Forecasting dynamic return distributions based on ordered binary choice
Result description
We present a simple approach to the forecasting of conditional probability distributions of asset returns. We work with a parsimonious specification of ordered binary choice regressions that imposes a connection on sign predictability across different quantiles. The model forecasts the future conditional probability distributions of returns quite precisely when using a past indicator and a past volatility proxy as predictors. The direct benefits of the model are revealed in an empirical application to the 29 most liquid U.S. stocks. The forecast probability distribution is translated to significant economic gains in a simple trading strategy. Our approach can also be useful in many other applications in which conditional distribution forecasts are desired.
Keywords
The result's identifiers
Result code in IS VaVaI
Alternative codes found
RIV/67985998:_____/19:00504930 RIV/00216208:11230/19:10395558 RIV/00216208:11640/19:00517974
Result on the web
DOI - Digital Object Identifier
Alternative languages
Result language
angličtina
Original language name
Forecasting dynamic return distributions based on ordered binary choice
Original language description
We present a simple approach to the forecasting of conditional probability distributions of asset returns. We work with a parsimonious specification of ordered binary choice regressions that imposes a connection on sign predictability across different quantiles. The model forecasts the future conditional probability distributions of returns quite precisely when using a past indicator and a past volatility proxy as predictors. The direct benefits of the model are revealed in an empirical application to the 29 most liquid U.S. stocks. The forecast probability distribution is translated to significant economic gains in a simple trading strategy. Our approach can also be useful in many other applications in which conditional distribution forecasts are desired.
Czech name
—
Czech description
—
Classification
Type
Jimp - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
50202 - Applied Economics, Econometrics
Result continuities
Project
GA16-14179S: New measures of dependence between economic variables
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
International Journal of Forecasting
ISSN
0169-2070
e-ISSN
—
Volume of the periodical
35
Issue of the periodical within the volume
3
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
13
Pages from-to
823-835
UT code for WoS article
000474317500001
EID of the result in the Scopus database
2-s2.0-85062917077
Basic information
Result type
Jimp - Article in a specialist periodical, which is included in the Web of Science database
OECD FORD
Applied Economics, Econometrics
Year of implementation
2019