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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 盧信銘(Hsin-Min Lu) | |
| dc.contributor.author | Shu-Yu Huang | en |
| dc.contributor.author | 黃舒瑜 | zh_TW |
| dc.date.accessioned | 2021-06-17T06:02:30Z | - |
| dc.date.available | 2024-02-14 | |
| dc.date.copyright | 2019-02-14 | |
| dc.date.issued | 2019 | |
| dc.date.submitted | 2019-01-30 | |
| dc.identifier.citation | REFERENCE
Black, F. (1976). Studies of Stock Price Volatility Changes. Proceeding of the 1976 Meetings of the Business and Economics Statistics Section, American Statistical Association, 177-181. Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327. Chang, J.-R., Hung, M.-W., Lee, C.-F., & Lu, H.-M. (2007). The jump behavior of foreign exchange market: analysis of Thai Baht. Review of Pacific Basin Financial Markets and Policies, 10(02), 265-288. Chib, S., Nardari, F., & Shephard, N. (2002). Markov chain Monte Carlo methods for stochastic volatility models. Journal of Econometrics, 108(2), 281-316. Christie, A. A. (1982). The stochastic behavior of common stock variances: Value, leverage and interest rate effects. Journal of Financial Economics, 10(4), 407-432. Clark, P. K. (1973). A subordinated stochastic process model with finite variance for speculative prices. Econometrica: Journal of the Econometric Society, 41(1), 135-155. Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the Econometric Society, 987-1007. Eraker, B., Johannes, M., & Polson, N. (2003). The impact of jumps in volatility and returns. Journal of Finance, 58(3), 1269-1300. Fama, E. F. (1965). The behavior of stock-market prices. Journal of Business, 38(1), 34-105. Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. Journal of Finance, 48(5), 1779-1801. Harvey, A. C. & N. Shephard (1996). Estimation of an asymmetric stochastic volatility model for asset returns. Journal of Business & Economic Statistics, 14(4), 429-434. Heston, S. L. (1993). A closed-form solution for options with stochastic volatility with applications to bond and currency options. Review of Financial Studies, 6(2), 327-343. Jacquier, E., Polson, N. G., & Rossi, P. E. (1994). Bayesian Analysis of Stochastic Volatility Models. Journal of Business & Economic Statistics, 12(4), 371-389. Jacquier, E., Polson, N. G., & Rossi, P. E. (2004). Bayesian analysis of stochastic volatility models with fat-tails and correlated errors. Journal of Econometrics, 122(1), 185-212. Kastner, G., & Frühwirth-Schnatter, S. (2014). Ancillarity-sufficiency interweaving strategy (ASIS) for boosting MCMC estimation of stochastic volatility models. Computational Statistics & Data Analysis, 76, 408-423. Li, H., Wells, M. T., & Yu, C. L. (2006). A Bayesian analysis of return dynamics with Lévy jumps. Review of Financial Studies, 21(5), 2345-2378. Liesenfeld, R. & R. C. Jung (2000). Stochastic volatility models: conditional normality versus heavy-tailed distributions. Journal of Applied Econometrics, 15(2) 137-160. Mandelbrot, B. (1963). The Variation of Certain Speculative Prices. Journal of Business, 36(4), 394-394. Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica: Journal of the Econometric Society, 59(2), 347-370. Sandmann, G. & S. J. Koopman (1998). Estimation of stochastic volatility models via Monte Carlo maximum likelihood. Journal of Econometrics, 87(2), 271-301. Shephard, N. (1996). Statistical aspects of ARCH and stochastic volatility. Monographs on Statistics and Applied Probability, 65, 1-67. Taylor, S. J. (1986). Modelling Financial Time Series, John Wiley. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/71526 | - |
| dc.description.abstract | 波動性可用於衡量金融資產的變動程度，其應用範疇包含風險管理、投資組合的選擇以及選擇權的定價等，因此，對投資者而言，如何準確地預測波動，成為金融領域中相當重要的課題。其中，隨機波動模型為衡量波動的一種模型，該模型將波動視為服從一個隨機過程的變量。
本研究主要探討離散時間的隨機波動模型，目的為提供一個實作基本隨機波動模型的R套件──logsv並且公開套件所有實作的相關細節。logsv所實作的模型採用馬可夫鏈蒙地卡羅估計方法。我們分別以兩組模擬資料、S&P 500、臺灣加權股價指數、美金對臺幣匯率、歐元對美金匯率等六組資料進行實驗，透過與真實值和前人研究結果的比較，檢驗模型實作的正確性。實驗結果顯示，我們所提供的logsv套件，不論是參數估計還是波動的估計，都有很好的表現。同時，利用波動估計的結果，我們討論可能引起高波動的相關金融事件或危機。 | zh_TW |
| dc.description.abstract | Modeling volatility becomes crucial in financial applications ranging from risk management, asset allocation to option pricing. Stochastic volatility (SV) models are one of the volatility models that treat the variances as an unobserved component following a stochastic process.
In this paper, we focus on the discrete time stochastic volatility (SV) models. We provide an R package, logsv, which implements the basic log SV model with the estimation of the Markov chain Monte Carlo approach and disclose all the implementation details. We fit the model to simulated datasets and real world datasets to test the fitness and correctness of our implementation. The experiment results with all datasets show that the estimation procedure works well on both parameter and volatility estimation. With the estimation results of the basic log SV model, we discuss some period of high volatility and highlight the financial crises and events that are potentially related. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T06:02:30Z (GMT). No. of bitstreams: 1 ntu-108-R05725058-1.pdf: 2255537 bytes, checksum: c43a32b6b44943c3f0556ffceaf33244 (MD5) Previous issue date: 2019 | en |
| dc.description.tableofcontents | 誌謝 I
摘要 II Abstract III List of Figures V List of Tables VI 1. Introduction 1 2. Literature Review 4 2.1. Basic Stochastic Volatility Models 5 2.1.1. Log Stochastic Volatility Models 5 2.1.2. Square-Root Stochastic Volatility Models 6 2.2. Extended Stochastic Volatility Models 7 2.2.1. Stochastic Volatility Models with Fat Tails 7 2.2.2. Stochastic Volatility Models with Leverage Effect 8 2.2.3. Stochastic Volatility Models with Jumps 9 2.3. Estimation of Stochastic Volatility Models 10 2.4. Implementation of Stochastic Volatility Models 12 3. Log Stochastic Volatility 13 3.1. Model 13 3.2. Algorithm 13 3.3. Implementation 20 4. Data 21 4.1. Simulated Data 21 4.2. Real World Data 22 4.3. Priors and Initial values 25 5. Estimation results 26 5.1. Results with Simulated Data 27 5.2. Results with Real World Data 29 5.2.1. Results with S&P 500 30 5.2.2. Results with TAIEX 35 5.2.3. Results with USD/TWD 38 5.2.4. Results with EUR/USD 42 6. Conclusion and Future Work 45 REFERENCE 46 | |
| dc.language.iso | en | |
| dc.subject | 隨機波動模型 | zh_TW |
| dc.subject | 貝氏估計 | zh_TW |
| dc.subject | 馬可夫鏈蒙地卡羅 | zh_TW |
| dc.subject | Markov chain Monte Carlo | en |
| dc.subject | Stochastic volatility model | en |
| dc.subject | Bayesian approach | en |
| dc.title | 隨機波動模型的實作與應用 | zh_TW |
| dc.title | An Implementation of Stochastic Volatility Model | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 107-1 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 余峻瑜(Jiun-Yu Yu),洪為璽(Wei-Hsi Hung) | |
| dc.subject.keyword | 隨機波動模型,貝氏估計,馬可夫鏈蒙地卡羅, | zh_TW |
| dc.subject.keyword | Stochastic volatility model,Bayesian approach,Markov chain Monte Carlo, | en |
| dc.relation.page | 47 | |
| dc.identifier.doi | 10.6342/NTU201900044 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2019-01-30 | |
| dc.contributor.author-college | 管理學院 | zh_TW |
| dc.contributor.author-dept | 資訊管理學研究所 | zh_TW |
| 顯示於系所單位： | 資訊管理學系 | |
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