "Instantaneous liquidity rate, its econometric measurement by volatility feedback." Comptes Rendus Mathematique 334, no. 6 (January 2002): 505–8. http://dx.doi.org/10.1016/s1631-073x(02)02297-5.

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32

Gorbachev, Olga. "Did Household Consumption Become More Volatile?" American Economic Review 101, no. 5 (August 1, 2011): 2248–70. http://dx.doi.org/10.1257/aer.101.5.2248.

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I show that after accounting for predictable variation arising from movements in real interest rates, preferences and income shocks, liquidity constraints and measurement errors, volatility of household consumption in the US increased by 25 percent between 1970 and 2004. The increase was lower than that of volatility of family income. Nonwhite and those with less than 13 years of education, for whom there was no differential increase in income volatility, experienced a significantly larger increase in volatility of household consumption. Substantial differences in wealth and access to credit markets point to the main reason for this divide. JEL: D12, D14, E21, J15

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33

Hu, Jiangshan, Yunyun Sui, and Fang Ma. "The Measurement Method of Investor Sentiment and Its Relationship with Stock Market." Computational Intelligence and Neuroscience 2021 (March 13, 2021): 1–11. http://dx.doi.org/10.1155/2021/6672677.

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Investor sentiment is a hot topic in behavioral finance. How to measure investor sentiment? Is the influence of investor sentiment on the stock market symmetrical? That is all we need to think about. Therefore, this paper firstly selects five emotional proxy variables and constructs an investor sentiment composite index by principal component analysis. Secondly, the MS-VAR model is employed to study the dynamic relationship among investor sentiment, stock market returns, and volatility. Using the model MSIH (2)-VAR (2), we found that the relationship among the investor sentiment, stock returns, and volatility is different in different regimes. The results of orthogonal cumulative impulse response analysis showed that the shock to investor sentiment has a significant impact on stock market returns, and this impact in the bullish stock market is significantly higher than in the bearish stock market. The impact of the shock to stock market returns on investor sentiment and stock market volatility is relatively significant. The shock to stock market volatility has significant effects on the stock market returns. Overall, the influence of investor sentiment on the stock market is asymmetric; that is, in different regimes of the stock market, the impact of investor sentiment on the stock market is different. Realizing this, investors can better understand and grasp the market, guiding their own investment behavior. Other researchers can also further study the measurement of investor sentiment on this basis to better guide investors’ behavior.

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34

Li, Y., U. Pöschl, and M. Shiraiwa. "Molecular corridors and parameterizations of volatility in the evolution of organic aerosols." Atmospheric Chemistry and Physics Discussions 15, no. 19 (October 15, 2015): 27877–915. http://dx.doi.org/10.5194/acpd-15-27877-2015.

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Abstract. The formation and aging of organic aerosols (OA) proceed through multiple steps of chemical reaction and mass transport in the gas and particle phases, which is challenging for the interpretation of field measurements and laboratory experiments as well as accurate representation of OA evolution in atmospheric aerosol models. Based on data from over 30 000 compounds, we show that organic compounds with a wide variety of functional groups fall into molecular corridors, characterized by a tight inverse correlation between molar mass and volatility. We developed parameterizations to predict the volatility of organic compounds containing oxygen, nitrogen and sulfur from the elemental composition that can be measured by soft-ionization high-resolution mass spectrometry. Field measurement data from new particle formation events, biomass burning, cloud/fog processing, and indoor environments were mapped into molecular corridors to characterize the chemical nature of the observed OA components. We found that less oxidized indoor OA are constrained to a corridor of low molar mass and high volatility, whereas highly oxygenated compounds in atmospheric water extend to high molar mass and low volatility. Among the nitrogen- and sulfur-containing compounds identified in atmospheric aerosols, amines tend to exhibit low molar mass and high volatility, whereas organonitrates and organosulfates follow high O : C corridors extending to high molar mass and low volatility. We suggest that the consideration of molar mass and molecular corridors can help to constrain volatility and particle phase state in the modeling of OA particularly for nitrogen- and sulfur-containing compounds.

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35

Martinez, Raul E., Brent J. Williams, Yaping Zhang, David Hagan, Michael Walker, Nathan M. Kreisberg, Susanne V. Hering, Thorsten Hohaus, John T. Jayne, and Douglas R. Worsnop. "Development of a volatility and polarity separator (VAPS) for volatility- and polarity-resolved organic aerosol measurement." Aerosol Science and Technology 50, no. 3 (February 2, 2016): 255–71. http://dx.doi.org/10.1080/02786826.2016.1147645.

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36

Roy, Malay K., and Madhusudan Karmakar. "Stock Market Volatility: Roots and Results." Vikalpa: The Journal for Decision Makers 20, no. 1 (January 1995): 37–50. http://dx.doi.org/10.1177/0256090919950104.

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Based on measurement of stock market volatility for the period 1935 to 1992, Malay K Roy and Madhusudan Karmakar focus on two key issues : a) What is the average level of volatility and whether it has increased in the current period; b) Whether the present trend of share price movement is likely to impair the development process of our economy.

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37

Rhouas, Sara, Mustapha Bouchekourte, and Norelislam El Hami. "Optimization of the impact measurement of market structure on liquidity and volatility." International Journal for Simulation and Multidisciplinary Design Optimization 13 (2022): 9. http://dx.doi.org/10.1051/smdo/2021040.

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Liquidity and volatility are the two barometers that allow stock markets to appreciate in terms of attractiveness, profitability and efficiency. Several macroeconomic and microstructure variables condition the level of liquidity that directly impact the asset allocation decisions of different investor profiles − institutional and individuals − and therefore the dynamics of the market as a whole. Volatility is the regulatory component that provides information on the level of risk that characterizes the market. Thus, the appreciation of these two elements is of considerable help to fund managers looking to optimize their equity pockets. In this work, we will use the liquidity ratio as a proxy variable for the liquidity of the Moroccan stock market, to estimate the indicators and factors that determine its short- and long-term variability. The appropriate econometric method would be to estimate an error correction vector model (ECVM) which has the property of determining the long- and short-term relationships between the variables. The volatility of the MASI index will be the subject of a second estimate to capture the shape of the function of its evolution.

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38

Ylisirniö, Arttu, Luis M. F. Barreira, Iida Pullinen, Angela Buchholz, John Jayne, Jordan E. Krechmer, Douglas R. Worsnop, Annele Virtanen, and Siegfried Schobesberger. "On the calibration of FIGAERO-ToF-CIMS: importance and impact of calibrant delivery for the particle-phase calibration." Atmospheric Measurement Techniques 14, no. 1 (January 15, 2021): 355–67. http://dx.doi.org/10.5194/amt-14-355-2021.

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Abstract. The Filter Inlet for Gases and AEROsols (FIGAERO) coupled with a time-of-flight chemical ionization mass spectrometer (ToF-CIMS) enables online measurements of both gas-phase and particle-phase chemical constituents of ambient aerosols. When properly calibrated, the incorporated particle filter collection and subsequent thermal desorption enable the direct measurement of volatility of said constituents. Previously published volatility calibration results however differ from each other significantly. In this study we investigate the reason for this discrepancy. We found a major source of error in the widely used syringe deposition calibration method that can lead to an overestimation of saturation vapour pressures by several orders of magnitude. We propose a new method for volatility calibration by using atomized calibration compounds that more accurately captures the evaporation of chemical constituents from ambient aerosol particles. For example, we found a difference of ∼ 15 ∘C in observed Tmax⁡ values between the atomizer method and syringe method using the lowest solution concentration (0.003 g L−1). This difference translates into a difference of up to 3 orders of magnitude in saturation concentration C∗ space. We justify our claim with evaporation modelling and direct scanning electron microscopy imaging, while also presenting possible error sources of the atomizer method. We finally present how typical calibration parameters derived with both methods impact the volatility basis set (VBS) derived from measurements of secondary organic aerosols (SOAs).

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39

Huang, Wei, Haiyan Li, Nina Sarnela, Liine Heikkinen, Yee Jun Tham, Jyri Mikkilä, Steven J. Thomas, Neil M. Donahue, Markku Kulmala, and Federico Bianchi. "Measurement report: Molecular composition and volatility of gaseous organic compounds in a boreal forest – from volatile organic compounds to highly oxygenated organic molecules." Atmospheric Chemistry and Physics 21, no. 11 (June 14, 2021): 8961–77. http://dx.doi.org/10.5194/acp-21-8961-2021.

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Abstract. The molecular composition and volatility of gaseous organic compounds were investigated during April–July 2019 at the Station for Measuring Ecosystem – Atmosphere Relations (SMEAR) II situated in a boreal forest in Hyytiälä, southern Finland. In order to obtain a more complete picture and full understanding of the molecular composition and volatility of ambient gaseous organic compounds (from volatile organic compounds, VOCs, to highly oxygenated organic molecules, HOMs), two different instruments were used. A Vocus proton-transfer-reaction time-of-flight mass spectrometer (Vocus PTR-ToF; hereafter Vocus) was deployed to measure VOCs and less oxygenated VOCs (i.e., OVOCs). In addition, a multi-scheme chemical ionization inlet coupled to an atmospheric pressure interface time-of-flight mass spectrometer (MION API-ToF) was used to detect less oxygenated VOCs (using Br− as the reagent ion; hereafter MION-Br) and more oxygenated VOCs (including HOMs; using NO3- as the reagent ion; hereafter MION-NO3). The comparison among different measurement techniques revealed that the highest elemental oxygen-to-carbon ratios (O : C) of organic compounds were observed by the MION-NO3 (0.9 ± 0.1, average ± 1 standard deviation), followed by the MION-Br (0.8 ± 0.1); lowest O : C ratios were observed by Vocus (0.2 ± 0.1). Diurnal patterns of the measured organic compounds were found to vary among different measurement techniques, even for compounds with the same molecular formula, suggesting contributions of different isomers detected by the different techniques and/or fragmentation from different parent compounds inside the instruments. Based on the complementary molecular information obtained from Vocus, MION-Br, and MION-NO3, a more complete picture of the bulk volatility of all measured organic compounds in this boreal forest was obtained. As expected, the VOC class was the most abundant (about 53.2 %), followed by intermediate-volatility organic compounds (IVOCs, about 45.9 %). Although condensable organic compounds (low-volatility organic compounds, LVOCs; extremely low volatility organic compounds, ELVOCs; and ultralow-volatility organic compounds, ULVOCs) only comprised about 0.2 % of the total gaseous organic compounds, they play an important role in new particle formation as shown in previous studies in this boreal forest. Our study shows the full characterization of the gaseous organic compounds in the boreal forest and the advantages of combining Vocus and MION API-ToF for measuring ambient organic compounds with different oxidation extents (from VOCs to HOMs). The results therefore provide a more comprehensive understanding of the molecular composition and volatility of atmospheric organic compounds as well as new insights into interpreting ambient measurements or testing/improving parameterizations in transport and climate models.

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40

Harnphattananusorn, Supanee. "Asymmetric Relationship between Exchange Rate Volatility and Oil Price: Case Study of Thai-Baht." International Journal of Energy Economics and Policy 12, no. 1 (January 19, 2022): 86–92. http://dx.doi.org/10.32479/ijeep.11940.

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This paper aims to investigate asymmetric relationship between exchange rate volatility and oil price using a nonlinear auto-regressive distribution Lag (NARDL) developed by Shin et al. (2014). This technique allow us for estimating asymmetric long-run as well as short-run coefficients in a cointegration framework. For exchange rate volatility measurement, GARCH (1,1) model is applied. We use monthly data from January 2000 to June 2021. The results show that there are asymmetric impacts of oil price shocks on Thailand exchange volatility both in the long run and short run.Moreover, both positive and negative shocks on stock price index increase exchange volatility in the short run.

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41

Liang, Zhenjie, Futian Weng, Yuanting Ma, Yan Xu, Miao Zhu, and Cai Yang. "Measurement and Analysis of High Frequency Assert Volatility Based on Functional Data Analysis." Mathematics 10, no. 7 (April 1, 2022): 1140. http://dx.doi.org/10.3390/math10071140.

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Information and communication technology have enabled the collection of high-frequency financial asset time series data. However, the high spatial and temporal resolution nature of these data makes it challenging to compare financial asset characteristics patterns and identify the risk. To address this challenge, a method for realized volatility calculation based on the functional data analysis (FDA) method is proposed. A time–price functional curve is constructed by the functional data analysis method to calculate the realized volatility as the curvature integral of the time–price functional curve. This method could effectively eliminate the interference of market microstructure noise, which could not only allow capital asset price to be decomposed into a continuous term and a noise term by asymptotic convergence, but also could decouple the noise from the discrete-time series. Additionally, it could obtain the value of volatility at any given time, which is no concern about correlations between repeated, mixed frequencies and unequal intervals sampling problems and relaxes the structural constraints and distribution setting of data acquisition. To demonstrate our methods, we analyze a per-second level financial asset dataset. Additionally, sensitivity analysis on the selection of the no equally spaced sample is conducted, and we further add noise to ensure the robustness of our methods and discuss their implications in practice, especially being conducive to more micro analysis of the volatility of the financial market and understanding the rapidly changing changes.

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42

Pei, Ziting, Xuhui Wang, and Xingye Yue. "Risk Measurement by G-Expected Shortfall." Mathematical Problems in Engineering 2021 (April 20, 2021): 1–13. http://dx.doi.org/10.1155/2021/6611237.

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G-expected shortfall (G-ES), which is a new type of worst-case expected shortfall (ES), is defined as measuring risk under infinite distributions induced by volatility uncertainty. Compared with extant notions of the worst-case ES, the G-ES can be computed using an explicit formula with low computational cost. We also conduct backtests for the G-ES. The empirical analysis demonstrates that the G-ES is a reliable risk measure.

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43

Li, Ying, Ulrich Pöschl, and Manabu Shiraiwa. "Molecular corridors and parameterizations of volatility in the chemical evolution of organic aerosols." Atmospheric Chemistry and Physics 16, no. 5 (March 14, 2016): 3327–44. http://dx.doi.org/10.5194/acp-16-3327-2016.

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Abstract. The formation and aging of organic aerosols (OA) proceed through multiple steps of chemical reaction and mass transport in the gas and particle phases, which is challenging for the interpretation of field measurements and laboratory experiments as well as accurate representation of OA evolution in atmospheric aerosol models. Based on data from over 30 000 compounds, we show that organic compounds with a wide variety of functional groups fall into molecular corridors, characterized by a tight inverse correlation between molar mass and volatility. We developed parameterizations to predict the saturation mass concentration of organic compounds containing oxygen, nitrogen, and sulfur from the elemental composition that can be measured by soft-ionization high-resolution mass spectrometry. Field measurement data from new particle formation events, biomass burning, cloud/fog processing, and indoor environments were mapped into molecular corridors to characterize the chemical nature of the observed OA components. We found that less-oxidized indoor OA are constrained to a corridor of low molar mass and high volatility, whereas highly oxygenated compounds in atmospheric water extend to high molar mass and low volatility. Among the nitrogen- and sulfur-containing compounds identified in atmospheric aerosols, amines tend to exhibit low molar mass and high volatility, whereas organonitrates and organosulfates follow high O : C corridors extending to high molar mass and low volatility. We suggest that the consideration of molar mass and molecular corridors can help to constrain volatility and particle-phase state in the modeling of OA particularly for nitrogen- and sulfur-containing compounds.

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44

YANG, CHUNXIA, SEN HU, BINGYING XIA, and RUI WANG. "LONG MEMORY IN STOCK MARKET VOLATILITY: THE INTERNATIONAL EVIDENCE." Modern Physics Letters B 26, no. 20 (July 5, 2012): 1250128. http://dx.doi.org/10.1142/s021798491250128x.

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It is still a hot topic to catch the auto-dependence behavior of volatility. Here, based on the measurement of average volatility, under different observation window size, we investigated the dependence of successive volatility of several main stock indices and their simulated GARCH(1, 1) model, there were obvious linear auto-dependence in the logarithm of volatility under a small observation window size and nonlinear auto-dependence under a big observation. After calculating the correlation and mutual information of the logarithm of volatility for Dow Jones Industrial Average during different periods, we find that some influential events can change the correlation structure and the volatilities of different periods have distinct influence on that of the remote future. Besides, GARCH model could produce similar behavior of dependence as real data and long memory property. But our analyses show that the auto-dependence of volatility in GARCH is different from that in real data, and the long memory is undervalued by GARCH.

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45

Grieshop, A. P., J. M. Logue, N. M. Donahue, and A. L. Robinson. "Laboratory investigation of photochemical oxidation of organic aerosol from wood fires 1: measurement and simulation of organic aerosol evolution." Atmospheric Chemistry and Physics 9, no. 4 (February 18, 2009): 1263–77. http://dx.doi.org/10.5194/acp-9-1263-2009.

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Abstract. Experiments were conducted to investigate the effects of photo-oxidation on organic aerosol (OA) emissions from flaming and smoldering hard- and soft-wood fires under plume-like conditions. This was done by exposing the dilute emissions from a small wood stove to UV light in a smog chamber and measuring the gas- and particle-phase pollutant concentrations with a suite of instruments including a Proton Transfer Reaction Mass Spectrometer (PTR-MS), an Aerosol Mass Spectrometer (AMS) and a thermodenuder. The measurements highlight how atmospheric processing can lead to considerable evolution of the mass and volatility of biomass-burning OA. Photochemical oxidation produced substantial new OA, increasing concentrations by a factor of 1.5 to 2.8 after several hours of exposure to typical summertime hydroxyl radical (OH) concentrations. Less than 20% of this new OA could be explained using a state-of-the-art secondary organic aerosol model and the measured decay of traditional SOA precursors. The thermodenuder data indicate that the primary OA is semivolatile; at 50°C between 50 and 80% of the fresh primary OA evaporated. Aging reduced the volatility of the OA; at 50°C only 20 to 40% of aged OA evaporated. The predictions of a volatility basis-set model that explicitly tracks the partitioning and aging of low-volatility organics was compared to the chamber data. The OA production can be explained by the oxidation of low-volatility organic vapors; the model can also reproduce observed changes in OA volatility and composition. The model was used to investigate the competition between photochemical processing and dilution on OA concentrations in plumes.

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46

Wulandari, Heni Dwi, Mustafid Mustafid, and Hasbi Yasin. "PENERAPAN METODE EXPONENTIALLY WEIGHTED MOVING AVERAGE (EWMA) DALAM PENGUKURAN RISIKO INEVSTASI SAHAM PORTOFOLIO UNTUK VOLATILITAS HETEROGEN." Jurnal Gaussian 7, no. 3 (August 29, 2018): 248–59. http://dx.doi.org/10.14710/j.gauss.v7i3.26658.

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Risk measurement is important in making an investment. One tool used in the measurement of investment risk is Value at Risk (VaR). VaR represents the greatest possible loss of investment with a given period and level of confidence. In the calculation of Value at Risk requires the assumption of normality and homogeneity. However, financial data rarely satisfies that assumption. Exponentially Weighted Moving Average is one method that can be used to overcome the existence of a heterogeneous variant. Daily volatility is calculated using the EWMA method by taking a decay factor of 0.94. VaR portfolio of ASII, BBNI and PTBA stocks is calculated using historical simulation method from the revised portfolio return with Hull and White volatility updating procedure. VaR values obtained are valid at a 99% confidence level based on the validity test of Kupiec PF and Basel rules. Keywords: Value at Risk (VaR), Portfolio, EWMA, Historical Simulation, Volatility Updating

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47

Baule, Rainer, Oliver Entrop, and Sebastian Wessels. "Performance measurement for option portfolios in a stochastic volatility framework." Quantitative Finance 22, no. 3 (December 7, 2021): 519–39. http://dx.doi.org/10.1080/14697688.2021.1985163.

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48

Evans, Greg J. "Measurement and Modeling of Iodine Volatility Above Irradiated Csl Solutions." Nuclear Technology 116, no. 3 (December 1996): 293–305. http://dx.doi.org/10.13182/nt96-a35285.

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49

Metin, Karakas. "Volatility measurement of the world indices using different entropy methods." Thermal Science 23, Suppl. 6 (2019): 1849–61. http://dx.doi.org/10.2298/tsci190130345m.

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In this paper, we show that the application of different entropy methods for world indices. To do this, we use the world indices such as Istanbul Stock Indices (BIST 30), Brazil Index (Bovespa), Germany Index (DAX), Britain Index (FTSE100), South Korea (KOSP?), Japan Index (N?kkei 225), United States Index (SP 500), and China Index (SHANGAI) that have been investigated over all of 8 years (2010-2018). We obtain Shannon, Tsallis, R?nyi and at last the approximate entropy. Consequently, we provide computational results for these entropies for weekly and monthly data.

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50

Xu, Xiaoyu. "Has ESG Performance Reduced Stock Price Volatility." Journal of Innovation and Development 3, no. 1 (May 17, 2023): 59–66. http://dx.doi.org/10.54097/jid.v3i1.8421.

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In the context of sustainable development, it is of practical importance to study whether ESG performance can influence stock price volatility. This paper empirically analyzes the impact of corporate ESG performance on stock price volatility using a sample of A-share listed companies in Shanghai and Shenzhen, China, from 2011-2021. It is found that corporate ESG performance can significantly reduce stock price volatility, and this finding remains robust after controlling for sample selection bias, changing the measurement of key variables, and transforming the empirical model. Mechanistic tests suggest that analyst attention and corporate reputation are potential mechanisms of influence between ESG performance and stock price volatility. Further study finds that investor sentiment contributes to ESG performance in reducing stock price volatility. This paper provides operational evidence for ESG performance to play a positive screening role, stabilize stock markets and promote high-quality development of listed companies.

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