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FORECASTING LONG-MEMORY VOLATILITY OF THE AUSTRALIAN FUTURES MARKET

Seong-Min Yoon (), Sang Hoon Kang, Sung-Jin Cho, Gyun Woo and Jeong-Hoon Ji
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Sang Hoon Kang: Gyeongsang National University
Sung-Jin Cho: Pukyong National University
Gyun Woo: Pusan National University
Jeong-Hoon Ji: Pusan National University

Theoretical and Applied Economics, 2009, vol. 12(541)(supplement), issue 12(541)(supplement), 763-770

Abstract: Accurate forecasting of volatility is of considerable interest in financial volatility research, particularly in regard to portfolio allocation, option pricing, and risk management. This article investigates and compares the ability to conduct one-day-ahead volatility forecasts in the Australian index futures market by three volatility models: GARCH, IGARCH and FIGARCH. The FIGARCH model better captured the long-memory property than did the GARCH and IGARCH models. Additionally, the FIGARCH model provided superior performance in one-day-ahead volatility forecasts. As discussed in this paper, the FIGARCH model should prove useful in forecasting the long-memory property in the Australian index futures market.

Keywords: diebold-mariano test; forecasting ability; long memory; lo's modified R/S analysis; SPI futures. (search for similar items in EconPapers)
Date: 2009
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