IDEAS home Printed from https://ideas.repec.org/f/pna602.html
   My authors  Follow this author

Teruo Nakatsuma

Personal Details

First Name:Teruo
Middle Name:
Last Name:Nakatsuma
Suffix:
RePEc Short-ID:pna602
[This author has chosen not to make the email address public]

Affiliation

Faculty of Economics
Keio University

Tokyo, Japan
http://www.econ.keio.ac.jp/
RePEc:edi:fekeijp (more details at EDIRC)

Research output

as
Jump to: Working papers Articles Chapters Books

Working papers

  1. Peter Romero & Eisaku Daniel Tanaka & Yuki Mikiya & Shinya Yoshino & Atsushi Oshio & Teruo Nakatsuma, 2023. "Vaccine Uptake - Geographic Psychology or the Information Field?," Working Papers e191, Tokyo Center for Economic Research.
  2. Wakuo Saito & Teruo Nakatsuma, 2021. "Hierarchical Bayesian Hedonic Regression Analysis of Japanese Rice Wine: Price is Right?," Keio-IES Discussion Paper Series 2021-019, Institute for Economics Studies, Keio University.
  3. Sakae Oya & Teruo Nakatsuma, 2021. "Identification in Bayesian Estimation of the Skewness Matrix in a Multivariate Skew-Elliptical Distribution," Papers 2108.04019, arXiv.org.
  4. Wakuo Saito & Teruo Nakatsuma, 2021. "The Cost Function Estimation of Japanese Sake Industry with Prefecture-Wise Panel Data," Keio-IES Discussion Paper Series 2021-011, Institute for Economics Studies, Keio University.
  5. Taiga Saito & Takanori Adachi & Teruo Nakatsuma & Akihiko Takahashi & Hiroshi Tsuda & Naoyuki Yoshino, 2018. "Trading and Ordering Patterns of Market Participants in High Frequency Trading Environment -Empirical Study in the Japanese Stock Market-(Forthcoming in Asia-Pacific Financial Markets)(Revised version," CARF F-Series CARF-F-438, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
  6. Taiga Saito & Takanori Adachi & Teruo Nakatsuma & Akihiko Takahashi & Hiroshi Tsuda & Naoyuki Yoshino, 2017. "Trading and Ordering Patterns of Market Participants in High Frequency Trading Environment -Empirical Study in the Japanese Stock Market-," CARF F-Series CARF-F-411, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
  7. Kenichiro McAlinn & Asahi Ushio & Teruo Nakatsuma, 2016. "Volatility Forecasts Using Nonlinear Leverage Effects," Papers 1605.06482, arXiv.org, revised Dec 2017.
  8. Teruo Nakatsuma, 1999. "Bayesian Analysis of the Convergence Hypothesis in Economic Drowth: A Markov Approach," Discussion Paper Series a368, Institute of Economic Research, Hitotsubashi University.
  9. Teruo Nakatsuma & Hiroki Tsurumi, 1996. "ARMA-GARCH Models: Bayes Estimation Versus MLE, and Bayes Non-stationarity Test," Departmental Working Papers 199619, Rutgers University, Department of Economics.

Articles

  1. Wakuo Saito & Makoto Nakakita & Teruo Nakatsuma, 2024. "Comparative Analysis of Japanese Rice Wine Export Trends: Large Firms in the Nada Region vs. SMEs in Other Regions," World, MDPI, vol. 5(3), pages 1-23, August.
  2. Tomoki Toyabe & Teruo Nakatsuma, 2022. "Stochastic Conditional Duration Model with Intraday Seasonality and Limit Order Book Information," JRFM, MDPI, vol. 15(10), pages 1-25, October.
  3. Teruo Nakatsuma, 2022. "Comment on “Why Fintech Is Not Changing Japanese Banking”," Asian Economic Policy Review, Japan Center for Economic Research, vol. 17(2), pages 313-314, July.
  4. Makoto Nakakita & Teruo Nakatsuma, 2021. "Bayesian Analysis of Intraday Stochastic Volatility Models of High-Frequency Stock Returns with Skew Heavy-Tailed Errors," JRFM, MDPI, vol. 14(4), pages 1-29, March.
  5. Kenichiro McAlinn & Asahi Ushio & Teruo Nakatsuma, 2020. "Volatility forecasts using stochastic volatility models with nonlinear leverage effects," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(2), pages 143-154, March.
  6. Taiga Saito & Takanori Adachi & Teruo Nakatsuma & Akihiko Takahashi & Hiroshi Tsuda & Naoyuki Yoshino, 2018. "Trading and Ordering Patterns of Market Participants in High Frequency Trading Environment: Empirical Study in the Japanese Stock Market," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 25(3), pages 179-220, September.
  7. Kenji Kamizono & Takeaki Kariya & Regina Liu & Teruo Nakatsuma, 2004. "A New Control Variate Estimator for an Asian Option," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 11(2), pages 143-160, June.
  8. Nakatsuma, Teruo, 2000. "Bayesian analysis of ARMA-GARCH models: A Markov chain sampling approach," Journal of Econometrics, Elsevier, vol. 95(1), pages 57-69, March.
  9. Teruo Nakatsuma & Hiroki Tsurumi, 1999. "Bayesian Estimation of ARMA-GARCH Model of Weekly Foreign Exchange Rates," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 6(1), pages 71-84, January.
  10. Nakatsuma Teruo, 1998. "A Markov-Chain Sampling Algorithm for GARCH Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 3(2), pages 1-13, July.

Chapters

  1. Teruo Nakatsuma, 2021. "The Mechanism of HFT and Its Merits and Demerits—The Information Efficiency Challenge," Springer Books, in: Sahoko Kaji & Teruo Nakatsuma & Masahiro Fukuhara (ed.), The Economics of Fintech, chapter 0, pages 167-177, Springer.
  2. Teruo Nakatsuma, 2021. "Machine Learning Principles and Applications," Springer Books, in: Sahoko Kaji & Teruo Nakatsuma & Masahiro Fukuhara (ed.), The Economics of Fintech, chapter 0, pages 155-165, Springer.
  3. Teruo Nakatsuma, 2021. "Asset Management and Robo-Advisors," Springer Books, in: Sahoko Kaji & Teruo Nakatsuma & Masahiro Fukuhara (ed.), The Economics of Fintech, chapter 0, pages 179-187, Springer.

Books

  1. Sahoko Kaji & Teruo Nakatsuma & Masahiro Fukuhara (ed.), 2021. "The Economics of Fintech," Springer Books, Springer, number 978-981-33-4913-1, January.

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. Taiga Saito & Takanori Adachi & Teruo Nakatsuma & Akihiko Takahashi & Hiroshi Tsuda & Naoyuki Yoshino, 2017. "Trading and Ordering Patterns of Market Participants in High Frequency Trading Environment -Empirical Study in the Japanese Stock Market-," CARF F-Series CARF-F-411, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.

    Cited by:

    1. Taiga Saito & Takanori Adachi & Teruo Nakatsuma & Akihiko Takahashi & Hiroshi Tsuda & Naoyuki Yoshino, 2017. "Trading and Ordering Patterns of Market Participants in High Frequency Trading Environment -Empirical Study in the Japanese Stock Market-," CARF F-Series CARF-F-411, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    2. Taiga Saito & Shivam Gupta, 2022. "Big Data Applications with Theoretical Models and Social Media in Financial Management," CIRJE F-Series CIRJE-F-1205, CIRJE, Faculty of Economics, University of Tokyo.
    3. Ritesh Kumar Dubey & A. Sarath Babu & Rajneesh Ranjan Jha & Urvashi Varma, 2022. "Algorithmic Trading Efficiency and its Impact on Market-Quality," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 29(3), pages 381-409, September.
    4. Taiga Saito & Shivam Gupta, 2022. "Big data applications with theoretical models and social media in financial management," CARF F-Series CARF-F-550, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    5. Taiga Saito & Akihiko Takahashi, 2018. "Online Supplement for "Stochastic Differential Game in High Frequency Market"," CIRJE F-Series CIRJE-F-1087, CIRJE, Faculty of Economics, University of Tokyo.
    6. Hayashi, Takaki & Nishide, Katsumasa, 2024. "Strategic liquidity provision in high-frequency trading," International Review of Financial Analysis, Elsevier, vol. 93(C).

  2. Teruo Nakatsuma & Hiroki Tsurumi, 1996. "ARMA-GARCH Models: Bayes Estimation Versus MLE, and Bayes Non-stationarity Test," Departmental Working Papers 199619, Rutgers University, Department of Economics.

    Cited by:

    1. Greyserman, Alex & Jones, Douglas H. & Strawderman, William E., 2006. "Portfolio selection using hierarchical Bayesian analysis and MCMC methods," Journal of Banking & Finance, Elsevier, vol. 30(2), pages 669-678, February.
    2. Lennart F. Hoogerheide & David Ardia & Nienke Corre, 2011. "Stock Index Returns' Density Prediction using GARCH Models: Frequentist or Bayesian Estimation?," Tinbergen Institute Discussion Papers 11-020/4, Tinbergen Institute.
    3. Hoogerheide, Lennart F. & Ardia, David & Corré, Nienke, 2012. "Density prediction of stock index returns using GARCH models: Frequentist or Bayesian estimation?," Economics Letters, Elsevier, vol. 116(3), pages 322-325.

Articles

  1. Teruo Nakatsuma, 2022. "Comment on “Why Fintech Is Not Changing Japanese Banking”," Asian Economic Policy Review, Japan Center for Economic Research, vol. 17(2), pages 313-314, July.

    Cited by:

    1. Yiping Huang & Takatoshi Ito & Kazumasa Iwata & Colin McKenzie & Shujiro Urata, 2022. "Digital Finance in Asia: Editors' Overview," Asian Economic Policy Review, Japan Center for Economic Research, vol. 17(2), pages 163-182, July.

  2. Makoto Nakakita & Teruo Nakatsuma, 2021. "Bayesian Analysis of Intraday Stochastic Volatility Models of High-Frequency Stock Returns with Skew Heavy-Tailed Errors," JRFM, MDPI, vol. 14(4), pages 1-29, March.

    Cited by:

    1. Xiao Jiang & Saralees Nadarajah & Thomas Hitchen, 2024. "A Review of Generalized Hyperbolic Distributions," Computational Economics, Springer;Society for Computational Economics, vol. 64(1), pages 595-624, July.

  3. Kenichiro McAlinn & Asahi Ushio & Teruo Nakatsuma, 2020. "Volatility forecasts using stochastic volatility models with nonlinear leverage effects," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(2), pages 143-154, March.

    Cited by:

    1. Mengxi He & Xianfeng Hao & Yaojie Zhang & Fanyi Meng, 2021. "Forecasting stock return volatility using a robust regression model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(8), pages 1463-1478, December.
    2. Xiafei Li & Dongxin Li & Xuhui Zhang & Guiwu Wei & Lan Bai & Yu Wei, 2021. "Forecasting regular and extreme gold price volatility: The roles of asymmetry, extreme event, and jump," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(8), pages 1501-1523, December.
    3. Guo, Xiaozhu & Huang, Yisu & Liang, Chao & Umar, Muhammad, 2022. "Forecasting volatility of EUA futures: New evidence," Energy Economics, Elsevier, vol. 110(C).

  4. Taiga Saito & Takanori Adachi & Teruo Nakatsuma & Akihiko Takahashi & Hiroshi Tsuda & Naoyuki Yoshino, 2018. "Trading and Ordering Patterns of Market Participants in High Frequency Trading Environment: Empirical Study in the Japanese Stock Market," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 25(3), pages 179-220, September.
    See citations under working paper version above.
  5. Kenji Kamizono & Takeaki Kariya & Regina Liu & Teruo Nakatsuma, 2004. "A New Control Variate Estimator for an Asian Option," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 11(2), pages 143-160, June.

    Cited by:

    1. Takahiko Fujita & Masahiro Ishii, 2010. "Valuation of a Repriceable Executive Stock Option," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 17(1), pages 1-18, March.

  6. Nakatsuma, Teruo, 2000. "Bayesian analysis of ARMA-GARCH models: A Markov chain sampling approach," Journal of Econometrics, Elsevier, vol. 95(1), pages 57-69, March.

    Cited by:

    1. Oleg Korenok & Bruce Mizrach & Stan Radchenko, 2004. "The Microeconomics of Macroeconomic Asymmetries: Sectoral Driving Forces and Firm Level Characteristics," Departmental Working Papers 200405, Rutgers University, Department of Economics.
    2. Ohtsuka, Yoshihiro & Oga, Takashi & Kakamu, Kazuhiko, 2010. "Forecasting electricity demand in Japan: A Bayesian spatial autoregressive ARMA approach," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2721-2735, November.
    3. Xibin Zhang & Maxwell L. King, 2011. "Bayesian semiparametric GARCH models," Monash Econometrics and Business Statistics Working Papers 24/11, Monash University, Department of Econometrics and Business Statistics.
    4. Qiang Xia & Heung Wong & Jinshan Liu & Rubing Liang, 2017. "Bayesian Analysis of Power-Transformed and Threshold GARCH Models: A Griddy-Gibbs Sampler Approach," Computational Economics, Springer;Society for Computational Economics, vol. 50(3), pages 353-372, October.
    5. Murat Midiliç, 2020. "Estimation of STAR–GARCH Models with Iteratively Weighted Least Squares," Computational Economics, Springer;Society for Computational Economics, vol. 55(1), pages 87-117, January.
    6. Bo Zhang & Joshua C.C. Chan & Jamie L. Cross, 2018. "Stochastic volatility models with ARMA innovations: An application to G7 inflation forecasts," CAMA Working Papers 2018-32, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    7. Galeano, Pedro, 2005. "Bayesian estimation of the gaussian mixture garch model," DES - Working Papers. Statistics and Econometrics. WS ws053605, Universidad Carlos III de Madrid. Departamento de Estadística.
    8. Eiji Minemura, 2006. "An Interest-rate Model Analysis Based on Data Augmentation Bayesian Forecasting," Journal of Applied Statistics, Taylor & Francis Journals, vol. 33(10), pages 1085-1104.
    9. Sarantis Tsiaplias, 2009. "Examining Feedback, Momentum and Overreaction in National Equity Markets," Melbourne Institute Working Paper Series wp2009n18, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    10. Goldman Elena & Tsurumi Hiroki, 2005. "Bayesian Analysis of a Doubly Truncated ARMA-GARCH Model," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 9(2), pages 1-38, June.
    11. Ting Ting Chen & Tetsuya Takaishi, 2013. "Empirical Study of the GARCH model with Rational Errors," Papers 1312.7057, arXiv.org.
    12. Yanhui Xi & Hui Peng & Yemei Qin, 2016. "Modeling Financial Time Series Based on a Market Microstructure Model with Leverage Effect," Discrete Dynamics in Nature and Society, Hindawi, vol. 2016, pages 1-15, February.
    13. Yu Yue & Paul Speckman & Dongchu Sun, 2012. "Priors for Bayesian adaptive spline smoothing," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 64(3), pages 577-613, June.
    14. Lanne, Markku & Luoto, Jani, 2008. "Robustness of the risk-return relationship in the U.S. stock market," Finance Research Letters, Elsevier, vol. 5(2), pages 118-127, June.
    15. Sylvia Kaufmann & Sylvia Frühwirth‐Schnatter, 2002. "Bayesian analysis of switching ARCH models," Journal of Time Series Analysis, Wiley Blackwell, vol. 23(4), pages 425-458, July.
    16. Audrone Virbickaite & M. Concepción Ausín & Pedro Galeano, 2015. "Bayesian Inference Methods For Univariate And Multivariate Garch Models: A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 29(1), pages 76-96, February.
    17. Wolfgang Aussenegg & Tatiana Miazhynskaia, 2006. "Uncertainty in Value-at-risk Estimates under Parametric and Non-parametric Modeling," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 20(3), pages 243-264, September.
    18. So, Mike K.P. & Chen, Cathy W.S. & Lee, Jen-Yu & Chang, Yi-Ping, 2008. "An empirical evaluation of fat-tailed distributions in modeling financial time series," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 77(1), pages 96-108.
    19. Manabu Asai & Michael McAleer, 2022. "Bayesian Analysis of Realized Matrix-Exponential GARCH Models," Computational Economics, Springer;Society for Computational Economics, vol. 59(1), pages 103-123, January.
    20. Stanislav Radchenko, 2004. "Oil price volatility and the asymmetric response of gasoline prices to oil price increases and decreases," Industrial Organization 0408001, University Library of Munich, Germany.
    21. Miazhynskaia, Tatiana & Fruhwirth-Schnatter, Sylvia & Dorffner, Georg, 2006. "Bayesian testing for non-linearity in volatility modeling," Computational Statistics & Data Analysis, Elsevier, vol. 51(3), pages 2029-2042, December.
    22. Chan, Joshua C.C., 2013. "Moving average stochastic volatility models with application to inflation forecast," Journal of Econometrics, Elsevier, vol. 176(2), pages 162-172.
    23. Gabriele Fiorentini & Enrique Sentana & Neil Shephard, 2003. "Likelihood-Based Estimation Of Latent Generalised Arch Structures," Working Papers. Serie AD 2003-06, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
    24. Tetsuya Takaishi, 2009. "Markov Chain Monte Carlo on Asymmetric GARCH Model Using the Adaptive Construction Scheme," Papers 0909.1478, arXiv.org.
    25. Deschamps, Philippe J., 2012. "Bayesian estimation of generalized hyperbolic skewed student GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3035-3054.
    26. Bernardi, Mauro & Costola, Michele, 2019. "High-dimensional sparse financial networks through a regularised regression model," SAFE Working Paper Series 244, Leibniz Institute for Financial Research SAFE.
    27. Hoogerheide, Lennart & van Dijk, Herman K., 2010. "Bayesian forecasting of Value at Risk and Expected Shortfall using adaptive importance sampling," International Journal of Forecasting, Elsevier, vol. 26(2), pages 231-247, April.
    28. Tetsuya Takaishi, 2009. "An Adaptive Markov Chain Monte Carlo Method for GARCH Model," Papers 0901.0992, arXiv.org.
    29. Murat Midilic, 2016. "Estimation Of Star-Garch Models With Iteratively Weighted Least Squares," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 16/918, Ghent University, Faculty of Economics and Business Administration.
    30. Kim, Chang-Jin & Kim, Jaeho, 2013. "Bayesian Inference in Regime-Switching ARMA Models with Absorbing States: The Dynamics of the Ex-Ante Real Interest Rate Under Structural Breaks," MPRA Paper 51117, University Library of Munich, Germany.
    31. Xibin Zhang & Maxwell L. King, 2013. "Gaussian kernel GARCH models," Monash Econometrics and Business Statistics Working Papers 19/13, Monash University, Department of Econometrics and Business Statistics.
    32. Sarantis Tsiaplias, 2007. "A Metropolis-in-Gibbs Sampler for Estimating Equity Market Factors," Melbourne Institute Working Paper Series wp2007n18, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    33. Teruo Nakatsuma & Hiroki Tsurumi, 1996. "ARMA-GARCH Models: Bayes Estimation Versus MLE, and Bayes Non-stationarity Test," Departmental Working Papers 199619, Rutgers University, Department of Economics.
    34. Asai, Manabu, 2009. "Bayesian analysis of stochastic volatility models with mixture-of-normal distributions," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(8), pages 2579-2596.
    35. Brownlees Christian T. & Vannucci Marina, 2013. "A Bayesian approach for capturing daily heterogeneity in intra-daily durations time series," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(1), pages 21-46, February.
    36. Hyun Kook Shin & Byoung Hark Yoo, 2012. "The Volatility Of The Won-Dollar Exchange Rate During The 2008-9 Crisis," Journal of Economic Development, Chung-Ang Unviersity, Department of Economics, vol. 37(4), pages 61-77, December.
    37. Ardia, David & Hoogerheide, Lennart F., 2010. "Efficient Bayesian estimation and combination of GARCH-type models," MPRA Paper 22919, University Library of Munich, Germany.
    38. Bai, Yizhou & Xue, Cheng, 2021. "An empirical study on the regulated Chinese agricultural commodity futures market based on skew Ornstein-Uhlenbeck model," Research in International Business and Finance, Elsevier, vol. 57(C).
    39. Chen, Cathy W.S. & Yu, Tiffany H.K., 2005. "Long-term dependence with asymmetric conditional heteroscedasticity in stock returns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 353(C), pages 413-424.
    40. Tsiaplias, Sarantis, 2008. "Factor estimation using MCMC-based Kalman filter methods," Computational Statistics & Data Analysis, Elsevier, vol. 53(2), pages 344-353, December.
    41. Jouchi Nakajima, 2008. "EGARCH and Stochastic Volatility: Modeling Jumps and Heavy-tails for Stock Returns," IMES Discussion Paper Series 08-E-23, Institute for Monetary and Economic Studies, Bank of Japan.
    42. Yoo Byoung Hark, 2010. "Estimating the Term Premium by a Markov Switching Model with ARMA-GARCH Errors," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 14(2), pages 1-20, March.
    43. Terence D.Agbeyegbe & Elena Goldman, 2005. "Estimation of threshold time series models using efficient jump MCMC," Economics Working Paper Archive at Hunter College 406, Hunter College Department of Economics, revised 2005.
    44. Tatiana Miazhynskaia & Georg Dorffner, 2006. "A comparison of Bayesian model selection based on MCMC with an application to GARCH-type models," Statistical Papers, Springer, vol. 47(4), pages 525-549, October.
    45. Ehlers, Ricardo S., 2012. "Computational tools for comparing asymmetric GARCH models via Bayes factors," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 82(5), pages 858-867.
    46. Yizhou Bai & Yongjin Wang & Haoyan Zhang & Xiaoyang Zhuo, 2022. "Bayesian Estimation of the Skew Ornstein-Uhlenbeck Process," Computational Economics, Springer;Society for Computational Economics, vol. 60(2), pages 479-527, August.
    47. Teruo Nakatsuma & Hiroki Tsurumi, 1999. "Bayesian Estimation of ARMA-GARCH Model of Weekly Foreign Exchange Rates," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 6(1), pages 71-84, January.
    48. Dimitrakopoulos, Stefanos & Tsionas, Mike, 2019. "Ordinal-response GARCH models for transaction data: A forecasting exercise," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1273-1287.

  7. Teruo Nakatsuma & Hiroki Tsurumi, 1999. "Bayesian Estimation of ARMA-GARCH Model of Weekly Foreign Exchange Rates," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 6(1), pages 71-84, January.

    Cited by:

    1. Goldman Elena & Tsurumi Hiroki, 2005. "Bayesian Analysis of a Doubly Truncated ARMA-GARCH Model," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 9(2), pages 1-38, June.
    2. Zhang, Xingfa & Zhang, Rongmao & Li, Yuan & Ling, Shiqing, 2022. "LADE-based inferences for autoregressive models with heavy-tailed G-GARCH(1, 1) noise," Journal of Econometrics, Elsevier, vol. 227(1), pages 228-240.
    3. Ardia, David & Hoogerheide, Lennart F., 2010. "Efficient Bayesian estimation and combination of GARCH-type models," MPRA Paper 22919, University Library of Munich, Germany.

  8. Nakatsuma Teruo, 1998. "A Markov-Chain Sampling Algorithm for GARCH Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 3(2), pages 1-13, July.

    Cited by:

    1. Song, Zefang & Song, Xinyuan & Li, Yuan, 2023. "Bayesian Analysis of ARCH-M model with a dynamic latent variable," Econometrics and Statistics, Elsevier, vol. 28(C), pages 47-62.
    2. Jan Henneke & Svetlozar Rachev & Frank Fabozzi & Metodi Nikolov, 2011. "MCMC-based estimation of Markov Switching ARMA-GARCH models," Applied Economics, Taylor & Francis Journals, vol. 43(3), pages 259-271.
    3. Monica Billio & Roberto Casarin & Anthony Osuntuyi, 2012. "Efficient Gibbs Sampling for Markov Switching GARCH Models," Working Papers 2012:35, Department of Economics, University of Venice "Ca' Foscari".
    4. David Ardia & Lennart F. Hoogerheide, 2010. "Bayesian Estimation of the GARCH(1,1) Model with Student-t Innovations," Tinbergen Institute Discussion Papers 10-045/4, Tinbergen Institute.
    5. Billio, Monica & Casarin, Roberto & Osuntuyi, Anthony, 2018. "Markov switching GARCH models for Bayesian hedging on energy futures markets," Energy Economics, Elsevier, vol. 70(C), pages 545-562.
    6. Bernardi, Mauro & Costola, Michele, 2019. "High-dimensional sparse financial networks through a regularised regression model," SAFE Working Paper Series 244, Leibniz Institute for Financial Research SAFE.
    7. Oscar Andrés Espinosa Acuna & Paola Andrea Vaca González, 2017. "Ajuste de modelos garch clásico y bayesiano con innovaciones t—student para el índice COLCAP," Revista de Economía del Caribe 17172, Universidad del Norte.
    8. Sarantis Tsiaplias, 2007. "A Metropolis-in-Gibbs Sampler for Estimating Equity Market Factors," Melbourne Institute Working Paper Series wp2007n18, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    9. Ardia, David & Hoogerheide, Lennart F., 2010. "Efficient Bayesian estimation and combination of GARCH-type models," MPRA Paper 22919, University Library of Munich, Germany.
    10. Martin Magris & Alexandros Iosifidis, 2023. "Variational Inference for GARCH-family Models," Papers 2310.03435, arXiv.org.
    11. Rodríguez Bernal, M. T. & Romero, Eva, 2013. "Data cloning estimation of GARCH and COGARCH models," DES - Working Papers. Statistics and Econometrics. WS ws132723, Universidad Carlos III de Madrid. Departamento de Estadística.
    12. Oscar Andrés Espinosa Acuna & Paola Andrea Vaca González, 2017. "Ajuste de modelos garch clásico y bayesiano con innovaciones t—student para el índice COLCAP," Revista de Economía del Caribe 17147, Universidad del Norte.

Chapters

    Sorry, no citations of chapters recorded.

Books

    Sorry, no citations of books recorded.

More information

Research fields, statistics, top rankings, if available.

Statistics

Access and download statistics for all items

Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 7 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-MST: Market Microstructure (3) 2017-07-09 2017-07-23 2018-09-03
  2. NEP-AGR: Agricultural Economics (2) 2021-05-24 2021-10-11
  3. NEP-ORE: Operations Research (2) 2021-08-23 2021-10-11
  4. NEP-ECM: Econometrics (1) 2021-08-23
  5. NEP-FMK: Financial Markets (1) 2016-05-28
  6. NEP-ISF: Islamic Finance (1) 2021-08-23

Corrections

All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. For general information on how to correct material on RePEc, see these instructions.

To update listings or check citations waiting for approval, Teruo Nakatsuma should log into the RePEc Author Service.

To make corrections to the bibliographic information of a particular item, find the technical contact on the abstract page of that item. There, details are also given on how to add or correct references and citations.

To link different versions of the same work, where versions have a different title, use this form. Note that if the versions have a very similar title and are in the author's profile, the links will usually be created automatically.

Please note that most corrections can take a couple of weeks to filter through the various RePEc services.

IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.