IDEAS home Printed from https://ideas.repec.org/r/oup/rfinst/v26y2013i11p2876-2915.html
   My bibliography  Save this item

Long-Run Risk and the Persistence of Consumption Shocks

Citations

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


Cited by:

  1. Gourieroux, Christian & Jasiak, Joann, 2010. "Inference for Noisy Long Run Component Process," MPRA Paper 98987, University Library of Munich, Germany.
  2. Jozef Baruník & Evžen KoÄ enda, 2019. "Total, Asymmetric and Frequency Connectedness between Oil and Forex Markets," The Energy Journal, , vol. 40(2_suppl), pages 157-174, December.
  3. Ian Dew-Becker & Stefano Giglio, 2016. "Asset Pricing in the Frequency Domain: Theory and Empirics," The Review of Financial Studies, Society for Financial Studies, vol. 29(8), pages 2029-2068.
  4. Baruník, Jozef & Kurka, Josef, 2024. "Risks of heterogeneously persistent higher moments," International Review of Financial Analysis, Elsevier, vol. 96(PA).
  5. Bandi, Federico M. & Tamoni, Andrea, 2023. "Business-cycle consumption risk and asset prices," Journal of Econometrics, Elsevier, vol. 237(2).
  6. Pedro Bordalo & Nicola Gennaioli & Rafael La Porta & Andrei Shleifer, 2024. "Belief Overreaction and Stock Market Puzzles," Journal of Political Economy, University of Chicago Press, vol. 132(5), pages 1450-1484.
  7. Lovcha, Yuliya & Perez-Laborda, Alejandro, 2020. "Dynamic frequency connectedness between oil and natural gas volatilities," Economic Modelling, Elsevier, vol. 84(C), pages 181-189.
  8. Alexis Direr, 2023. "Portfolio Choice With Time Horizon Risk," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 26(06n07), pages 1-19, November.
  9. Bandi, Federico M. & Su, Yinan, 2025. "Conditional spectral methods," Journal of Econometrics, Elsevier, vol. 248(C).
  10. David Dillenberger & Daniel Gottlieb & Pietro Ortoleva, 2017. "Stochastic Impatience and the Separation of Time and Risk Preferences," PIER Working Paper Archive 20-026, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 05 Jul 2020.
  11. Peress, Joël & Dong, Xi & KANG, NAMHO, 2020. "Fast and Slow Arbitrage: Fund Flows and Mispricing in the Frequency Domain," CEPR Discussion Papers 15235, C.E.P.R. Discussion Papers.
  12. Ilaria Piatti & Fabio Trojani, 2020. "Dividend Growth Predictability and the Price–Dividend Ratio," Management Science, INFORMS, vol. 66(1), pages 130-158, January.
  13. Wang, Xunxiao, 2020. "Frequency dynamics of volatility spillovers among crude oil and international stock markets: The role of the interest rate," Energy Economics, Elsevier, vol. 91(C).
  14. Barigozzi, Matteo & Hallin, Marc & Soccorsi, Stefano & von Sachs, Rainer, 2021. "Time-varying general dynamic factor models and the measurement of financial connectedness," Journal of Econometrics, Elsevier, vol. 222(1), pages 324-343.
  15. Xyngis, Georgios, 2017. "Business-cycle variation in macroeconomic uncertainty and the cross-section of expected returns: Evidence for scale-dependent risks," Journal of Empirical Finance, Elsevier, vol. 44(C), pages 43-65.
  16. Dergunov, Ilya & Meinerding, Christoph & Schlag, Christian, 2019. "Extreme inflation and time-varying consumption growth," Discussion Papers 16/2019, Deutsche Bundesbank.
  17. Teply, Petr & Kvapilikova, Ivana, 2017. "Measuring systemic risk of the US banking sector in time-frequency domain," The North American Journal of Economics and Finance, Elsevier, vol. 42(C), pages 461-472.
  18. Mensi, Walid & Al-Yahyaee, Khamis Hamed & Vo, Xuan Vinh & Kang, Sang Hoon, 2021. "Modeling the frequency dynamics of spillovers and connectedness between crude oil and MENA stock markets with portfolio implications," Economic Analysis and Policy, Elsevier, vol. 71(C), pages 397-419.
  19. Meissner, Thomas & Pfeiffer, Philipp, 2022. "Measuring preferences over the temporal resolution of consumption uncertainty," Journal of Economic Theory, Elsevier, vol. 200(C).
  20. Bandi, F.M. & Perron, B. & Tamoni, A. & Tebaldi, C., 2019. "The scale of predictability," Journal of Econometrics, Elsevier, vol. 208(1), pages 120-140.
  21. López, Ramón E. & Yoon, Sang W., 2020. "Sustainable development: Structural transformation and the consumer demand," Structural Change and Economic Dynamics, Elsevier, vol. 52(C), pages 22-38.
  22. Wang, Xunxiao & Wang, Yudong, 2019. "Volatility spillovers between crude oil and Chinese sectoral equity markets: Evidence from a frequency dynamics perspective," Energy Economics, Elsevier, vol. 80(C), pages 995-1009.
  23. Stein, Tobias, 2024. "Forecasting the equity premium with frequency-decomposed technical indicators," International Journal of Forecasting, Elsevier, vol. 40(1), pages 6-28.
  24. Fosten, Jack, 2019. "CO2 emissions and economic activity: A short-to-medium run perspective," Energy Economics, Elsevier, vol. 83(C), pages 415-429.
  25. Faria, Gonçalo & Verona, Fabio, 2018. "Forecasting stock market returns by summing the frequency-decomposed parts," Journal of Empirical Finance, Elsevier, vol. 45(C), pages 228-242.
  26. Brož, Václav & Kočenda, Evžen, 2022. "Mortgage-related bank penalties and systemic risk among U.S. banks," Journal of International Money and Finance, Elsevier, vol. 122(C).
  27. Faria, Gonçalo & Verona, Fabio, 2024. "Enhancing forecast accuracy through frequencydomain combination: Applications to financial and economic indicators," Bank of Finland Research Discussion Papers 14/2024, Bank of Finland.
  28. Chun, Dohyun & Cho, Hoon & Kim, Jihun, 2022. "The relationship between carbon-intensive fuel and renewable energy stock prices under the emissions trading system," Energy Economics, Elsevier, vol. 114(C).
  29. Thomas Conlon & John Cotter & Ramazan Gençay, 2015. "Long-run international diversification," Working Papers 201502, Geary Institute, University College Dublin.
  30. Jozef Baruník & Matěj Nevrla, 2023. "Quantile Spectral Beta: A Tale of Tail Risks, Investment Horizons, and Asset Prices," Journal of Financial Econometrics, Oxford University Press, vol. 21(5), pages 1590-1646.
  31. Conlon, Thomas & Cotter, John & Gençay, Ramazan, 2018. "Long-run wavelet-based correlation for financial time series," European Journal of Operational Research, Elsevier, vol. 271(2), pages 676-696.
  32. Julian Thimme, 2017. "Intertemporal Substitution In Consumption: A Literature Review," Journal of Economic Surveys, Wiley Blackwell, vol. 31(1), pages 226-257, February.
  33. Ilya Dergunov & Christoph Meinerding & Christian Schlag, 2023. "Extreme Inflation and Time-Varying Expected Consumption Growth," Management Science, INFORMS, vol. 69(5), pages 2972-3002, May.
  34. Roh, Tai-Yong & Lee, Changjun & Min, Byoung-Kyu, 2019. "Consumption growth predictability and asset prices," Journal of Empirical Finance, Elsevier, vol. 51(C), pages 95-118.
  35. Matteo Barigozzi & Marc Hallin & Stefano Soccorsi, 2019. "Time-Varying General Dynamic Factor Models and the Measurement of Financial Connectedness," Working Papers ECARES 2019-09, ULB -- Universite Libre de Bruxelles.
  36. Mensi, Walid & Al Rababa'a, Abdel Razzaq & Alomari, Mohammad & Vo, Xuan Vinh & Kang, Sang Hoon, 2022. "Dynamic frequency volatility spillovers and connectedness between strategic commodity and stock markets: US-based sectoral analysis," Resources Policy, Elsevier, vol. 79(C).
  37. Degiannakis, Stavros & Filis, George & Hassani, Hossein, 2018. "Forecasting global stock market implied volatility indices," Journal of Empirical Finance, Elsevier, vol. 46(C), pages 111-129.
  38. Federico Severino, 2016. "Isometric operators on Hilbert spaces and Wold decomposition of stationary time series," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 39(2), pages 203-234, November.
  39. Liu, Zhenhua & Ji, Qiang & Zhai, Pengxiang & Ding, Zhihua, 2023. "Asymmetric and time-frequency volatility connectedness between China and international crude oil markets with portfolio implications," Research in International Business and Finance, Elsevier, vol. 66(C).
  40. Caporin, Massimiliano & Naeem, Muhammad Abubakr & Arif, Muhammad & Hasan, Mudassar & Vo, Xuan Vinh & Hussain Shahzad, Syed Jawad, 2021. "Asymmetric and time-frequency spillovers among commodities using high-frequency data," Resources Policy, Elsevier, vol. 70(C).
  41. Clark Lundberg, 2019. "Identifying horizon-based heterogeneity in the cross section of portfolio returns," Economics Bulletin, AccessEcon, vol. 39(2), pages 1163-1175.
  42. Neuhierl, Andreas & Varneskov, Rasmus T., 2021. "Frequency dependent risk," Journal of Financial Economics, Elsevier, vol. 140(2), pages 644-675.
  43. Lovcha, Yuliya & Perez-Laborda, Alejandro, 2022. "Long-memory and volatility spillovers across petroleum futures," Energy, Elsevier, vol. 243(C).
  44. Adlai Fisher & Charles Martineau & Jinfei Sheng, 2022. "Macroeconomic Attention and Announcement Risk Premia," The Review of Financial Studies, Society for Financial Studies, vol. 35(11), pages 5057-5093.
  45. Gunay, Samet & Dömötör, Barbara & Víg, Attila András, 2025. "Investigation of emerging market stress under various frequency bands: Evidence from FX market uncertainty and liquidity," Emerging Markets Review, Elsevier, vol. 65(C).
  46. Bu, Di & Liao, Yin & Shi, Jing & Peng, Hongfeng, 2019. "Dynamic expected shortfall: A spectral decomposition of tail risk across time horizons," Journal of Economic Dynamics and Control, Elsevier, vol. 108(C).
  47. Malkhozov, Aytek & Tamoni, Andrea, 2015. "News shocks and asset prices," LSE Research Online Documents on Economics 62004, London School of Economics and Political Science, LSE Library.
  48. Ahmad Yamin S & Paya Ivan, 2020. "Temporal aggregation of random walk processes and implications for economic analysis," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 24(2), pages 1-20, April.
  49. Mensi, Walid & Vo, Xuan Vinh & Kang, Sang Hoon, 2021. "Multiscale spillovers, connectedness, and portfolio management among precious and industrial metals, energy, agriculture, and livestock futures," Resources Policy, Elsevier, vol. 74(C).
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.