IDEAS home Printed from https://ideas.repec.org/r/eee/reveco/v27y2013icp209-223.html

Return distribution predictability and its implications for portfolio selection

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Li, Jinfang, 2019. "Sentiment trading, informed trading and dynamic asset pricing," The North American Journal of Economics and Finance, Elsevier, vol. 47(C), pages 210-222.
  2. Alexander, Carol & Han, Yang & Meng, Xiaochun, 2023. "Static and dynamic models for multivariate distribution forecasts: Proper scoring rule tests of factor-quantile versus multivariate GARCH models," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1078-1096.
  3. Demirer, Rıza & Ferrer, Román & Shahzad, Syed Jawad Hussain, 2020. "Oil price shocks, global financial markets and their connectedness," Energy Economics, Elsevier, vol. 88(C).
  4. Bodnar, Taras & Parolya, Nestor & Schmid, Wolfgang, 2015. "On the exact solution of the multi-period portfolio choice problem for an exponential utility under return predictability," European Journal of Operational Research, Elsevier, vol. 246(2), pages 528-542.
  5. Bartosz Łamasz & Natalia Iwaszczuk, 2020. "The Impact of Implied Volatility Fluctuations on Vertical Spread Option Strategies: The Case of WTI Crude Oil Market," Energies, MDPI, vol. 13(20), pages 1-23, October.
  6. Dina Azhgaliyeva & Ranjeeta Mishra & Zhanna Kapsalyamova, 2021. "Oil Price Shocks and Green Bonds: A Longitudinal Multilevel Model," ADBI Working Papers 1278, Asian Development Bank Institute.
  7. Chen, Rongda & Zhou, Hanxian & Yu, Lean & Jin, Chenglu & Zhang, Shuonan, 2021. "An efficient method for pricing foreign currency options," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 74(C).
  8. Jasman Tuyon & Zamri Ahmad, 2021. "Dynamic risk attributes in Malaysia stock markets: Behavioural finance insights," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(4), pages 5793-5814, October.
  9. Campisi, Giovanni & Muzzioli, Silvia & De Baets, Bernard, 2024. "A comparison of machine learning methods for predicting the direction of the US stock market on the basis of volatility indices," International Journal of Forecasting, Elsevier, vol. 40(3), pages 869-880.
  10. Li, Jinfang, 2014. "Multi-period sentiment asset pricing model with information," International Review of Economics & Finance, Elsevier, vol. 34(C), pages 118-130.
  11. Gebka, Bartosz & Wohar, Mark E., 2019. "Stock return distribution and predictability: Evidence from over a century of daily data on the DJIA index," International Review of Economics & Finance, Elsevier, vol. 60(C), pages 1-25.
  12. Yaw‐Huei Wang & Kuang‐Chieh Yen, 2019. "The information content of the implied volatility term structure on future returns," European Financial Management, European Financial Management Association, vol. 25(2), pages 380-406, March.
  13. Gonzalez-Perez, Maria T., 2015. "Model-free volatility indexes in the financial literature: A review," International Review of Economics & Finance, Elsevier, vol. 40(C), pages 141-159.
  14. Yu, Jing-Rung & Paul Chiou, Wan-Jiun & Lee, Wen-Yi & Lin, Shun-Ji, 2020. "Portfolio models with return forecasting and transaction costs," International Review of Economics & Finance, Elsevier, vol. 66(C), pages 118-130.
  15. Shamsi Zamenjani, Azam, 2021. "Do financial variables help predict the conditional distribution of the market portfolio?," Journal of Empirical Finance, Elsevier, vol. 62(C), pages 327-345.
  16. Jasman Tuyon & Zamri Ahmada, 2016. "Behavioural finance perspectives on Malaysian stock market efficiency," Borsa Istanbul Review, Research and Business Development Department, Borsa Istanbul, vol. 16(1), pages 43-61, March.
  17. Gebka, Bartosz & Wohar, Mark E., 2018. "The predictive power of the yield spread for future economic expansions: Evidence from a new approach," Economic Modelling, Elsevier, vol. 75(C), pages 181-195.
  18. Semenov, Andrei, 2015. "The small-cap effect in the predictability of individual stock returns," International Review of Economics & Finance, Elsevier, vol. 38(C), pages 178-197.
  19. Swanson, Norman R. & Urbach, Richard, 2015. "Prediction and simulation using simple models characterized by nonstationarity and seasonality," International Review of Economics & Finance, Elsevier, vol. 40(C), pages 312-323.
  20. Giovanni Campisi & Silvia Muzzioli, 2021. "Designing volatility indices for Austria, Finland and Spain," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 35(3), pages 369-455, September.
  21. Liang, Hanchao & Yang, Chunpeng & Cai, Chuangqun, 2017. "Beauty contest, bounded rationality, and sentiment pricing dynamics," Economic Modelling, Elsevier, vol. 60(C), pages 71-80.
  22. Le, Trung H., 2021. "International portfolio allocation: The role of conditional higher moments," International Review of Economics & Finance, Elsevier, vol. 74(C), pages 33-57.
  23. Corredor, Pilar & Ferrer, Elena & Santamaria, Rafael, 2013. "Investor sentiment effect in stock markets: Stock characteristics or country-specific factors?," International Review of Economics & Finance, Elsevier, vol. 27(C), pages 572-591.
  24. Trung H. Le, 2024. "Forecasting VaR and ES in emerging markets: The role of time‐varying higher moments," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(2), pages 402-414, March.
  25. Simaan, Majeed & Simaan, Yusif & Tang, Yi, 2018. "Estimation error in mean returns and the mean-variance efficient frontier," International Review of Economics & Finance, Elsevier, vol. 56(C), pages 109-124.
  26. Chen, Shun & Ge, Lei, 2021. "A learning-based strategy for portfolio selection," International Review of Economics & Finance, Elsevier, vol. 71(C), pages 936-942.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.