Recursive preferences, learning and large deviations

Chetan Dave, Kwok Ping Tsang

Research output: Contribution to journalArticle

Abstract

We estimate the relative contribution of recursive preferences versus adaptive learning in accounting for the tail thickness of price-dividends/rents ratios. We find that both of these sources of volatility account for volatility in liquid (stocks) but not illiquid (housing) assets.

Original languageEnglish (US)
Pages (from-to)329-334
Number of pages6
JournalEconomics Letters
Volume124
Issue number3
DOIs
StatePublished - Jan 1 2014

Fingerprint

Large deviations
Recursive preferences
Dividends
Adaptive learning
Assets
Rent

Keywords

  • Adaptive learning
  • Asset prices
  • Fat tails
  • Large deviations
  • Recursive preferences

ASJC Scopus subject areas

  • Economics and Econometrics
  • Finance

Cite this

Recursive preferences, learning and large deviations. / Dave, Chetan; Tsang, Kwok Ping.

In: Economics Letters, Vol. 124, No. 3, 01.01.2014, p. 329-334.

Research output: Contribution to journalArticle

Dave, Chetan ; Tsang, Kwok Ping. / Recursive preferences, learning and large deviations. In: Economics Letters. 2014 ; Vol. 124, No. 3. pp. 329-334.
@article{cd18eca1b6c546a08af6a87075dc6f16,
title = "Recursive preferences, learning and large deviations",
abstract = "We estimate the relative contribution of recursive preferences versus adaptive learning in accounting for the tail thickness of price-dividends/rents ratios. We find that both of these sources of volatility account for volatility in liquid (stocks) but not illiquid (housing) assets.",
keywords = "Adaptive learning, Asset prices, Fat tails, Large deviations, Recursive preferences",
author = "Chetan Dave and Tsang, {Kwok Ping}",
year = "2014",
month = "1",
day = "1",
doi = "10.1016/j.econlet.2014.06.014",
language = "English (US)",
volume = "124",
pages = "329--334",
journal = "Economics Letters",
issn = "0165-1765",
publisher = "Elsevier",
number = "3",

}

TY - JOUR

T1 - Recursive preferences, learning and large deviations

AU - Dave, Chetan

AU - Tsang, Kwok Ping

PY - 2014/1/1

Y1 - 2014/1/1

N2 - We estimate the relative contribution of recursive preferences versus adaptive learning in accounting for the tail thickness of price-dividends/rents ratios. We find that both of these sources of volatility account for volatility in liquid (stocks) but not illiquid (housing) assets.

AB - We estimate the relative contribution of recursive preferences versus adaptive learning in accounting for the tail thickness of price-dividends/rents ratios. We find that both of these sources of volatility account for volatility in liquid (stocks) but not illiquid (housing) assets.

KW - Adaptive learning

KW - Asset prices

KW - Fat tails

KW - Large deviations

KW - Recursive preferences

UR - http://www.scopus.com/inward/record.url?scp=84903891034&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84903891034&partnerID=8YFLogxK

U2 - 10.1016/j.econlet.2014.06.014

DO - 10.1016/j.econlet.2014.06.014

M3 - Article

VL - 124

SP - 329

EP - 334

JO - Economics Letters

JF - Economics Letters

SN - 0165-1765

IS - 3

ER -