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題名 白銀期貨的價格限制-以馬可夫鏈蒙地卡羅方法分析
price limits in the silver futures market: a MCMC approach
作者 鄭仲均
貢獻者 謝淑貞
鄭仲均
關鍵詞 價格限制
風險值
MCMC
FIGARCH
日期 2006
上傳時間 6-May-2016 16:34:45 (UTC+8)
摘要 在這篇論文中，我們運用馬可夫鏈蒙地卡羅(MCMC)方法來估計沒有價格限制下的白銀期貨價格。接著我們採用FIGARCH模型來計算VaR值，以進而評估估計成果。在本文中我們分別對三種不同分配下的FIGARCH模型計算VaR值，而實證結果顯示出在沒有價格限制下，白銀期貨有較好的估計結果。
In this paper, we try to implement the MCMC method to simulate the price of the silver futures without price limits. Then we compute the VaR by using the FIGARCH model because of the long memory properties in our data. There are three distributions we use to estimate model and compute VaR. The empirical results show that the silver futures without price limits performs better in computing in-sample VaR.
參考文獻 Baillie, R. T., Bollerslev, T., and Mikkelsen, H., 1996, Fractionally integrated generalized autoregressive conditional heteroskedasticity, Journal of Econometrics 74, 3-30.
     Barkoulas, J. T. and Baum, C. F., 1998, Long term dependence in stock returns, Working Paper, Department of Economics, Boston College, USA.
     Beine, M. and Laurent, S., 2003, Central bank interventions and jumps in double long memory models of daily exchange rate, Working Paper.
     Bollerslev, T., 1986, Generalized autoregressive conditional heteroskedasticity, Journal of Econometrics 31, 307-327.
     Bollerslev, T. and Mikkelen, H. O., 1996, Modeling and pricing long memory in stock market volatility, Journal of Econometrics 73, 151-184.
     Brunetti, C. and Gilbert, C. L., 2000, Bivariate FIGARCH and fractional cointegration, Journal of Empirical Finance 7, 509-530.
     Chiu, J.-C, 2000, Implementing Markov Chain Monte Carlo in Econometrics, Working Paper, Graduate Institute of International Economics, National Chung Cheng University.
     Chou, C. H., 2000, The performance of VaR measurements- the empirical studies of currency exchange rates, Graduate Institute of Finance, Fu Jen Catholic University.
     Chou, P.-H and Wu, S.,1998, A further investigation of daily price limits, Journal of Financial Studies 16, 19-48
     Dempster, A.P., Laird, N.M., and Rubin, D.B. (1977), “Maximum Likelihood from
     Incomplete Data via the EM Algorithm,” Journal of the Royal Statistical Society,
     Ser. B., 39, 1–38.
     Dueker, M. and Asea, P. K., 1995, Non-monotonic long memory dynamics in black-market exchange rates, Working Paper, Federal Reserve Bank of ST. Louis.
     Engle, R. F., 1982, Autoregressive conditional heteroskedasticity with estimates of the variance of united kingdom inflation, Econometrica 50, 987-1007.
     Giot, P. and Laurent, S., 2003, Value-at-risk for long and short trading positions, Journal of Applied Econometrics 18, 641-664.
     Glyn A. Holton, 2003, Value-at-risk: Theory and Practice, Academic Press.
     Granger, C. W. J. and Ding, Z. 1996, Varieties of long memory models, Journal of Econometrics 73, 61-77.
     Henry, O. T., 2000, Long memory in stock returns: some international evidence, Working Paper, Department of Economics, The University of Melbourne, Australia.
     Kupiec, P., 1995, Techniques for verifying the accuracy of risk measurement models, Journal of Derivatives 2, 174-184.
     Lambert, P. and Laurent, S., 2000, Modeling skewness dynamics in series of financial data, Discussion Paper, Institute de Statistique, Louvain-la-Neuve.
     Lo, A. W., 1991, Long-term memory in stock market price, Econometrica 59, 1279-1313.
     Liu, S. and Brorsen, B., 1995, Maximun likelihood estimation of a GARCH-stable model, Journal of Applied Econometrics 2, 185-273.
     Ruey S. Tsay, 2002, Analysis of Financial time series, John Wiley & Sons.
     Ruey S. Tsay, 2003, The magnet effect of price limits: evidence from high-frequency data on Taiwan Stock Exchange, Journal of Empirical Finance 10, 133-168.
     Sriananthakumar, S. and Silvapulle, S., 2003, Estimating value at risks for short and long trading positions, Working Paper, Department of Economics and Business Statistics, Monash University, Australia.
描述 碩士
國立政治大學
國際經營與貿易學系
93351029
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0093351029
資料類型 thesis
dc.contributor.advisor 謝淑貞zh_TW
dc.contributor.author (Authors) 鄭仲均zh_TW
dc.creator (作者) 鄭仲均zh_TW
dc.date (日期) 2006en_US
dc.date.accessioned 6-May-2016 16:34:45 (UTC+8)-
dc.date.available 6-May-2016 16:34:45 (UTC+8)-
dc.date.issued (上傳時間) 6-May-2016 16:34:45 (UTC+8)-
dc.identifier (Other Identifiers) G0093351029en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/94396-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 國際經營與貿易學系zh_TW
dc.description (描述) 93351029zh_TW
dc.description.abstract (摘要) 在這篇論文中，我們運用馬可夫鏈蒙地卡羅(MCMC)方法來估計沒有價格限制下的白銀期貨價格。接著我們採用FIGARCH模型來計算VaR值，以進而評估估計成果。在本文中我們分別對三種不同分配下的FIGARCH模型計算VaR值，而實證結果顯示出在沒有價格限制下，白銀期貨有較好的估計結果。zh_TW
dc.description.abstract (摘要) In this paper, we try to implement the MCMC method to simulate the price of the silver futures without price limits. Then we compute the VaR by using the FIGARCH model because of the long memory properties in our data. There are three distributions we use to estimate model and compute VaR. The empirical results show that the silver futures without price limits performs better in computing in-sample VaR.en_US
dc.description.tableofcontents 1. Introduction 5
     2. Research Methodology 9
      2.1 Unit Root Tests and Lo’s Test................9
      2.1.1 The Augmented Dickey-Fuller Test.........9
      2.1.2 The Phillips Perron Test................10
      2.1.3 Lo’s Test..............................11
     2.2 Markov Chain Monte Carlo (MCMC) method.........12
     2.3 FIGARCH (p, d, q) model........................14
     2.4 VaR model and Kupiec LR Test...................16
      2.4.1 VaR model..............................16
      2.4.2 Kupiec LR Test.........................17
     3. Data and Empirical Results 19
     3.1 Data...........................................19
     3.2 Estimation Results.............................20
      3.2.1 The MCMC method results................20
      3.2.2 The FIGARCH model results..............21
     3.3 The computation of VaR.........................22
      3.3.1 The computation of in-sample VaR with price
      limit....................................22
      3.3.2 The computation of in-sample VaR without price
      limit....................................23
      3.3.3 Comparison...............................24
     4. Conclusions 25
     Reference 27
     Tables and Figures 30		zh_TW
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0093351029en_US
dc.subject (關鍵詞) 價格限制zh_TW
dc.subject (關鍵詞) 風險值zh_TW
dc.subject (關鍵詞) MCMCen_US
dc.subject (關鍵詞) FIGARCHen_US
dc.title (題名) 白銀期貨的價格限制-以馬可夫鏈蒙地卡羅方法分析zh_TW
dc.title (題名) price limits in the silver futures market: a MCMC approachen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) Baillie, R. T., Bollerslev, T., and Mikkelsen, H., 1996, Fractionally integrated generalized autoregressive conditional heteroskedasticity, Journal of Econometrics 74, 3-30.
     Barkoulas, J. T. and Baum, C. F., 1998, Long term dependence in stock returns, Working Paper, Department of Economics, Boston College, USA.
     Beine, M. and Laurent, S., 2003, Central bank interventions and jumps in double long memory models of daily exchange rate, Working Paper.
     Bollerslev, T., 1986, Generalized autoregressive conditional heteroskedasticity, Journal of Econometrics 31, 307-327.
     Bollerslev, T. and Mikkelen, H. O., 1996, Modeling and pricing long memory in stock market volatility, Journal of Econometrics 73, 151-184.
     Brunetti, C. and Gilbert, C. L., 2000, Bivariate FIGARCH and fractional cointegration, Journal of Empirical Finance 7, 509-530.
     Chiu, J.-C, 2000, Implementing Markov Chain Monte Carlo in Econometrics, Working Paper, Graduate Institute of International Economics, National Chung Cheng University.
     Chou, C. H., 2000, The performance of VaR measurements- the empirical studies of currency exchange rates, Graduate Institute of Finance, Fu Jen Catholic University.
     Chou, P.-H and Wu, S.,1998, A further investigation of daily price limits, Journal of Financial Studies 16, 19-48
     Dempster, A.P., Laird, N.M., and Rubin, D.B. (1977), “Maximum Likelihood from
     Incomplete Data via the EM Algorithm,” Journal of the Royal Statistical Society,
     Ser. B., 39, 1–38.
     Dueker, M. and Asea, P. K., 1995, Non-monotonic long memory dynamics in black-market exchange rates, Working Paper, Federal Reserve Bank of ST. Louis.
     Engle, R. F., 1982, Autoregressive conditional heteroskedasticity with estimates of the variance of united kingdom inflation, Econometrica 50, 987-1007.
     Giot, P. and Laurent, S., 2003, Value-at-risk for long and short trading positions, Journal of Applied Econometrics 18, 641-664.
     Glyn A. Holton, 2003, Value-at-risk: Theory and Practice, Academic Press.
     Granger, C. W. J. and Ding, Z. 1996, Varieties of long memory models, Journal of Econometrics 73, 61-77.
     Henry, O. T., 2000, Long memory in stock returns: some international evidence, Working Paper, Department of Economics, The University of Melbourne, Australia.
     Kupiec, P., 1995, Techniques for verifying the accuracy of risk measurement models, Journal of Derivatives 2, 174-184.
     Lambert, P. and Laurent, S., 2000, Modeling skewness dynamics in series of financial data, Discussion Paper, Institute de Statistique, Louvain-la-Neuve.
     Lo, A. W., 1991, Long-term memory in stock market price, Econometrica 59, 1279-1313.
     Liu, S. and Brorsen, B., 1995, Maximun likelihood estimation of a GARCH-stable model, Journal of Applied Econometrics 2, 185-273.
     Ruey S. Tsay, 2002, Analysis of Financial time series, John Wiley & Sons.
     Ruey S. Tsay, 2003, The magnet effect of price limits: evidence from high-frequency data on Taiwan Stock Exchange, Journal of Empirical Finance 10, 133-168.
     Sriananthakumar, S. and Silvapulle, S., 2003, Estimating value at risks for short and long trading positions, Working Paper, Department of Economics and Business Statistics, Monash University, Australia.		zh_TW