Recursive preferences, learning and large deviations

Chetan Dave; Kwok Ping Tsang

doi:10.1016/j.econlet.2014.06.014

Recursive preferences, learning and large deviations

Chetan Dave, Kwok Ping Tsang

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

6 Citations (Scopus)

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. © 2014 Elsevier B.V.

Original language	English
Pages (from-to)	329-334
Journal	Economics Letters
Volume	124
Issue number	3
DOIs	https://doi.org/10.1016/j.econlet.2014.06.014
Publication status	Published - Sept 2014
Externally published	Yes

Research Keywords

Adaptive learning
Asset prices
Fat tails
Large deviations
Recursive preferences

Access Output

10.1016/j.econlet.2014.06.014

Other Access Options

Check CityUHK Library

Permanent Link

Right click to copy link

Cite this

@article{f607438891c944ac83a7fbaf27dba0eb,

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. {\textcopyright} 2014 Elsevier B.V.",

keywords = "Adaptive learning, Asset prices, Fat tails, Large deviations, Recursive preferences",

author = "Chetan Dave and Tsang, {Kwok Ping}",

year = "2014",

month = sep,

doi = "10.1016/j.econlet.2014.06.014",

language = "English",

volume = "124",

pages = "329--334",

journal = "Economics Letters",

issn = "0165-1765",

publisher = "Elsevier B.V.",

number = "3",

}

TY - JOUR

T1 - Recursive preferences, learning and large deviations

AU - Dave, Chetan

AU - Tsang, Kwok Ping

PY - 2014/9

Y1 - 2014/9

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. © 2014 Elsevier B.V.

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. © 2014 Elsevier B.V.

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 - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84903891034&origin=recordpage

U2 - 10.1016/j.econlet.2014.06.014

DO - 10.1016/j.econlet.2014.06.014

M3 - RGC 21 - Publication in refereed journal

SN - 0165-1765

VL - 124

SP - 329

EP - 334

JO - Economics Letters

JF - Economics Letters

IS - 3

ER -

Recursive preferences, learning and large deviations

Abstract

Research Keywords

Access Output

Other Access Options

Permanent Link

Other Files and Links

Fingerprint

Cite this