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
相关论文
共 50 条
  • [31] Irregularity, volatility, risk, and financial market time series
    Pincus, S
    Kalman, RE
    PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2004, 101 (38) : 13709 - 13714
  • [32] Volatility forecasting of financial time series using wavelet based exponential generalized autoregressive conditional heteroscedasticity model
    Mohammed, Siti Aisyah
    Abu Bakar, Mohd Aftar
    Ariff, Noratiqah Mohd
    COMMUNICATIONS IN STATISTICS-THEORY AND METHODS, 2020, 49 (01) : 178 - 188
  • [33] A novel intuitionistic fuzzy time series prediction model with cascaded structure for financial time series
    Yolcu, Ozge Cagcag
    Yolcu, Ufuk
    EXPERT SYSTEMS WITH APPLICATIONS, 2023, 215
  • [34] Financial time series prediction using artificial intelligence techniques
    Castillo, O
    Melin, P
    ZEITSCHRIFT FUR ANGEWANDTE MATHEMATIK UND MECHANIK, 1996, 76 : 393 - 394
  • [35] Modelling fluctuations of financial time series: from cascade process to stochastic volatility model
    Muzy, JF
    Delour, J
    Bacry, E
    EUROPEAN PHYSICAL JOURNAL B, 2000, 17 (03): : 537 - 548
  • [36] Financial Time Series Prediction Using Spiking Neural Networks
    Reid, David
    Hussain, Abir Jaafar
    Tawfik, Hissam
    PLOS ONE, 2014, 9 (08):
  • [37] Modelling fluctuations of financial time series: from cascade process to stochastic volatility model
    J.F. Muzy
    J. Delour
    E. Bacry
    The European Physical Journal B - Condensed Matter and Complex Systems, 2000, 17 : 537 - 548
  • [38] Financial Time Series Prediction Using Exogenous Series and Combined Neural Networks
    Amorim Neto, Manoel C.
    Calvalcanti, George D. C.
    Ren, Tsang Ing
    IJCNN: 2009 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1- 6, 2009, : 2578 - 2585
  • [39] Time-series prediction of hourly atmospheric pressure using ANFIS and LSTM approaches
    Mehmet Bilgili
    Akın Ilhan
    Şaban Ünal
    Neural Computing and Applications, 2022, 34 : 15633 - 15648
  • [40] Modelling and Prediction of Financial Time Series
    Bingham, N. H.
    COMMUNICATIONS IN STATISTICS-THEORY AND METHODS, 2014, 43 (07) : 1351 - 1361