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Research Papers

A New Approach for Forecasting the Price Range With Financial Interval-Valued Time Series Data

[+] Author and Article Information
Wei Yang

School of Mathematical Science, Institute of Management and Decision, Shanxi University, Taiyuan, Shanxi 030006, Chinae-mail: yangwei@sxu.edu.cn

Ai Han

Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, Chinae-mail: hanai@amss.ac.cn

1Corresponding author.

Manuscript received August 30, 2014; final manuscript received January 15, 2015; published online April 20, 2015. Assoc. Editor: Athanasios Pantelous.

ASME J. Risk Uncertainty Part B 1(2), 021004 (Apr 20, 2015) (8 pages) Paper No: RISK-14-1044; doi: 10.1115/1.4029751 History: Received August 30, 2014; Accepted February 05, 2015; Online April 20, 2015

This paper proposes an interval-based methodology to model and forecast the price range or range-based volatility process of financial asset prices. Comparing with the existing volatility models, the proposed model utilizes more information contained in the interval time series than using the range information only or modeling the high and low price processes separately. An empirical study of the U.S. stock market daily data shows that the proposed interval-based model produces more accurate range forecasts than the classic point-based linear models for range process, in terms of both in-sample and out-of-sample forecasts. The statistical tests show that the forecasting advantages of the interval-based model are statistically significant in most cases. In addition, some stability tests have been conducted for ascertaining the advantages of the interval-based model through different sample windows and forecasting periods, which reveals similar results. This study provides a new interval-based perspective for volatility modeling and forecasting of financial time series data.

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Grahic Jump Location
Fig. 1

Daily lows, highs, and ranges of the interval-valued S&P500 price index

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