Saturday, June 11, 2022

Great Summer Courses in Glasgow

Summer School Sept 5-9, Adam Smith Business School, University of Glasgow:

Kamil Yilmaz will teach a two-day Network Connectedness course Sept 5-6, covering both methods and applications ("Financial and Macroeconomic Connectedness: A Network Approach to Measurement and Monitoring").

Refet Gurkaynak will teach a two-day High-Frequency Finance course Sept 7-8, again covering both methods and applications ("Asset Price Reactions to News: High Frequency Methods and Applications").

Both courses will be helpful for researchers and policy analysts at universities, central banks, international policy institutes, and think tanks.

Looks great!

Monday, February 28, 2022

New and Novel ARCH Model Application (Seriously)

 The Variability and Volatility of Sleep: An Archetypal Approach

By:Hamermesh, Daniel S. (Barnard College); Pfann, Gerard A. (Maastricht University)
Abstract:Using Dutch time-diary data from 1975-2005 covering over 10,000 respondents for 7 consecutive days each, we show that individuals' sleep time exhibits both variability and volatility characterized by stationary autoregressive conditional heteroscedasticity: The absolute values of deviations from a person's average sleep on one day are positively correlated with those on the next day. Sleep is more variable on weekends and among people with less education, who are younger and who do not have young children at home. Volatility is greater among parents with young children, slightly greater among men than women, but independent of other demographics. A theory of economic incentives to minimize the dispersion of sleep predicts that higher-wage workers will exhibit less dispersion, a result demonstrated using extraneous estimates of earnings equations to impute wage rates. Volatility in sleep spills over onto volatility in other personal activities, with no reverse causation onto sleep. The results illustrate a novel dimension of economic inequality and could be applied to a wide variety of human behavior and biological processes.
Keywords:time use, ARCH, economic incentives in biological processes, volatility
JEL:C22 J22 I14

Long-Memory Neural Nets

 Fractional SDE-Net: Generation of Time Series Data with Long-term Memory

By:Kohei HayashiKei Nakagawa
Abstract:In this paper, we focus on generation of time-series data using neural networks. It is often the case that input time-series data, especially taken from real financial markets, is irregularly sampled, and its noise structure is more complicated than i.i.d. type. To generate time series with such a property, we propose fSDE-Net: neural fractional Stochastic Differential Equation Network. It generalizes the neural SDE model by using fractional Brownian motion with Hurst index larger than half, which exhibits long-term memory property. We derive the solver of fSDE-Net and theoretically analyze the existence and uniqueness of the solution to fSDE-Net. Our experiments demonstrate that the fSDE-Net model can replicate distributional properties well.

Tuesday, February 22, 2022

Range-Based ("Candlestick") Volatility Estimation Slides


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 C + 1.364 HL + 0.019 HLC

(***) LWZ results have strong resemblance


VOL  = λ1 (H-L) + λ2 |C-O| (by assumption)

(***) Restrictive ?

VOL* = 0.811 (H-L) – 0.369 |C-O|

How does the range fit in?

Efficiency hierarchy (worst to best):



HL range



“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


Large k

Fixed(Small) k

Vol Proxy:




ABDL 2001

BNS 2002

ABDL 2003

BLL 2021


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!

Saturday, February 19, 2022

Range-Based Volatility Seminar, MONDAY 2/21

 Should be very fun!  

SoFiE Seminar with Jia Li and Francis X. Diebold
Host:Dacheng Xiu (The University of Chicago Booth School of Business)


Jia Li (Duke University)


"Reading the Candlesticks: An OK Estimator for Volatility"


Francis X. Diebold (University of Pennsylvania)


February 21, 2022


11am New York / 8am San Diego / 4pm London / 5pm Paris / 12am Beijing

Zoom Link:


A link to a video recording will soon after the event.

SoFiE Seminar Submissions

The SoFiE Seminar Series welcomes the submission of papers on any aspect of financial econometrics for possible inclusion in the series. Submissions will be reviewed by the Series’ scientific committee and will be considered on a rolling basis; there is no deadline for submissions.

-           To submit a paper, email a PDF version of the paper

-           Complete papers are preferred to incomplete papers or summaries.

-           Submissions do not need to be blinded.

-           Papers should not already be accepted for publication when submitted, though they can have been presented in other seminar series or conferences/workshops.

-           Due to resource constraints, it is impossible to respond to everyone who submits a paper; you will be contacted only if your paper is selected for inclusion in the series.

-           Submissions from junior authors and members of groups that are under-represented in the field are particularly encouraged.

-           Seminars are held every other Monday at 11am New York time.

Zoom InvitationHi there,You are invited to a Zoom webinar.When: Feb 21, 2022 11:00 AM Eastern Time (US and Canada)Topic: SoFiE Seminar - Jia Li and Francis X. Diebold - February 21 2022, 11:00amPlease click the link below to join the webinar: One tap mobile :    US: +16465588656,,93619106089#  or +13126266799,,93619106089#Or Telephone:    Dial(for higher quality, dial a number based on your current location):        US: +1 646 558 8656  or +1 312 626 6799  or +1 301 715 8592  or +1 669 900 6833  or +1 253 215 8782  or +1 346 248 7799Webinar ID: 936 1910 6089    International numbers available: an H.323/SIP room system:    H.323: (US West) (US East) (China) (India Mumbai) (India Hyderabad) (Amsterdam Netherlands) (Germany) (Australia Sydney) (Australia Melbourne) (Hong Kong SAR) (Singapore) (Brazil) (Mexico) (Canada Toronto) (Canada Vancouver) (Japan Tokyo) (Japan Osaka)    Meeting ID: 936 1910 6089    SIP: 
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