博碩士論文 110225023 詳細資訊




以作者查詢圖書館館藏 以作者查詢臺灣博碩士 以作者查詢全國書目 勘誤回報 、線上人數:8 、訪客IP:44.222.64.76
姓名 陳泓凱(Hong-Kai Chen)  查詢紙本館藏   畢業系所 統計研究所
論文名稱 基於貝氏之財務區間時間序列的建模方法
(A Bayesian-based approach for modeling financial interval time series)
相關論文
★ Credit Risk Illustrated under Coupled diffusions★ The analysis of log returns using copula-based Markov models
★ Systemic risk with relative behavior★ 在厚尾分配下的均值收斂交易策略
★ Comparison of Credit Risk in Coupled Diffusion Model and Merton′s Model★ Estimation in copula-based Markov mixture normal model
★ 金融系統性風險的回顧分析★ New insights on ′′A semi-parametric model for wearable sensor-based physical activity monitoring data with informative device wear"
★ A parametric model for wearable sensor-based physical activity monitoring data with informative device wear★ Optimal Asset Allocation using Black-Litterman with Smooth Transition Model
★ VIX Index Analysis using Copula-Based Markov Chain Models★ 使用雙重指數平滑預測模型及無母數容忍限的配對交易策略
★ Intraday Pairs Trading on Taiwan Semiconductor Companies through Mean Reverting Processes★ Target index tracing through portfolio optimization
★ Estimation in Copula-Based Markov Models under Weibull Distributions★ Optimal Strategies for Index Tracking with Risky Constrains
檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   [檢視]  [下載]
  1. 本電子論文使用權限為同意立即開放。
  2. 已達開放權限電子全文僅授權使用者為學術研究之目的,進行個人非營利性質之檢索、閱讀、列印。
  3. 請遵守中華民國著作權法之相關規定,切勿任意重製、散佈、改作、轉貼、播送,以免觸法。

摘要(中) 從經濟和金融的觀點來看,數據分析和預測一直是關鍵和重要的議題。在實際應用中,大多數模型只考慮股票的收盤價,導致關鍵數據(如最高價和最低價)被排除在外。因此,為了改善基於關鍵數據的參數估計和預測,我們依賴於幾何布朗運動(GBM)框架,利用開盤價、收盤價、最高價和最低價的概似函數來估計參數σ2,並運用反射原理和Girsanov定理。本研究旨在通過馬可夫鏈蒙特卡羅(MCMC)演算法對參數進行估計,並將其與最大概似估計(MLE)方法進行比較,同時使用95-分位數可信區間來評估該演算法在模擬研究中對所提出模型的適用性,並使用相對誤差(RE)指標比較模擬結果。最後,在實證分析中,所提出的方法在將符號數據應用於標準普爾500指數 (S&P 500)的真實數據方面展現了良好的表現。
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.
關鍵字(中) ★ 可信區間
★ 馬可夫鏈蒙地卡羅
★ 最大概似估計方法
★ 標準普爾500指數
關鍵字(英) ★ credible interval
★ Markov Chain Monte Carlo
★ Maximum likelihood estimation
★ S&P 500 index
論文目次 1 Introduction . . . . . . . . . . 1
2 Method and model . . . . . . . . 4
2.1 Model . . . . . . . . . . . . . 4
2.2 Method . . . . . . . . . . . . . 7
2.3 One-step-ahead prediction . . . . 8
3 Simulations . . . . . . . . . . . . 9
4 Empirical studies . . . . . . . . . 16
5 Conclusion and future extensions . . . 22
5.1 Conclusion . . . . . . . . . . . . . 22
5.2 Future extensions . . . . . . . . . . 22
5.2.1 Asymmetry model study . . . . . . . . 22
5.2.2 Change point detection . . . . . . . . 23
A Proofs . . . . . . . . . . . . . . . . . . 24
A.1 Proof of Remark 1 . . . . . . . . . . . . 24
A.2 Proof of Remark 2 . . . . . . . . . . . . 24
A.3 Proof of Remark 3 . . . . . . . . . . . . 26
A.4 Proof of Remark 4 . . . . . . . . . . . . 26
A.5 Proof of Proposition 1 . . . . . . . . . . 27
B Figures for Simulation Studies . . . . . . . 30
C Figures for Empirical Studies . . . . . . . . 36
D Codes . . . . . . . . . . . . . . . . . . . . 39
D.1 Simulation Studies . . . . . . . . . . . . . 39
D.2 Empirical Studies . . . . . . . . . . . . . . 46
Reference . . . . . . . . . . . . . . . . . . . . 52
參考文獻 [1] Lin, E.M.H., Chen, C.W.S., Gerlach, R. (2012) Forecasting volatility with asymmetric smooth transition dynamic range models, International Journal of Forecasting, 28, 384–399.
[2] Gerlach, R. and Chen, C.W.S. (2016) Bayesian expected shortfall forecasting incorporating the intraday range, Journal of Financial Econometrics, 14, 128-158.
[3] Chen, C.W.S., Gerlach, R., Hwang, RBK, and McAleer, M (2012) Forecasting Value-atRisk using nonlinear regression quantiles and the intra-day range, International Journal of Forecasting, 28, 557–574.
[4] Billard, L. and Diday, E. (2003). From the Statistics of Data to the Statistics of Knowledge: Symbolic Data Analysis. Journal of the American Statistical Association. 98, 470–487. https://doi.org/10.1198/016214503000242.
[5] Billard, L. and Diday, E. (2006). Symbolic Data Analysis: Conceptual Statistics and Data Mining 1st ed.. John Wiley and Sons, New Jersey.
[6] Neto, E.A.L. and De Carvalho, F.A.T. (2007). Centre and Range Method for Fitting a Linear Regression Model to Symbolic Interval Data. Computational Statistics and Data
Analysis. 52, 1500–1515.
[7] Arroyo, J., Gonza´lez-Rivera, G., and Mate´, C. (2011). Forecasting with Interval and Histogram Data: Some Financial Applications. Handbook of Empirical Economics and Finance. 247–279.
[8] Rodrigues, P. M. and Salish, N. (2011). Modeling and forecasting interval time series with Threshold models: An application to S&P500 Index returns. Working papers.
[9] Lin, L.C. and Sun, L.H. (2019). Modeling financial interval time series. PLoS One. 14, e0211709.
[10] Shreve, S.E. (2004). Stochastic Calculus for Finance II: Continuous-Time Models 2nd ed. Springer, New York.
[11] Hastings, W.K. (1970). Monte Carlo Sampling Methods Using Markov Chains and Their Applications. Biometrika. 57, 97–109.
[12] Ialongo, C. (2019). Confidence Interval For Quantiles and Percentiles. Biochemia Medica. 29, 5-17.
[13] Helfrick, A.D. and Cooper, W.D. (1996). Modern Electronic Instrumentation and Measurement Techniques. Prentice Hall of India, Delhi.
[14] Kim, K.Y., and Shin, Y. (2018). A Distance Boundary with Virtual Nodes for the Weighted Centroid Localization Algorithm. Sensors. 18, 1054.
[15] Adams, R.P. and MacKay, D.J. (2007). Bayesian Online Changepoint Detection. https: //doi.org/10.48550/arXiv.0710.3742
[16] Kuo, D.H. (2022). Online Change Point Detection under a Copula-Based Markov Chain Model for Normal Sequential Data. Graduate Institute of Statistics, National Central University.
[17] Yamanishi, K. and Takeuchi, J.I. (2022, July 23-26). A unifying framework for detecting outliers and change points from non-stationary time series data. 2002 Proceedings of the Eight ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Edmonton, Alta, Canada. 676-681. https://doi.org/10.1145/775047.775148.
[18] Zachos, I. (2018). Bayesian On-line Change-point Detection. Department of Computer Science, University of Warwick.
[19] Katser, I., Kozitsin, V., Lobachev, V., and Maksimov, I. (2021). Unsupervised Offline Changepoint Detection Ensembles. Applied Sciences. 11, 4280.
[20] Manca, G. and Fay, A. (2022, March 29-31). Off-Line Detection of Abrupt and Transitional Change Points in Industrial Process Signals Conference presentation. 2022 4th International Conference on Applied Automation and Industrial Diagnostics (ICAAID). Hail, Saudi Arabia. https://ieeexplore.ieee.org/document/9799507
指導教授 孫立憲(Li-Hsien Sun) 審核日期 2023-7-24
推文 facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu   
網路書籤 Google bookmarks   del.icio.us   hemidemi   myshare   

若有論文相關問題,請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407,或E-mail聯絡  - 隱私權政策聲明