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CORRELATION FOR ESTIMATING VOLATILE MATTER OF COALS
Chemical Engineering Communications, 2001Abstract A set of 460 Indian coal samples has been used to establish correlations between volatile matter percentage yield (%VMdmmr) and compositional parameters, such as the weight percentages of Carbon (Cw), Hydrogen (Hw) and Oxygen (Ow); atomic ratios of the H/C, O/C and H/C; and atomic percentages of Carbon (Ca), Hydrogen (Ha) and Oxygen (Oa ...
Kedar Prasad Singh
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Implications of exchange rate volatility for trade: Volatility measurement matters
Review of International Economics, 2021AbstractThe relationship between exchange rate volatility and trade is inconclusive in the current literature. I find evidence this is due to variation in definitions of exchange rate volatility. My results suggest common volatility measures weigh variation differently over time and economic conditions.
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Combustion of Volatile Matter in Fluidized Beds
Industrial & Engineering Chemistry Research, 1980The change of the volatiles burn-out time has been investigated for different particle sizes, superficial velocities, volatile matter content and masses charged. The results have shown that the volatiles take about 3–12 sec to burn for the particle size(1.52–3.06mm) and velocity range (0.38–0.58m/s) studied.
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Volatility Forecasts: Do Volatility Estimators and Evaluation Methods Matter?
Journal of Futures Markets, 2013This study investigates the volatility forecasting abilities of return‐based and range‐based estimators for two stock indices and two individual stocks in the U.S. stock market. The forecasting performances are evaluated by two robust statistical loss functions, and further by financial applications in risk management and option pricing.
Jiang, I-Ming +2 more
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Volatility forecasting: the jumps do matter [PDF]
, 2008 This study reconsiders the role of jumps for volatility forecasting by showing that jumps have positive and mostly significant impact on future volatility. This result becomes apparent once volatility is correctly separated into its continuous and discontinuous component. To this purpose, we introduce the concept of threshold multipower variation (TMPV)
Fulvio Corsi +2 more
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The Journal of Trading, 2008 The disposition effect is the well-known tendency to ride losers and sell winners. A panel of individual investor trading records allows examination of the differences in price, volume, and volatility attributable to the disposition effect. When disposition-prone investors increase their holdings in a stock, the volatility, volume, and the return of ...
William N. Goetzmann, Massimo Massa
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Risk and Return in Stochastic Volatility Models: Volatility Feedback Matters!
SSRN Electronic Journal, 2007We develop a model of stock return volatility that includes a positive risk-return relation, a significant volatility feedback effect and explains a rich set of empirical phenomena. It explains the negative correlation between returns and volatility we observe in stock data.
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Determination of dry matter and volatiles in silage
Journal of the Science of Food and Agriculture, 1960AbstractAn apparatus designed to collect the volatile constituents produced during the drying of foods has been used to investigate the losses of volatiles from silages dried at 100°. In 28 silages examined the mean volatility of acetic acid was 87‐9% and of butyric acid 89‐4%.
P. McDonald, W. A. Dewar
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Should Stochastic Volatility Matter to the Cost‐Constrained Investor?
Mathematical Finance, 2003Significant strides have been made in the development of continuous‐time portfolio optimization models since Merton (1969). Two independent advances have been the incorporation of transaction costs and time‐varying volatility into the investor's optimization problem. Transaction costs generally inhibit investors from trading too often.
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Determination of combustible volatile matter in fuels
Fuel, 1987Abstract A method for determination of combustible volatile matter by flash-pyrolysis has been developed. The analysis is applicable to fuels with H C values ranging from 0.2 to 2.05. This range includes hard coals, lignites, peats, biomass and fuel oil. The precision is similar to traditional gas chromatography, i.e. 6–8%.
Göran Eklund +2 more
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