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A comparative assessment of different fuzzy regression methods for volatility forecasting

Silvia Muzzioli and Bernard De Baets (UGent)
(2013) FUZZY OPTIMIZATION AND DECISION MAKING. 12(4). p.433-450
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Abstract
The aim of this paper is to compare different fuzzy regression methods in the assessment of the information content on future realised volatility of option-based volatility forecasts. These methods offer a suitable tool to handle both imprecision of measurements and fuzziness of the relationship among variables. Therefore, they are particularly useful for volatility forecasting, since the variable of interest (realised volatility) is unobservable and a proxy for it is used. Moreover, measurement errors in both realised volatility and volatility forecasts may affect the regression results. We compare both the possibilistic regression method of Tanaka et al. (IEEE Trans Syst Man Cybern 12:903-907, 1982) and the least squares fuzzy regression method of Savic and Pedrycz (Fuzzy Sets Syst 39:51-63, 1991). In our case study, based on intra-daily data of the DAX-index options market, both methods have proved to have advantages and disadvantages. Overall, among the two methods, we prefer the Savic and Pedrycz (Fuzzy Sets Syst 39:51-63, 1991) method, since it contains as special case (the central line) the ordinary least squares regression, is robust to the analysis of the variables in logarithmic terms or in levels, and provides sharper results than the Tanaka et al. (IEEE Trans Syst Man Cybern 12:903-907, 1982) method.
Keywords
Linear programming, Fuzzy regression methods, Least squares, Volatility forecasting, LINEAR-REGRESSION, LEAST-SQUARES, INTEREST-RATES, TERM STRUCTURE, MODEL, MARKET

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MLA
Muzzioli, Silvia, and Bernard De Baets. “A Comparative Assessment of Different Fuzzy Regression Methods for Volatility Forecasting.” FUZZY OPTIMIZATION AND DECISION MAKING, vol. 12, no. 4, 2013, pp. 433–50, doi:10.1007/s10700-013-9161-1.
APA
Muzzioli, S., & De Baets, B. (2013). A comparative assessment of different fuzzy regression methods for volatility forecasting. FUZZY OPTIMIZATION AND DECISION MAKING, 12(4), 433–450. https://doi.org/10.1007/s10700-013-9161-1
Chicago author-date
Muzzioli, Silvia, and Bernard De Baets. 2013. “A Comparative Assessment of Different Fuzzy Regression Methods for Volatility Forecasting.” FUZZY OPTIMIZATION AND DECISION MAKING 12 (4): 433–50. https://doi.org/10.1007/s10700-013-9161-1.
Chicago author-date (all authors)
Muzzioli, Silvia, and Bernard De Baets. 2013. “A Comparative Assessment of Different Fuzzy Regression Methods for Volatility Forecasting.” FUZZY OPTIMIZATION AND DECISION MAKING 12 (4): 433–450. doi:10.1007/s10700-013-9161-1.
Vancouver
1.
Muzzioli S, De Baets B. A comparative assessment of different fuzzy regression methods for volatility forecasting. FUZZY OPTIMIZATION AND DECISION MAKING. 2013;12(4):433–50.
IEEE
[1]
S. Muzzioli and B. De Baets, “A comparative assessment of different fuzzy regression methods for volatility forecasting,” FUZZY OPTIMIZATION AND DECISION MAKING, vol. 12, no. 4, pp. 433–450, 2013.
@article{4207874,
  abstract     = {{The aim of this paper is to compare different fuzzy regression methods in the assessment of the information content on future realised volatility of option-based volatility forecasts. These methods offer a suitable tool to handle both imprecision of measurements and fuzziness of the relationship among variables. Therefore, they are particularly useful for volatility forecasting, since the variable of interest (realised volatility) is unobservable and a proxy for it is used. Moreover, measurement errors in both realised volatility and volatility forecasts may affect the regression results. We compare both the possibilistic regression method of Tanaka et al. (IEEE Trans Syst Man Cybern 12:903-907, 1982) and the least squares fuzzy regression method of Savic and Pedrycz (Fuzzy Sets Syst 39:51-63, 1991). In our case study, based on intra-daily data of the DAX-index options market, both methods have proved to have advantages and disadvantages. Overall, among the two methods, we prefer the Savic and Pedrycz (Fuzzy Sets Syst 39:51-63, 1991) method, since it contains as special case (the central line) the ordinary least squares regression, is robust to the analysis of the variables in logarithmic terms or in levels, and provides sharper results than the Tanaka et al. (IEEE Trans Syst Man Cybern 12:903-907, 1982) method.}},
  author       = {{Muzzioli, Silvia and De Baets, Bernard}},
  issn         = {{1568-4539}},
  journal      = {{FUZZY OPTIMIZATION AND DECISION MAKING}},
  keywords     = {{Linear programming,Fuzzy regression methods,Least squares,Volatility forecasting,LINEAR-REGRESSION,LEAST-SQUARES,INTEREST-RATES,TERM STRUCTURE,MODEL,MARKET}},
  language     = {{eng}},
  number       = {{4}},
  pages        = {{433--450}},
  title        = {{A comparative assessment of different fuzzy regression methods for volatility forecasting}},
  url          = {{http://doi.org/10.1007/s10700-013-9161-1}},
  volume       = {{12}},
  year         = {{2013}},
}
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