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1

Hodgson, Allan. Volatility and index futures options. [s.l.]: [s.n.], 1988.

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2

Cassese, Gianluca. Modelling the MIB30 implied volatility surface: Does efficiency matter? [St. Louis, Mo.]: Federal Reserve Bank of St. Louis, 2005.

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3

Gonçalves, Silva. Predictable dynamics in the S&P 500 index options implied volatility surface. [St. Louis, Mo.]: Federal Reserve Bank of St. Louis, 2005.

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4

Engle, R. F. Index-option pricing with stochastic volatility and the value of accurate variance forecasts. Cambridge, MA: National Bureau of Economic Research, 1993.

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5

Schwert, G. William. Stock volatility in the new millennium: How wacky is Nasdaq? Cambridge, MA: National Bureau of Economic Research, 2001.

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6

MacDonald, Ronald. Stock prices, dividends, efficiency and excessive volatility: Some evidence for the FT ordinary share index. Aberdeen: University of Aberdeen. Department of Economics, 1987.

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7

Thosar, Satish. Increased stock volatility and excess returns in the index futures trading era: New evidence from additions to the S&P500 index. Boston, MA: Boston University, School of Management, 1992.

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8

Augen, Jeffrey. The volatility edge in options trading: New technical strategies for trading equity and index options in unstable markets. Upper Saddle River, N.J: FT Press, 2008.

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9

Giblin, Paul R. The impact of volatility on the levels of basis, open interest and volume in the FTSE 100 index futures market. Dublin: Universitry College Dublin, 1995.

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10

United States. Congress. House. Committee on Agriculture. Subcommittee on Conservation, Credit, and Rural Development. Review of recent volatility in the stock market and the stock index futures markets: Hearing before the Subcommittee on Conservation, Credit, and Rural Development of the Committee on Agriculture, House of Representatives, One Hundredth Congress, first session, November 4, 1987. Washington: U.S. G.P.O., 1988.

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11

United States. Congress. House. Committee on Agriculture. Subcommittee on Conservation, Credit, and Rural Development. Review of recent volatility in the stock market and the stock index futures markets: Hearing before the Subcommittee on Conservation, Credit, and Rural Development of the Committee on Agriculture, House of Representatives, One Hundredth Congress, first session, November 4, 1987. Washington: U.S. G.P.O., 1988.

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12

CBOE 250 futures: Asset allocation : restructuring portfolio risk. [Chicago]: The Board :the Chicago Board Options Exchange, 1988.

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13

CBOE 250 futures: Index characteristics and composition : futures contract terms and uses. [Chicago]: The Board, 1988.

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14

Library of Congress. Congressional Research Service, ed. Program trading: Efficiency and volatility in the securities markets. Washington, D.C: Library of Congress, Congressional Research Service, 1987.

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15

Program trading: Efficiency and volatility in the securities markets. Washington, D.C: Library of Congress, Congressional Research Service, 1987.

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16

Larson, Donald F. Food Prices and Food Price Volatility. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190656010.003.0022.

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This chapter examines food prices from 1900 to 2015. Despite growing populations, rising incomes, new technologies, globalization, and the emergence of commodities as an asset class, no trends are evident in food price levels or volatility. Still, food prices have averaged higher since 2010, harming the poor and raising fears that agricultural productivity growth has slowed. Consistently since 1900, food prices have been more volatile than the prices of manufactured goods and most other commodity groups. This relation drives terms-of-trade volatility, which slows economic growth. At the farm level, price volatility impedes investment and technology adoption, and encourages low-income livelihood strategies. Past policies to manage food prices have not worked and governments have shifted to policies aimed at mitigating the consequences of high and volatile food prices. Extending the reach of risk markets, warehouse receipt systems, index insurance, and contract farming can be useful policy components.
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17

VIX Index and Volatility-Based Global Indexes and Trading Instruments: A Guide to Investment and Trading Features. CFA Institute Research Foundation, 2020.

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18

VIX Index and Volatility-Based Global Indexes and Trading Instruments: A Guide to Investment and Trading Features. CFA Institute Research Foundation, 2020.

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19

Auinger, Florian. The Causal Relationship between the S&P 500 and the VIX Index: Critical Analysis of Financial Market Volatility and Its Predictability. Springer Gabler, 2015.

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20

D, Reams Bernard, ed. The Stock market crash of October 1987: Federal documents and materials on the volatility of the stock market and stock index futures markets. Buffalo, N.Y: W.S. Hein, 1988.

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21

Reams, Bernard D. The Stock Market Crash of October, 1987: Federal Documents and Materials on the Volatility of the Stock Market and Stock Index Futures Markets, Set. William S. Hein & Company, 1994.

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22

Quintana, José Mario, Carlos Carvalho, James Scott, and Thomas Costigliola. Extracting S&P500 and NASDAQ Volatility: The Credit Crisis of 2007–2008. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.13.

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This article demonstrates the utility of Bayesian modelling and inference in financial market volatility analysis, using the 2007-2008 credit crisis as a case study. It first describes the applied problem and goal of the Bayesian analysis before introducing the sequential estimation models. It then discusses the simulation-based methodology for inference, including Markov chain Monte Carlo (MCMC) and particle filtering methods for filtering and parameter learning. In the study, Bayesian sequential model choice techniques are used to estimate volatility and volatility dynamics for daily data for the year 2007 for three market indices: the Standard and Poor’s S&P500, the NASDAQ NDX100 and the financial equity index called XLF. Three models of financial time series are estimated: a model with stochastic volatility, a model with stochastic volatility that also incorporates jumps in volatility, and a Garch model.
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