Evaluating Volatility Using an ANFIS Model for Financial Time Series Prediction

Johanna M. Orozco-Castañeda (), Sebastián Alzate-Vargas and Danilo Bedoya-Valencia
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Johanna M. Orozco-Castañeda: Instituto de Matemáticas, Universidad de Antioquia, Calle 67 No. 53-108, Medellín 050010, Colombia
Sebastián Alzate-Vargas: Departamento de Ciencias Matemáticas, Universidad de Puerto Rico Recinto Mayagüez, Mayagüez P.O. Box 9000, Puerto Rico
Danilo Bedoya-Valencia: Independent Researcher, Medellín 050021, Colombia

Risks, 2024, vol. 12, issue 10, 1-15

Abstract: This paper develops and implements an Autoregressive Integrated Moving Average model with an Adaptive Neuro-Fuzzy Inference System (ARIMA-ANFIS) for BTCUSD price prediction and risk assessment. The goal of these forecasts is to identify patterns from past data and achieve an understanding of the future behavior of the price and its volatility. The proposed ARIMA-ANFIS model is compared with a benchmark ARIMA-GARCH model. To evaluated the adequacy of the models in terms of risk assessment, we compare the confidence intervals of the price and accuracy measures for the testing sample. Additionally, we implement the diebold and Mariano test to compare the accuracy of the two volatility forecasts. The results revealed that each volatility model focuses on different aspects of the data dynamics. The ANFIS model, while effective in certain scenarios, may expose one to unexpected risks due to its underestimation of volatility during turbulent periods. On the other hand, the GARCH(1,1) model, by producing higher volatility estimates, may lead to excessive caution, potentially reducing returns.

Keywords: optimization; dynamic systems; data modeling; forecasting; time series; fuzzy systems; soft computing; adaptive systems (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2024
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