The Experts below are selected from a list of 320598 Experts worldwide ranked by ideXlab platform
Ehud I Ronn - One of the best experts on this subject based on the ideXlab platform.
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computing the market price of Volatility risk in the energy commodity markets
Journal of Banking and Finance, 2008Co-Authors: James S Doran, Ehud I RonnAbstract:In this paper, we demonstrate the need for a negative market price of Volatility risk to recover the difference between Black-Scholes [Black, F., Scholes, M., 1973. The pricing of options and corporate liabilities. Journal of Political Economy 81, 637-654]/Black [Black, F., 1976. Studies of stock price Volatility changes. In: Proceedings of the 1976 Meetings of the Business and Economics Statistics Section, American Statistical Association, pp. 177-181] implied Volatility and realized-term Volatility. Initially, using quasi-Monte Carlo simulation, we demonstrate numerically that a negative market price of Volatility risk is the key risk premium in explaining the disparity between risk-neutral and statistical Volatility in both equity and commodity-energy markets. This is robust to multiple specifications that also incorporate jumps. Next, using futures and options data from natural gas, heating oil and crude oil contracts over a 10Â year period, we estimate the Volatility risk premium and demonstrate that the premium is negative and significant for all three commodities. Additionally, there appear distinct seasonality patterns for natural gas and heating oil, where winter/withdrawal months have higher Volatility risk premiums. Computing such a negative market price of Volatility risk highlights the importance of Volatility risk in understanding priced Volatility in these financial markets.
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computing the market price of Volatility risk in the energy commodity markets
Social Science Research Network, 2004Co-Authors: James S Doran, Ehud I RonnAbstract:In this paper we demonstrate the need for a negative market price of Volatility risk to recover the difference between Black-Scholes (1973)/Black (1976) implied Volatility and realized term Volatility. Initially, using quasi-Monte Carlo simulation, we demonstrate numerically that a negative market price of Volatility risk is the key risk premium in explaining the disparity between risk-neutral and statistical Volatility in both equity and commodity-energy markets. This is robust to multiple specifications that also incorporate jumps. Next, using futures and options data from natural gas, heating oil and crude oil contracts over a ten year period, we estimate the Volatility risk premium and demonstrate that the premium is negative and significant for all three commodities. Additionally, there appear distinct seasonality patterns for natural gas and heating oil, where winter/withdrawal months have higher Volatility risk premiums. Computing such a negative market price of Volatility risk highlights the importance of Volatility risk in understanding priced Volatility in these financial markets.
Robert E Whaley - One of the best experts on this subject based on the ideXlab platform.
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market Volatility prediction and the efficiency of the s p 100 index option market
Journal of Financial Economics, 1992Co-Authors: Campbell R Harvey, Robert E WhaleyAbstract:Abstract Most models of market Volatility use either past returns or ex post Volatility to forecast Volatility. In this paper, the dynamic behavior of market Volatility is assessed by forecasting the Volatility implied in the transaction prices of Standard & Poor's 100 index options. We test and reject the hypothesis that Volatility changes are unpredictable. However, while our statistical model delivers precise forecasts, abnormal returns are not possible in a trading strategy (based on daily out-of-sample Volatility projections) which takes transaction costs into account, suggesting that predictable time-varying Volatility is consistent with market efficiency.
James S Doran - One of the best experts on this subject based on the ideXlab platform.
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computing the market price of Volatility risk in the energy commodity markets
Journal of Banking and Finance, 2008Co-Authors: James S Doran, Ehud I RonnAbstract:In this paper, we demonstrate the need for a negative market price of Volatility risk to recover the difference between Black-Scholes [Black, F., Scholes, M., 1973. The pricing of options and corporate liabilities. Journal of Political Economy 81, 637-654]/Black [Black, F., 1976. Studies of stock price Volatility changes. In: Proceedings of the 1976 Meetings of the Business and Economics Statistics Section, American Statistical Association, pp. 177-181] implied Volatility and realized-term Volatility. Initially, using quasi-Monte Carlo simulation, we demonstrate numerically that a negative market price of Volatility risk is the key risk premium in explaining the disparity between risk-neutral and statistical Volatility in both equity and commodity-energy markets. This is robust to multiple specifications that also incorporate jumps. Next, using futures and options data from natural gas, heating oil and crude oil contracts over a 10Â year period, we estimate the Volatility risk premium and demonstrate that the premium is negative and significant for all three commodities. Additionally, there appear distinct seasonality patterns for natural gas and heating oil, where winter/withdrawal months have higher Volatility risk premiums. Computing such a negative market price of Volatility risk highlights the importance of Volatility risk in understanding priced Volatility in these financial markets.
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computing the market price of Volatility risk in the energy commodity markets
Social Science Research Network, 2004Co-Authors: James S Doran, Ehud I RonnAbstract:In this paper we demonstrate the need for a negative market price of Volatility risk to recover the difference between Black-Scholes (1973)/Black (1976) implied Volatility and realized term Volatility. Initially, using quasi-Monte Carlo simulation, we demonstrate numerically that a negative market price of Volatility risk is the key risk premium in explaining the disparity between risk-neutral and statistical Volatility in both equity and commodity-energy markets. This is robust to multiple specifications that also incorporate jumps. Next, using futures and options data from natural gas, heating oil and crude oil contracts over a ten year period, we estimate the Volatility risk premium and demonstrate that the premium is negative and significant for all three commodities. Additionally, there appear distinct seasonality patterns for natural gas and heating oil, where winter/withdrawal months have higher Volatility risk premiums. Computing such a negative market price of Volatility risk highlights the importance of Volatility risk in understanding priced Volatility in these financial markets.
Campbell R Harvey - One of the best experts on this subject based on the ideXlab platform.
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market Volatility prediction and the efficiency of the s p 100 index option market
Journal of Financial Economics, 1992Co-Authors: Campbell R Harvey, Robert E WhaleyAbstract:Abstract Most models of market Volatility use either past returns or ex post Volatility to forecast Volatility. In this paper, the dynamic behavior of market Volatility is assessed by forecasting the Volatility implied in the transaction prices of Standard & Poor's 100 index options. We test and reject the hypothesis that Volatility changes are unpredictable. However, while our statistical model delivers precise forecasts, abnormal returns are not possible in a trading strategy (based on daily out-of-sample Volatility projections) which takes transaction costs into account, suggesting that predictable time-varying Volatility is consistent with market efficiency.
Jeffrey G. Williamson - One of the best experts on this subject based on the ideXlab platform.
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commodity price Volatility and world market integration since 1700
The Review of Economics and Statistics, 2011Co-Authors: David S Jacks, Kevin H Orourke, Jeffrey G. WilliamsonAbstract:Abstract Poor countries are more volatile than rich countries, and this Volatility impedes their growth. Furthermore, commodity prices are a key source of that Volatility. This paper explores price Volatility since 1700 to offer three stylized facts: commodity price Volatility has not increased over time, commodities have always shown greater price Volatility than manufactures, and world market integration breeds less commodity price Volatility. Thus, economic isolation is associated with much greater commodity price Volatility, while world market integration is associated with less.
