Reading the Candlesticks:
An OK Estimator for Volatility
Paper by J. Li, D. Wang and Q. Zhang
(LWZ)
Discussion by F.X. Diebold
Society for Financial Econometrics
February 21, 2002
Reading the Candlesticks:
An OK Estimator for Volatility
Paper by J. Li, D. Wang and Q. Zhang
(LWZ)
Discussion by F.X. Diebold
Society for Financial Econometrics
February 21, 2002
(***) Consider a different title…
Classic
Traditions: University of Chicago, Journal
of Business, Al Madansky, …
n CLI, CCI analyses related to modern
macro/BC nowcasting (Zarnowitz, Neftci, ...)
n Range-based volatility estimation related
to modern financial volatility nowcasting
The Extreme
Value Method for Estimating the Variance of the Rate of Return
Author(s): Michael
Parkinson
Source: The Journal of Business , Jan., 1980, Vol. 53, No. 1 (Jan., 1980), pp. 61-65
On the Estimation of Security Price
Volatilities from Historical Data
Author(s): Mark B. Garman
and Michael J. Klass
Source: The Journal of Business , Jan., 1980, Vol. 53, No. 1 (Jan., 1980), pp. 67-78
Others have extended:
n HLC-based estimation (e.g., Beckers,
1983; Rogers and Satchell, 1991)
n HLOC-based estimation (e.g., Yang and
Zhang, 2000)
(***) Should
be discussed
In Yang and Zhang (2000):
VOL = O - .383
(***) LWZ results have strong
resemblance
In LWZ:
VOL
= λ1
(***) Restrictive ?
VOL* = 0.811
How does the range fit in?
Efficiency hierarchy
(worst to best):
r^2
|r|
HL range
HLC
HLOC
“large-k” RV
r^2 or |r|: r=0 implies vol=0
r^2 or |r|: Even when r non-zero,
very different paths can be scored the same
Range: The key vol info is embedded
different
days can be scored the same
Range: Even the range can be
tricked
(Only large-k RV can’t be tricked…)
Why care about the range?
(if only large-k RV can’t be
tricked…)
n Effortless yet highly efficient
n Robust to microstructure noise
(bias is just average B/A spread)
n Available over long historical
periods
(and risk premia are all about recessions)
Also, using
range improves large-k RV
Christensen
and Podolskij (2007)
(* Needs more discussion)
Not so
compelling in large-k contexts?
What to do
when you can’t do
(or don’t want to do) large-k RV?
n Fixed(small)-k r^2-based RV (Bollerslev,
Li and Liao, 2021 (BLL))
n Fixed(small)-k range-based RV (LWZ)
(More compelling than large-k range-based RV:
Efficiency, robustness, …)
RV Volatility
Proxies and Treatment of k
k: |
Large k |
Fixed(Small) k |
Vol Proxy: |
|
|
r^2 |
ABDL 2001 BNS 2002 ABDL 2003 |
BLL 2021 |
Range |
CP 2007 |
LWZ 2022 |
ABDL 2001 Andersen,
Bollerslev, Diebold and Labys, JASA
BNS 2002 Barndorff-Nielsen
and Shephard, JRSS
ABDL 2003 Andersen,
Bollerslev, Diebold and Labys, Ectca
CP 2007 Christensen
and Podolskij, JoE
BLL 2021 Bollerslev,
Li, and Liao, JoE
LWZ
2022 Li, Wang and Zhang, unpublished – NICE!
No comments:
Post a Comment
Note: Only a member of this blog may post a comment.