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Forecasting volatilities in equity, bond and money markets: A market-based approach

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  • Kent Wang

    (Wang Yanan Institute for Studies in Economics, Xiamen University and UQ Business School, The University of Queensland, k.wang@business.uq.edu.au)

Abstract

This study examines the forecasting power of the most popular volatility forecasting models in the S&P 500 index market, the Eurodollar futures market, and the 30-year US T-Bond futures market at a daily level using a market-based option-pricing error approach. Comparison has been made between two methods including and excluding implied volatility in option-pricing error approach in forecasting next-day volatilities. To remove any advantage to option-implied volatility, the analysis is performed in two steps. Spurious regression biases and biases in the measurement of volatility forecasts are controlled for.The evidence from this paper supports the use of implied volatility as a proxy for market volatility, as it works best in forecasting next-day realized volatility in all the three US markets. The appropriateness of including implied volatility in option-pricing error approach is also discussed.

Suggested Citation

  • Kent Wang, 2010. "Forecasting volatilities in equity, bond and money markets: A market-based approach," Australian Journal of Management, Australian School of Business, vol. 35(2), pages 165-180, August.
    • Handle: RePEc:sae:ausman:v:35:y:2010:i:2:p:165-180
    DOI: 10.1177/0312896210370080
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    References listed on IDEAS

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    Cited by:

    1. Zheyao Pan, 2018. "A state‐price volatility index for the U.S. government bond market," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 58(S1), pages 573-597, November.
    2. Robert E. Marks, 2010. "Editorial: A final farewell," Australian Journal of Management, Australian School of Business, vol. 35(2), pages 115-117, August.
    3. repec:wyi:journl:002192 is not listed on IDEAS
    4. Jamie Alcock & Godfrey Smith, 2017. "Non-parametric American option valuation using Cressie–Read divergences," Australian Journal of Management, Australian School of Business, vol. 42(2), pages 252-275, May.
    5. Michael O'Neill & Gulasekaran Rajaguru, 2020. "A response surface analysis of critical values for the lead‐lag ratio with application to high frequency and non‐synchronous financial data," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 60(4), pages 3979-3990, December.
    6. Kent Wang & Yuqiang Guo, 2014. "Predictability of time-varying jump premiums: Evidence based on calibration," Australian Journal of Management, Australian School of Business, vol. 39(3), pages 369-394, August.

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