| 摘要(英) |
From an economic and financial perspective, data analysis and prediction are always critical and crucial topics. In real application, most models only consider the closing price of stocks, leading to the exclusion of crucial data, such as the highest and lowest prices. Hence, in general, in order to improve parameter estimation and prediction based on the addition crucial data, relied on the Geometric Brownian Motion (GBM) framework, we obtain the likelihood
function of the opening, closing, highest, and lowest prices to estimate the parameter σ2 by employing the reflection principle and the Girsanov theorem. The purpose of this study is to investigate the performance through the Markov Chain Monte Carlo (MCMC) algorithm for parameter estimation and compare it with the maximum likelihood estimation (MLE)
method. Additionally, we use the 95th percentile credible interval to assess the suitability of the algorithm for the proposed model in the simulation study and to compare the simulation results of each model using the relative error (RE) measure. Finally, in the empirical analysis, the proposed method demonstrates a strong track record in applying symbolic data to real-world data for the S&P 500 index. |
| 參考文獻 |
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