Evaluating Volatility Using an ANFIS Model for Financial Time Series Prediction

被引:0
|
作者
Orozco-Castaneda, Johanna M. [1 ]
Alzate-Vargas, Sebastian [2 ]
Bedoya-Valencia, Danilo
机构
[1] Univ Antioquia, Inst Matemat, Calle 67 53-108, Medellin 050010, Colombia
[2] Univ Puerto Rico Recinto Mayaguez, Dept Ciencias Matemat, POB 9000, Mayaguez, PR USA
关键词
optimization; dynamic systems; data modeling; forecasting; time series; fuzzy systems; soft computing; adaptive systems; FUZZY; ANN;
D O I
10.3390/risks12100156
中图分类号
F8 [财政、金融];
学科分类号
0202 ;
摘要
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.
引用
收藏
页数:15
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