Least squares model fitting to fuzzy vector data
Fuzzy Sets and Systems
Fuzzy data analysis by possibilistic linear models
Fuzzy Sets and Systems - Fuzzy Numbers
Information Sciences: an International Journal
Possibilistic linear systems and their application to the linear regression model
Fuzzy Sets and Systems
Evaluation of fuzzy linear regression models
Fuzzy Sets and Systems
On assessing the H value in fuzzy linear regression
Fuzzy Sets and Systems
Fuzzy linear regression with fuzzy intervals
Fuzzy Sets and Systems
A least-squares approach to fuzzy linear regression analysis
Computational Statistics & Data Analysis
Fuzzy ARIMA model for forecasting the foreign exchange market
Fuzzy Sets and Systems
Fuzzy regression methods—a comparative assessment
Fuzzy Sets and Systems
Hybrid fuzzy least-squares regression analysis and its relibabilty measures
Fuzzy Sets and Systems
Fuzzy regression using asymmetric fuzzy coefficients and fuzzified neural networks
Fuzzy Sets and Systems
A fuzzy linear regression model with better explanatory power
Fuzzy Sets and Systems - Information processing
Regression with fuzzy random data
Computational Statistics & Data Analysis
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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.