---
_id: '66449'
abstract:
- lang: eng
text: "This paper evaluates the forecasting performance of an expanded class of
(semi-)parametric \r\nGARCH models belonging to the EGARCH family (EGF), including
recently introduced long \r\nand short memory specifications and their semiparametric
extensions. The semiparametric \r\nvariants employ a multiplicative volatility
decomposition into conditional and slowly varying \r\nunconditional components,
where the latter is estimated via a data-driven local polynomial \r\nsmoother
to accommodate non-stationarities commonly observed in financial time series.
Based \r\non the revised Basel Committee framework for market-risk assessment,
all models are capable \r\nof producing rolling one-day-ahead forecasts for Value
at Risk (VaR) and Expected Shortfall \r\n(ES) under a wide range of symmetric
and skewed innovation distributions. Their forecasting \r\naccuracy is examined
using the regulatory traffic light tests for VaR and the recently developed \r\nES-specific
traffic light procedure, complemented by the regulatory loss function. In addition,
\r\nmodel selection incorporates both a recently proposed corrected firm-oriented
loss function that \r\naccounts for opportunity costs and the Weighted Absolute
Deviation (WAD) criterion. The \r\nempirical comparison demonstrates that (semiparametric)
long memory GARCH models - \r\nparticularly those combining fractional dynamics
with nonparametric scale adjustments - can \r\nserve as valuable alternatives
to traditional parametric short memory models, offering more \r\nstable volatility
estimates and improved tail-risk forecasts for practical risk management \r\napplications."
author:
- first_name: Dominik Christian
full_name: Hanke, Dominik Christian
id: '63677'
last_name: Hanke
- first_name: André
full_name: Uhde, André
id: '36049'
last_name: Uhde
- first_name: Yuanhua
full_name: Feng, Yuanhua
id: '20760'
last_name: Feng
citation:
ama: Hanke DC, Uhde A, Feng Y. Application of Novel Exponential (Semi-)Parametric
Short and Long Memory GARCH Models under Regulatory Requirements of Basel III.;
2026.
apa: Hanke, D. C., Uhde, A., & Feng, Y. (2026). Application of Novel Exponential
(Semi-)Parametric Short and Long Memory GARCH Models under Regulatory Requirements
of Basel III.
bibtex: '@book{Hanke_Uhde_Feng_2026, title={Application of Novel Exponential (Semi-)Parametric
Short and Long Memory GARCH Models under Regulatory Requirements of Basel III},
author={Hanke, Dominik Christian and Uhde, André and Feng, Yuanhua}, year={2026}
}'
chicago: Hanke, Dominik Christian, André Uhde, and Yuanhua Feng. Application
of Novel Exponential (Semi-)Parametric Short and Long Memory GARCH Models under
Regulatory Requirements of Basel III, 2026.
ieee: D. C. Hanke, A. Uhde, and Y. Feng, Application of Novel Exponential (Semi-)Parametric
Short and Long Memory GARCH Models under Regulatory Requirements of Basel III.
2026.
mla: Hanke, Dominik Christian, et al. Application of Novel Exponential (Semi-)Parametric
Short and Long Memory GARCH Models under Regulatory Requirements of Basel III.
2026.
short: D.C. Hanke, A. Uhde, Y. Feng, Application of Novel Exponential (Semi-)Parametric
Short and Long Memory GARCH Models under Regulatory Requirements of Basel III,
2026.
date_created: 2026-07-13T09:21:49Z
date_updated: 2026-07-16T09:07:19Z
ddc:
- '330'
department:
- _id: '200'
- _id: '186'
file:
- access_level: open_access
content_type: application/pdf
creator: dhanke
date_created: 2026-07-13T09:21:45Z
date_updated: 2026-07-13T09:21:45Z
file_id: '66450'
file_name: TAF_WP_105_HankeUhdeFeng2026.pdf
file_size: 1830858
relation: main_file
file_date_updated: 2026-07-13T09:21:45Z
has_accepted_license: '1'
jel:
- C22
- C4
- C5
- C6
- B26
keyword:
- semiparametric GARCH extension
- data-driven local polynomial smoother
- long memory
- GARCH models
- Value at Risk
- Expected Shortfall
- traffic light test
- backtesting
- Basel III
- market risk
language:
- iso: eng
oa: '1'
status: public
title: Application of Novel Exponential (Semi-)Parametric Short and Long Memory GARCH
Models under Regulatory Requirements of Basel III
type: working_paper
user_id: '63677'
year: '2026'
...
---
_id: '66447'
abstract:
- lang: eng
text: "Volatility modeling is utilized across numerous fields including finance,
environmental studies, and \r\nsocial sciences. It is particularly relevant in
scenarios where understanding and predicting conditional \r\nvariability is crucial,
such as when dealing with incremental or time-dependent data. In this paper, novel
\r\nshort and long memory volatility models of the EGARCH family are introduced
and analyzed, which \r\nare closely related to the well-established EGARCH model
proposed by Nelson (1991) but share \r\ndesirable theoretical properties in several
dimensions. Recently developed members of the so-called \r\nEGARCH family, which
introduces a modulus-log transformation proposed by John and Draper (1980) \r\nand
a power transformation for the size and magnitude effect to tackle the problem
with near-zero \r\ninnovations and the asymmetric impact of positive and negative
shocks on the volatility, are discussed. \r\nAfter a theoretical discussion of
the proposed and related volatility models, the practical performance \r\nof the
elaborated volatility models is compared to well-established and traditional GARCH
approaches. \r\nA general QMLE algorithm is proposed to estimate the model parameters.
The practical relevance of the \r\nadvanced models is illustrated through a comparative
study. By applying these volatility models to a \r\nvariety of international stock
index returns, this paper identifies market-specific characteristics as well \r\nas
unique strengths and weaknesses of discussed volatility models. Although the practical
performance \r\nof the recently introduced models is comparable to those obtained
by the traditional EGARCH model, \r\nthey generally outperform traditional non-exponential
volatility models used as benchmarks and thus \r\nprovide a useful alternative
to existing short and long memory volatility models. "
author:
- first_name: Dominik Christian
full_name: Hanke, Dominik Christian
id: '63677'
last_name: Hanke
- first_name: Yuanhua
full_name: Feng, Yuanhua
id: '20760'
last_name: Feng
- first_name: André
full_name: Uhde, André
id: '36049'
last_name: Uhde
citation:
ama: Hanke DC, Feng Y, Uhde A. Comparing the Behaviors of Some Original Short
and Long Memory Exponential Volatility Models.; 2026.
apa: Hanke, D. C., Feng, Y., & Uhde, A. (2026). Comparing the behaviors of
some original short and long memory exponential volatility models.
bibtex: '@book{Hanke_Feng_Uhde_2026, title={Comparing the behaviors of some original
short and long memory exponential volatility models}, author={Hanke, Dominik
Christian and Feng, Yuanhua and Uhde, André}, year={2026} }'
chicago: Hanke, Dominik Christian, Yuanhua Feng, and André Uhde. Comparing the
Behaviors of Some Original Short and Long Memory Exponential Volatility Models,
2026.
ieee: D. C. Hanke, Y. Feng, and A. Uhde, Comparing the behaviors of some original
short and long memory exponential volatility models. 2026.
mla: Hanke, Dominik Christian, et al. Comparing the Behaviors of Some Original
Short and Long Memory Exponential Volatility Models. 2026.
short: D.C. Hanke, Y. Feng, A. Uhde, Comparing the Behaviors of Some Original Short
and Long Memory Exponential Volatility Models, 2026.
date_created: 2026-07-13T09:15:09Z
date_updated: 2026-07-16T09:07:24Z
ddc:
- '330'
department:
- _id: '186'
file:
- access_level: open_access
content_type: application/pdf
creator: dhanke
date_created: 2026-07-13T09:14:58Z
date_updated: 2026-07-13T09:14:58Z
file_id: '66448'
file_name: TAF_WP_104_HankeFengUhde2026.pdf
file_size: 1169286
relation: main_file
file_date_updated: 2026-07-13T09:14:58Z
has_accepted_license: '1'
jel:
- C4
- C5
- B23
- B26
- C32
keyword:
- Modulus Log-GARCH
- Modified (FI)EGARCH
- Modulus asymmetric (FI)Log-GARCH
- (FI)EGARCH
- long memory
- modulus-log transformation
- QMLE
- model selection
- implementation in R
language:
- iso: eng
oa: '1'
status: public
title: Comparing the behaviors of some original short and long memory exponential
volatility models
type: working_paper
user_id: '63677'
year: '2026'
...
---
_id: '60118'
author:
- first_name: Marlon
full_name: Fritz, Marlon
last_name: Fritz
- first_name: Sarah
full_name: Forstinger, Sarah
last_name: Forstinger
- first_name: Yuanhua
full_name: Feng, Yuanhua
id: '20760'
last_name: Feng
- first_name: Thomas
full_name: Gries, Thomas
id: '186'
last_name: Gries
citation:
ama: Fritz M, Forstinger S, Feng Y, Gries T. Forecasting economic growth by combining
local linear and standard approaches. Journal of Applied Statistics. 2024;52(7):1342-1360.
doi:10.1080/02664763.2024.2424920
apa: Fritz, M., Forstinger, S., Feng, Y., & Gries, T. (2024). Forecasting economic
growth by combining local linear and standard approaches. Journal of Applied
Statistics, 52(7), 1342–1360. https://doi.org/10.1080/02664763.2024.2424920
bibtex: '@article{Fritz_Forstinger_Feng_Gries_2024, title={Forecasting economic
growth by combining local linear and standard approaches}, volume={52}, DOI={10.1080/02664763.2024.2424920},
number={7}, journal={Journal of Applied Statistics}, publisher={Informa UK Limited},
author={Fritz, Marlon and Forstinger, Sarah and Feng, Yuanhua and Gries, Thomas},
year={2024}, pages={1342–1360} }'
chicago: 'Fritz, Marlon, Sarah Forstinger, Yuanhua Feng, and Thomas Gries. “Forecasting
Economic Growth by Combining Local Linear and Standard Approaches.” Journal
of Applied Statistics 52, no. 7 (2024): 1342–60. https://doi.org/10.1080/02664763.2024.2424920.'
ieee: 'M. Fritz, S. Forstinger, Y. Feng, and T. Gries, “Forecasting economic growth
by combining local linear and standard approaches,” Journal of Applied Statistics,
vol. 52, no. 7, pp. 1342–1360, 2024, doi: 10.1080/02664763.2024.2424920.'
mla: Fritz, Marlon, et al. “Forecasting Economic Growth by Combining Local Linear
and Standard Approaches.” Journal of Applied Statistics, vol. 52, no. 7,
Informa UK Limited, 2024, pp. 1342–60, doi:10.1080/02664763.2024.2424920.
short: M. Fritz, S. Forstinger, Y. Feng, T. Gries, Journal of Applied Statistics
52 (2024) 1342–1360.
date_created: 2025-06-03T10:33:51Z
date_updated: 2025-11-09T09:49:02Z
department:
- _id: '35'
- _id: '200'
- _id: '202'
- _id: '475'
doi: 10.1080/02664763.2024.2424920
intvolume: ' 52'
issue: '7'
language:
- iso: eng
page: 1342-1360
publication: Journal of Applied Statistics
publication_identifier:
issn:
- 0266-4763
- 1360-0532
publication_status: published
publisher: Informa UK Limited
status: public
title: Forecasting economic growth by combining local linear and standard approaches
type: journal_article
user_id: '186'
volume: 52
year: '2024'
...
---
_id: '35992'
abstract:
- lang: eng
text: 'In this paper new semiparametric generalized autoregressive conditional heteroscedasticity
(GARCH) models with long memory are introduced. A multiplicative decomposition
of the volatility into a conditional component and an unconditional component
is assumed. The estimation of the latter is carried out by means of a data-driven
local polynomial smoother. According to the revised recommendations by the Basel
Committee on Banking Supervision to measure market risk in the banks’ trading
books, these new semiparametric GARCH models are applied to obtain rolling one-step
ahead forecasts for the value-at-risk and expected shortfall (ES) for market risk
assets. Standard regulatory traffic-light tests and a newly introduced traffic-light
test for the ES are carried out for all models. In addition, model performance
is assessed via a recently introduced model selection criterion. The practical
relevance of our proposal is demonstrated by a comparative study. Our results
indicate that semiparametric long-memory GARCH models are a meaningful substitute
for their conventional, parametric counterparts. '
article_type: original
author:
- first_name: Sebastian
full_name: Letmathe, Sebastian
id: '23991'
last_name: Letmathe
- first_name: Yuanhua
full_name: Feng, Yuanhua
id: '20760'
last_name: Feng
- first_name: André
full_name: Uhde, André
id: '36049'
last_name: Uhde
citation:
ama: Letmathe S, Feng Y, Uhde A. Semiparametric GARCH models with long memory applied
to Value at Risk and Expected Shortfall. Journal of Risk. 25(2).
apa: Letmathe, S., Feng, Y., & Uhde, A. (n.d.). Semiparametric GARCH models
with long memory applied to Value at Risk and Expected Shortfall. Journal of
Risk, 25(2).
bibtex: '@article{Letmathe_Feng_Uhde, title={Semiparametric GARCH models with long
memory applied to Value at Risk and Expected Shortfall}, volume={25}, number={2},
journal={Journal of Risk}, author={Letmathe, Sebastian and Feng, Yuanhua and Uhde,
André} }'
chicago: Letmathe, Sebastian, Yuanhua Feng, and André Uhde. “Semiparametric GARCH
Models with Long Memory Applied to Value at Risk and Expected Shortfall.” Journal
of Risk 25, no. 2 (n.d.).
ieee: S. Letmathe, Y. Feng, and A. Uhde, “Semiparametric GARCH models with long
memory applied to Value at Risk and Expected Shortfall,” Journal of Risk,
vol. 25, no. 2.
mla: Letmathe, Sebastian, et al. “Semiparametric GARCH Models with Long Memory Applied
to Value at Risk and Expected Shortfall.” Journal of Risk, vol. 25, no.
2.
short: S. Letmathe, Y. Feng, A. Uhde, Journal of Risk 25 (n.d.).
date_created: 2023-01-11T10:50:27Z
date_updated: 2023-11-17T10:26:36Z
department:
- _id: '186'
- _id: '188'
intvolume: ' 25'
issue: '2'
keyword:
- long memory
- generalized autoregressive conditional heteroscedasticity (GARCH) models
- value-at-risk (VaR)
- expected shortfall (ES)
- traffic-light test
- backtesting
language:
- iso: eng
publication: Journal of Risk
publication_status: inpress
status: public
title: Semiparametric GARCH models with long memory applied to Value at Risk and Expected
Shortfall
type: journal_article
user_id: '36049'
volume: 25
year: '2022'
...
---
_id: '29317'
abstract:
- lang: eng
text: In this paper new semiparametric GARCH models with long memory are in- troduced.
The estimation of the nonparametric scale function is carried out by an adapted
version of the SEMIFAR algorithm (Beran et al., 2002). Recurring on the revised
recommendations by the Basel Committee to measure market risk in the banks' trading
books (Basel Committee on Banking Supervision, 2013), the semi- parametric GARCH
models are applied to obtain rolling one-step ahead forecasts for the Value at
Risk (VaR) and Expected Shortfall (ES) for market risk assets. In addition, standard
regulatory traffic light tests (Basel Committee on Banking Supervision, 1996)
and a newly introduced traffic light test for the ES are carried out for all models.
The practical relevance of our proposal is demonstrated by a comparative study.
Our results indicate that semiparametric long memory GARCH models are an attractive
alternative to their conventional, parametric counterparts.
author:
- first_name: Sebastian
full_name: Letmathe, Sebastian
id: '23991'
last_name: Letmathe
- first_name: Yuanhua
full_name: Feng, Yuanhua
id: '20760'
last_name: Feng
- first_name: André
full_name: Uhde, André
id: '36049'
last_name: Uhde
orcid: https://orcid.org/0000-0002-8058-8857
citation:
ama: Letmathe S, Feng Y, Uhde A. Semiparametric GARCH models with long memory applied
to Value at Risk and Expected Shortfall. Journal of Risk. doi:10.21314/JOR.2022.044
apa: Letmathe, S., Feng, Y., & Uhde, A. (n.d.). Semiparametric GARCH models
with long memory applied to Value at Risk and Expected Shortfall. Journal of
Risk. https://doi.org/10.21314/JOR.2022.044
bibtex: '@article{Letmathe_Feng_Uhde, title={Semiparametric GARCH models with long
memory applied to Value at Risk and Expected Shortfall}, DOI={10.21314/JOR.2022.044},
journal={Journal of Risk}, author={Letmathe, Sebastian and Feng, Yuanhua and Uhde,
André} }'
chicago: Letmathe, Sebastian, Yuanhua Feng, and André Uhde. “Semiparametric GARCH
Models with Long Memory Applied to Value at Risk and Expected Shortfall.” Journal
of Risk, n.d. https://doi.org/10.21314/JOR.2022.044.
ieee: 'S. Letmathe, Y. Feng, and A. Uhde, “Semiparametric GARCH models with long
memory applied to Value at Risk and Expected Shortfall,” Journal of Risk,
doi: 10.21314/JOR.2022.044.'
mla: Letmathe, Sebastian, et al. “Semiparametric GARCH Models with Long Memory Applied
to Value at Risk and Expected Shortfall.” Journal of Risk, doi:10.21314/JOR.2022.044.
short: S. Letmathe, Y. Feng, A. Uhde, Journal of Risk (n.d.).
date_created: 2022-01-13T11:23:02Z
date_updated: 2024-04-17T13:34:54Z
department:
- _id: '186'
- _id: '19'
doi: 10.21314/JOR.2022.044
jel:
- C14
- C51
- C52
- G17
- G32
keyword:
- Semiparametric
- long memory
- GARCH models
- forecasting
- Value at Risk
- Expected Shortfall
- traffic light test
- Basel Committee on Banking Supervision
language:
- iso: eng
publication: Journal of Risk
publication_status: inpress
status: public
title: Semiparametric GARCH models with long memory applied to Value at Risk and Expected
Shortfall
type: journal_article
user_id: '36049'
year: '2022'
...
---
_id: '50025'
author:
- first_name: Yuanhua
full_name: Feng, Yuanhua
id: '20760'
last_name: Feng
- first_name: Thomas
full_name: Gries, Thomas
id: '186'
last_name: Gries
- first_name: Sebastian
full_name: Letmathe, Sebastian
last_name: Letmathe
- first_name: Dominik
full_name: Schulz, Dominik
last_name: Schulz
citation:
ama: Feng Y, Gries T, Letmathe S, Schulz D. The smoots Package in R for Semiparametric
Modeling of Trend Stationary Time Series. The R Journal. 2022;14(1):182-195.
doi:10.32614/rj-2022-017
apa: Feng, Y., Gries, T., Letmathe, S., & Schulz, D. (2022). The smoots Package
in R for Semiparametric Modeling of Trend Stationary Time Series. The R Journal,
14(1), 182–195. https://doi.org/10.32614/rj-2022-017
bibtex: '@article{Feng_Gries_Letmathe_Schulz_2022, title={The smoots Package in
R for Semiparametric Modeling of Trend Stationary Time Series}, volume={14}, DOI={10.32614/rj-2022-017}, number={1},
journal={The R Journal}, publisher={The R Foundation}, author={Feng, Yuanhua and
Gries, Thomas and Letmathe, Sebastian and Schulz, Dominik}, year={2022}, pages={182–195}
}'
chicago: 'Feng, Yuanhua, Thomas Gries, Sebastian Letmathe, and Dominik Schulz. “The
Smoots Package in R for Semiparametric Modeling of Trend Stationary Time Series.”
The R Journal 14, no. 1 (2022): 182–95. https://doi.org/10.32614/rj-2022-017.'
ieee: 'Y. Feng, T. Gries, S. Letmathe, and D. Schulz, “The smoots Package in R for
Semiparametric Modeling of Trend Stationary Time Series,” The R Journal,
vol. 14, no. 1, pp. 182–195, 2022, doi: 10.32614/rj-2022-017.'
mla: Feng, Yuanhua, et al. “The Smoots Package in R for Semiparametric Modeling
of Trend Stationary Time Series.” The R Journal, vol. 14, no. 1, The R
Foundation, 2022, pp. 182–95, doi:10.32614/rj-2022-017.
short: Y. Feng, T. Gries, S. Letmathe, D. Schulz, The R Journal 14 (2022) 182–195.
date_created: 2023-12-21T12:09:53Z
date_updated: 2025-11-10T09:32:36Z
doi: 10.32614/rj-2022-017
intvolume: ' 14'
issue: '1'
keyword:
- Statistics
- Probability and Uncertainty
- Numerical Analysis
- Statistics and Probability
language:
- iso: eng
page: 182-195
publication: The R Journal
publication_identifier:
issn:
- 2073-4859
publication_status: published
publisher: The R Foundation
status: public
title: The smoots Package in R for Semiparametric Modeling of Trend Stationary Time
Series
type: journal_article
user_id: '186'
volume: 14
year: '2022'
...
---
_id: '16873'
author:
- first_name: Christian
full_name: Peitz, Christian
id: '2980'
last_name: Peitz
- first_name: Yuanhua
full_name: Feng, Yuanhua
id: '20760'
last_name: Feng
- first_name: Bernard Michael
full_name: Gilroy, Bernard Michael
id: '175'
last_name: Gilroy
- first_name: Nico
full_name: Stöckmann, Nico
id: '65503'
last_name: Stöckmann
citation:
ama: 'Peitz C, Feng Y, Gilroy BM, Stöckmann N. The Shanghai-Hong Kong Stock Connect:
An Application of the Semi-CGARCH and Semi-EGARCH. Asian Economic and Financial
Review. 2020;10(4):427-438.'
apa: 'Peitz, C., Feng, Y., Gilroy, B. M., & Stöckmann, N. (2020). The Shanghai-Hong
Kong Stock Connect: An Application of the Semi-CGARCH and Semi-EGARCH. Asian
Economic and Financial Review, 10(4), 427–438.'
bibtex: '@article{Peitz_Feng_Gilroy_Stöckmann_2020, title={The Shanghai-Hong Kong
Stock Connect: An Application of the Semi-CGARCH and Semi-EGARCH}, volume={10},
number={4}, journal={Asian Economic and Financial Review}, publisher={Asian Economic
and Social Society}, author={Peitz, Christian and Feng, Yuanhua and Gilroy, Bernard
Michael and Stöckmann, Nico}, year={2020}, pages={427–438} }'
chicago: 'Peitz, Christian, Yuanhua Feng, Bernard Michael Gilroy, and Nico Stöckmann.
“The Shanghai-Hong Kong Stock Connect: An Application of the Semi-CGARCH and Semi-EGARCH.”
Asian Economic and Financial Review 10, no. 4 (2020): 427–38.'
ieee: 'C. Peitz, Y. Feng, B. M. Gilroy, and N. Stöckmann, “The Shanghai-Hong Kong
Stock Connect: An Application of the Semi-CGARCH and Semi-EGARCH,” Asian Economic
and Financial Review, vol. 10, no. 4, pp. 427–438, 2020.'
mla: 'Peitz, Christian, et al. “The Shanghai-Hong Kong Stock Connect: An Application
of the Semi-CGARCH and Semi-EGARCH.” Asian Economic and Financial Review,
vol. 10, no. 4, Asian Economic and Social Society, 2020, pp. 427–38.'
short: C. Peitz, Y. Feng, B.M. Gilroy, N. Stöckmann, Asian Economic and Financial
Review 10 (2020) 427–438.
date_created: 2020-04-27T11:33:44Z
date_updated: 2022-01-06T06:52:58Z
department:
- _id: '203'
intvolume: ' 10'
issue: '4'
language:
- iso: eng
page: 427-438
publication: Asian Economic and Financial Review
publisher: Asian Economic and Social Society
status: public
title: 'The Shanghai-Hong Kong Stock Connect: An Application of the Semi-CGARCH and
Semi-EGARCH'
type: journal_article
user_id: '26589'
volume: 10
year: '2020'
...
---
_id: '4665'
author:
- first_name: Bastian
full_name: Schäfer, Bastian
id: '70618'
last_name: Schäfer
- first_name: Yuanhua
full_name: Feng, Yuanhua
id: '20760'
last_name: Feng
citation:
ama: 'Schäfer B, Feng Y. Further Development of the Double Conditional Smoothing
for Nonparametric Surfaces Under a Lattice Spatial Model. In: Book of Abstracts.
; 2018:7.'
apa: Schäfer, B., & Feng, Y. (2018). Further Development of the Double Conditional
Smoothing for Nonparametric Surfaces Under a Lattice Spatial Model. In Book
of Abstracts (p. 7). Paderborn, Germany.
bibtex: '@inproceedings{Schäfer_Feng_2018, title={Further Development of the Double
Conditional Smoothing for Nonparametric Surfaces Under a Lattice Spatial Model},
booktitle={Book of Abstracts}, author={Schäfer, Bastian and Feng, Yuanhua}, year={2018},
pages={7} }'
chicago: Schäfer, Bastian, and Yuanhua Feng. “Further Development of the Double
Conditional Smoothing for Nonparametric Surfaces Under a Lattice Spatial Model.”
In Book of Abstracts, 7, 2018.
ieee: B. Schäfer and Y. Feng, “Further Development of the Double Conditional Smoothing
for Nonparametric Surfaces Under a Lattice Spatial Model,” in Book of Abstracts,
Paderborn, Germany, 2018, p. 7.
mla: Schäfer, Bastian, and Yuanhua Feng. “Further Development of the Double Conditional
Smoothing for Nonparametric Surfaces Under a Lattice Spatial Model.” Book of
Abstracts, 2018, p. 7.
short: 'B. Schäfer, Y. Feng, in: Book of Abstracts, 2018, p. 7.'
conference:
end_date: 6.7.2018
location: Paderborn, Germany
name: European Conference on Data Analysis
start_date: 4.7.2018
date_created: 2018-10-11T12:24:19Z
date_updated: 2022-01-06T07:01:17Z
department:
- _id: '206'
language:
- iso: eng
page: '7'
publication: Book of Abstracts
status: public
title: Further Development of the Double Conditional Smoothing for Nonparametric Surfaces
Under a Lattice Spatial Model
type: conference
user_id: '1112'
year: '2018'
...
---
_id: '4667'
author:
- first_name: Yuanhua
full_name: Feng, Yuanhua
id: '20760'
last_name: Feng
- first_name: Sebastian
full_name: Letmathe, Sebastian
id: '23991'
last_name: Letmathe
citation:
ama: Feng Y, Letmathe S. The Non-Gaussian ESEMIFAR Model. 2018:7.
apa: Feng, Y., & Letmathe, S. (2018). The Non-Gaussian ESEMIFAR Model. Presented
at the European Conference on Data Analysis, Paderborn, Germany.
bibtex: '@article{Feng_Letmathe_2018, series={Book of Abstracts}, title={The Non-Gaussian
ESEMIFAR Model}, author={Feng, Yuanhua and Letmathe, Sebastian}, year={2018},
pages={7}, collection={Book of Abstracts} }'
chicago: Feng, Yuanhua, and Sebastian Letmathe. “The Non-Gaussian ESEMIFAR Model.”
Book of Abstracts, 2018.
ieee: Y. Feng and S. Letmathe, “The Non-Gaussian ESEMIFAR Model.” p. 7, 2018.
mla: Feng, Yuanhua, and Sebastian Letmathe. The Non-Gaussian ESEMIFAR Model.
2018, p. 7.
short: Y. Feng, S. Letmathe, (2018) 7.
conference:
end_date: 6.7.2018
location: Paderborn, Germany
name: European Conference on Data Analysis
start_date: 4.7.2018
date_created: 2018-10-11T12:26:05Z
date_updated: 2022-01-06T07:01:17Z
department:
- _id: '206'
language:
- iso: eng
page: '7'
series_title: Book of Abstracts
status: public
title: The Non-Gaussian ESEMIFAR Model
type: conference
user_id: '10075'
year: '2018'
...
---
_id: '4668'
author:
- first_name: Sarah
full_name: Forstinger, Sarah
id: '10075'
last_name: Forstinger
- first_name: Yuanhua
full_name: Feng, Yuanhua
id: '20760'
last_name: Feng
- first_name: Christian
full_name: Peitz, Christian
id: '2980'
last_name: Peitz
citation:
ama: 'Forstinger S, Feng Y, Peitz C. Forecasting Non-Negative Financial Processes
Using Different Parametric and Semi-Parametric ACD-Type Models. In: Book of
Abstracts. ; 2018:17.'
apa: Forstinger, S., Feng, Y., & Peitz, C. (2018). Forecasting Non-Negative
Financial Processes Using Different Parametric and Semi-Parametric ACD-Type Models.
In Book of Abstracts (p. 17). Paderborn, Germany.
bibtex: '@inproceedings{Forstinger_Feng_Peitz_2018, title={Forecasting Non-Negative
Financial Processes Using Different Parametric and Semi-Parametric ACD-Type Models},
booktitle={Book of Abstracts}, author={Forstinger, Sarah and Feng, Yuanhua and
Peitz, Christian}, year={2018}, pages={17} }'
chicago: Forstinger, Sarah, Yuanhua Feng, and Christian Peitz. “Forecasting Non-Negative
Financial Processes Using Different Parametric and Semi-Parametric ACD-Type Models.”
In Book of Abstracts, 17, 2018.
ieee: S. Forstinger, Y. Feng, and C. Peitz, “Forecasting Non-Negative Financial
Processes Using Different Parametric and Semi-Parametric ACD-Type Models,” in
Book of Abstracts, Paderborn, Germany, 2018, p. 17.
mla: Forstinger, Sarah, et al. “Forecasting Non-Negative Financial Processes Using
Different Parametric and Semi-Parametric ACD-Type Models.” Book of Abstracts,
2018, p. 17.
short: 'S. Forstinger, Y. Feng, C. Peitz, in: Book of Abstracts, 2018, p. 17.'
conference:
end_date: 6.7.2018
location: Paderborn, Germany
name: European Conference on Data Analysis
start_date: 4.7.2018
date_created: 2018-10-11T12:27:34Z
date_updated: 2022-01-06T07:01:17Z
department:
- _id: '206'
language:
- iso: eng
page: '17'
publication: Book of Abstracts
status: public
title: Forecasting Non-Negative Financial Processes Using Different Parametric and
Semi-Parametric ACD-Type Models
type: conference
user_id: '10075'
year: '2018'
...
---
_id: '4669'
author:
- first_name: 'Xuehai '
full_name: 'Zhang, Xuehai '
last_name: Zhang
- first_name: Yuanhua
full_name: Feng, Yuanhua
id: '20760'
last_name: Feng
citation:
ama: 'Zhang X, Feng Y. A Box-Cox Semiparametric Multiplicative Error Model. In:
Book of Abstracts. ; 2018:19.'
apa: Zhang, X., & Feng, Y. (2018). A Box-Cox Semiparametric Multiplicative Error
Model. In Book of Abstracts (p. 19). Paderborn, Germany.
bibtex: '@inproceedings{Zhang_Feng_2018, title={A Box-Cox Semiparametric Multiplicative
Error Model}, booktitle={Book of Abstracts}, author={Zhang, Xuehai and Feng,
Yuanhua}, year={2018}, pages={19} }'
chicago: Zhang, Xuehai , and Yuanhua Feng. “A Box-Cox Semiparametric Multiplicative
Error Model.” In Book of Abstracts, 19, 2018.
ieee: X. Zhang and Y. Feng, “A Box-Cox Semiparametric Multiplicative Error Model,”
in Book of Abstracts, Paderborn, Germany, 2018, p. 19.
mla: Zhang, Xuehai, and Yuanhua Feng. “A Box-Cox Semiparametric Multiplicative Error
Model.” Book of Abstracts, 2018, p. 19.
short: 'X. Zhang, Y. Feng, in: Book of Abstracts, 2018, p. 19.'
conference:
end_date: 6.7.2018
location: Paderborn, Germany
name: European Conference on Data Analysis
start_date: 4.7.2018
date_created: 2018-10-11T12:28:28Z
date_updated: 2022-01-06T07:01:17Z
department:
- _id: '206'
language:
- iso: eng
page: '19'
publication: Book of Abstracts
status: public
title: A Box-Cox Semiparametric Multiplicative Error Model
type: conference
user_id: '10075'
year: '2018'
...
---
_id: '4633'
author:
- first_name: Xuehai
full_name: Zhang, Xuehai
last_name: Zhang
- first_name: Yuanhua
full_name: Feng, Yuanhua
id: '20760'
last_name: Feng
- first_name: Christian
full_name: Peitz, Christian
id: '2980'
last_name: Peitz
citation:
ama: Zhang X, Feng Y, Peitz C. A General Class of SemiGARCH Models Based on the
Box-Cox Transformation.; 2017.
apa: Zhang, X., Feng, Y., & Peitz, C. (2017). A general class of SemiGARCH
models based on the Box-Cox transformation.
bibtex: '@book{Zhang_Feng_Peitz_2017, title={A general class of SemiGARCH models
based on the Box-Cox transformation}, author={Zhang, Xuehai and Feng, Yuanhua
and Peitz, Christian}, year={2017} }'
chicago: Zhang, Xuehai, Yuanhua Feng, and Christian Peitz. A General Class of
SemiGARCH Models Based on the Box-Cox Transformation, 2017.
ieee: X. Zhang, Y. Feng, and C. Peitz, A general class of SemiGARCH models based
on the Box-Cox transformation. 2017.
mla: Zhang, Xuehai, et al. A General Class of SemiGARCH Models Based on the Box-Cox
Transformation. 2017.
short: X. Zhang, Y. Feng, C. Peitz, A General Class of SemiGARCH Models Based on
the Box-Cox Transformation, 2017.
date_created: 2018-10-11T06:43:53Z
date_updated: 2022-01-06T07:01:16Z
department:
- _id: '206'
- _id: '475'
language:
- iso: eng
status: public
title: A general class of SemiGARCH models based on the Box-Cox transformation
type: working_paper
user_id: '10075'
year: '2017'
...
---
_id: '4671'
author:
- first_name: Yuanhua
full_name: Feng, Yuanhua
id: '20760'
last_name: Feng
- first_name: Thomas
full_name: Gries, Thomas
id: '186'
last_name: Gries
citation:
ama: Feng Y, Gries T. Data-Driven Local Polynomial for the Trend and Its Derivatives
in Economic Time Series.; 2017.
apa: Feng, Y., & Gries, T. (2017). Data-driven local polynomial for the trend
and its derivatives in economic time series.
bibtex: '@book{Feng_Gries_2017, title={Data-driven local polynomial for the trend
and its derivatives in economic time series}, author={Feng, Yuanhua and Gries,
Thomas}, year={2017} }'
chicago: Feng, Yuanhua, and Thomas Gries. Data-Driven Local Polynomial for the
Trend and Its Derivatives in Economic Time Series, 2017.
ieee: Y. Feng and T. Gries, Data-driven local polynomial for the trend and its
derivatives in economic time series. 2017.
mla: Feng, Yuanhua, and Thomas Gries. Data-Driven Local Polynomial for the Trend
and Its Derivatives in Economic Time Series. 2017.
short: Y. Feng, T. Gries, Data-Driven Local Polynomial for the Trend and Its Derivatives
in Economic Time Series, 2017.
date_created: 2018-10-11T12:43:07Z
date_updated: 2022-01-06T07:01:17Z
department:
- _id: '206'
- _id: '475'
language:
- iso: eng
status: public
title: Data-driven local polynomial for the trend and its derivatives in economic
time series
type: working_paper
user_id: '10075'
year: '2017'
...
---
_id: '4592'
author:
- first_name: Yuanhua
full_name: Feng, Yuanhua
id: '20760'
last_name: Feng
- first_name: Sarah
full_name: Forstinger, Sarah
id: '10075'
last_name: Forstinger
- first_name: Christian
full_name: Peitz, Christian
id: '2980'
last_name: Peitz
citation:
ama: Feng Y, Forstinger S, Peitz C. On the iterative plug-in algorithm for estimating
diurnal patterns of financial trade durations. Journal of Statistical Computation
and Simulation. 2015;86(12):2291-2307. doi:10.1080/00949655.2015.1107908
apa: Feng, Y., Forstinger, S., & Peitz, C. (2015). On the iterative plug-in
algorithm for estimating diurnal patterns of financial trade durations. Journal
of Statistical Computation and Simulation, 86(12), 2291–2307. https://doi.org/10.1080/00949655.2015.1107908
bibtex: '@article{Feng_Forstinger_Peitz_2015, title={On the iterative plug-in algorithm
for estimating diurnal patterns of financial trade durations}, volume={86}, DOI={10.1080/00949655.2015.1107908},
number={12}, journal={Journal of Statistical Computation and Simulation}, publisher={Informa
UK Limited}, author={Feng, Yuanhua and Forstinger, Sarah and Peitz, Christian},
year={2015}, pages={2291–2307} }'
chicago: 'Feng, Yuanhua, Sarah Forstinger, and Christian Peitz. “On the Iterative
Plug-in Algorithm for Estimating Diurnal Patterns of Financial Trade Durations.”
Journal of Statistical Computation and Simulation 86, no. 12 (2015): 2291–2307.
https://doi.org/10.1080/00949655.2015.1107908.'
ieee: Y. Feng, S. Forstinger, and C. Peitz, “On the iterative plug-in algorithm
for estimating diurnal patterns of financial trade durations,” Journal of Statistical
Computation and Simulation, vol. 86, no. 12, pp. 2291–2307, 2015.
mla: Feng, Yuanhua, et al. “On the Iterative Plug-in Algorithm for Estimating Diurnal
Patterns of Financial Trade Durations.” Journal of Statistical Computation
and Simulation, vol. 86, no. 12, Informa UK Limited, 2015, pp. 2291–307, doi:10.1080/00949655.2015.1107908.
short: Y. Feng, S. Forstinger, C. Peitz, Journal of Statistical Computation and
Simulation 86 (2015) 2291–2307.
date_created: 2018-10-10T09:29:40Z
date_updated: 2022-01-06T07:01:14Z
department:
- _id: '206'
doi: 10.1080/00949655.2015.1107908
intvolume: ' 86'
issue: '12'
language:
- iso: eng
page: 2291-2307
publication: Journal of Statistical Computation and Simulation
publication_identifier:
issn:
- 0094-9655
- 1563-5163
publication_status: published
publisher: Informa UK Limited
status: public
title: On the iterative plug-in algorithm for estimating diurnal patterns of financial
trade durations
type: journal_article
user_id: '10075'
volume: 86
year: '2015'
...
---
_id: '4593'
author:
- first_name: Yuanhua
full_name: Feng, Yuanhua
id: '20760'
last_name: Feng
- first_name: Chen
full_name: Zhou, Chen
last_name: Zhou
citation:
ama: Feng Y, Zhou C. Forecasting financial market activity using a semiparametric
fractionally integrated Log-ACD. International Journal of Forecasting.
2015;31(2):349-363. doi:10.1016/j.ijforecast.2014.09.001
apa: Feng, Y., & Zhou, C. (2015). Forecasting financial market activity using
a semiparametric fractionally integrated Log-ACD. International Journal of
Forecasting, 31(2), 349–363. https://doi.org/10.1016/j.ijforecast.2014.09.001
bibtex: '@article{Feng_Zhou_2015, title={Forecasting financial market activity using
a semiparametric fractionally integrated Log-ACD}, volume={31}, DOI={10.1016/j.ijforecast.2014.09.001},
number={2}, journal={International Journal of Forecasting}, publisher={Elsevier
BV}, author={Feng, Yuanhua and Zhou, Chen}, year={2015}, pages={349–363} }'
chicago: 'Feng, Yuanhua, and Chen Zhou. “Forecasting Financial Market Activity Using
a Semiparametric Fractionally Integrated Log-ACD.” International Journal of
Forecasting 31, no. 2 (2015): 349–63. https://doi.org/10.1016/j.ijforecast.2014.09.001.'
ieee: Y. Feng and C. Zhou, “Forecasting financial market activity using a semiparametric
fractionally integrated Log-ACD,” International Journal of Forecasting,
vol. 31, no. 2, pp. 349–363, 2015.
mla: Feng, Yuanhua, and Chen Zhou. “Forecasting Financial Market Activity Using
a Semiparametric Fractionally Integrated Log-ACD.” International Journal of
Forecasting, vol. 31, no. 2, Elsevier BV, 2015, pp. 349–63, doi:10.1016/j.ijforecast.2014.09.001.
short: Y. Feng, C. Zhou, International Journal of Forecasting 31 (2015) 349–363.
date_created: 2018-10-10T09:33:43Z
date_updated: 2022-01-06T07:01:14Z
department:
- _id: '206'
doi: 10.1016/j.ijforecast.2014.09.001
intvolume: ' 31'
issue: '2'
language:
- iso: eng
page: 349-363
publication: International Journal of Forecasting
publication_identifier:
issn:
- 0169-2070
publication_status: published
publisher: Elsevier BV
status: public
title: Forecasting financial market activity using a semiparametric fractionally integrated
Log-ACD
type: journal_article
user_id: '10075'
volume: 31
year: '2015'
...
---
_id: '4649'
alternative_title:
- Festschrift in honour of Prof. Siegfried Heiler
citation:
ama: 'Beran J, Feng Y, Hebbel H, eds. Empirical Economic and Financial Research
- Theory, Methods and Practice. Berlin: Springer; 2015.'
apa: 'Beran, J., Feng, Y., & Hebbel, H. (Eds.). (2015). Empirical Economic
and Financial Research - Theory, Methods and Practice. Berlin: Springer.'
bibtex: '@book{Beran_Feng_Hebbel_2015, place={Berlin}, title={Empirical Economic
and Financial Research - Theory, Methods and Practice}, publisher={Springer},
year={2015} }'
chicago: 'Beran, Jan, Yuanhua Feng, and Hartmut Hebbel, eds. Empirical Economic
and Financial Research - Theory, Methods and Practice. Berlin: Springer, 2015.'
ieee: 'J. Beran, Y. Feng, and H. Hebbel, Eds., Empirical Economic and Financial
Research - Theory, Methods and Practice. Berlin: Springer, 2015.'
mla: Beran, Jan, et al., editors. Empirical Economic and Financial Research -
Theory, Methods and Practice. Springer, 2015.
short: J. Beran, Y. Feng, H. Hebbel, eds., Empirical Economic and Financial Research
- Theory, Methods and Practice, Springer, Berlin, 2015.
date_created: 2018-10-11T08:57:17Z
date_updated: 2022-01-06T07:01:16Z
department:
- _id: '206'
editor:
- first_name: Jan
full_name: Beran, Jan
last_name: Beran
- first_name: Yuanhua
full_name: Feng, Yuanhua
id: '20760'
last_name: Feng
- first_name: Hartmut
full_name: Hebbel, Hartmut
last_name: Hebbel
language:
- iso: eng
place: Berlin
publication_status: published
publisher: Springer
status: public
title: Empirical Economic and Financial Research - Theory, Methods and Practice
type: book_editor
user_id: '10075'
year: '2015'
...
---
_id: '4650'
author:
- first_name: Jan
full_name: Beran, Jan
last_name: Beran
- first_name: Yuanhua
full_name: Feng, Yuanhua
id: '20760'
last_name: Feng
- first_name: Hartmut
full_name: Hebbel, Hartmut
last_name: Hebbel
citation:
ama: 'Beran J, Feng Y, Hebbel H. Introduction. In: Empirical Economic and Financial
Research. Cham: Springer International Publishing; 2015:1-6. doi:10.1007/978-3-319-03122-4_1'
apa: 'Beran, J., Feng, Y., & Hebbel, H. (2015). Introduction. In Empirical
Economic and Financial Research (pp. 1–6). Cham: Springer International Publishing.
https://doi.org/10.1007/978-3-319-03122-4_1'
bibtex: '@inbook{Beran_Feng_Hebbel_2015, place={Cham}, title={Introduction}, DOI={10.1007/978-3-319-03122-4_1},
booktitle={Empirical Economic and Financial Research}, publisher={Springer International
Publishing}, author={Beran, Jan and Feng, Yuanhua and Hebbel, Hartmut}, year={2015},
pages={1–6} }'
chicago: 'Beran, Jan, Yuanhua Feng, and Hartmut Hebbel. “Introduction.” In Empirical
Economic and Financial Research, 1–6. Cham: Springer International Publishing,
2015. https://doi.org/10.1007/978-3-319-03122-4_1.'
ieee: 'J. Beran, Y. Feng, and H. Hebbel, “Introduction,” in Empirical Economic
and Financial Research, Cham: Springer International Publishing, 2015, pp.
1–6.'
mla: Beran, Jan, et al. “Introduction.” Empirical Economic and Financial Research,
Springer International Publishing, 2015, pp. 1–6, doi:10.1007/978-3-319-03122-4_1.
short: 'J. Beran, Y. Feng, H. Hebbel, in: Empirical Economic and Financial Research,
Springer International Publishing, Cham, 2015, pp. 1–6.'
date_created: 2018-10-11T08:59:27Z
date_updated: 2022-01-06T07:01:16Z
department:
- _id: '206'
doi: 10.1007/978-3-319-03122-4_1
language:
- iso: eng
page: 1-6
place: Cham
publication: Empirical Economic and Financial Research
publication_identifier:
isbn:
- '9783319031217'
- '9783319031224'
issn:
- 1570-5811
- 2214-7977
publication_status: published
publisher: Springer International Publishing
status: public
title: Introduction
type: book_chapter
user_id: '10075'
year: '2015'
...
---
_id: '4656'
author:
- first_name: Yuanhua
full_name: Feng, Yuanhua
id: '20760'
last_name: Feng
- first_name: Chen
full_name: Zhou, Chen
last_name: Zhou
citation:
ama: Feng Y, Zhou C. An Iterative Plug-in Algorithm for Realized Kernels.;
2015.
apa: Feng, Y., & Zhou, C. (2015). An iterative plug-in algorithm for realized
kernels.
bibtex: '@book{Feng_Zhou_2015, title={An iterative plug-in algorithm for realized
kernels}, author={Feng, Yuanhua and Zhou, Chen}, year={2015} }'
chicago: Feng, Yuanhua, and Chen Zhou. An Iterative Plug-in Algorithm for Realized
Kernels, 2015.
ieee: Y. Feng and C. Zhou, An iterative plug-in algorithm for realized kernels.
2015.
mla: Feng, Yuanhua, and Chen Zhou. An Iterative Plug-in Algorithm for Realized
Kernels. 2015.
short: Y. Feng, C. Zhou, An Iterative Plug-in Algorithm for Realized Kernels, 2015.
date_created: 2018-10-11T11:16:09Z
date_updated: 2022-01-06T07:01:16Z
department:
- _id: '206'
- _id: '475'
status: public
title: An iterative plug-in algorithm for realized kernels
type: working_paper
user_id: '10075'
year: '2015'
...
---
_id: '4599'
author:
- first_name: Jan
full_name: Beran, Jan
last_name: Beran
- first_name: Yuanhua
full_name: Feng, Yuanhua
id: '20760'
last_name: Feng
- first_name: Sucharita
full_name: Ghosh, Sucharita
last_name: Ghosh
citation:
ama: 'Beran J, Feng Y, Ghosh S. Modelling long-range dependence and trends in duration
series: an approach based on EFARIMA and ESEMIFAR models. Statistical Papers.
2014;56(2):431-451. doi:10.1007/s00362-014-0590-x'
apa: 'Beran, J., Feng, Y., & Ghosh, S. (2014). Modelling long-range dependence
and trends in duration series: an approach based on EFARIMA and ESEMIFAR models.
Statistical Papers, 56(2), 431–451. https://doi.org/10.1007/s00362-014-0590-x'
bibtex: '@article{Beran_Feng_Ghosh_2014, title={Modelling long-range dependence
and trends in duration series: an approach based on EFARIMA and ESEMIFAR models},
volume={56}, DOI={10.1007/s00362-014-0590-x},
number={2}, journal={Statistical Papers}, publisher={Springer Nature}, author={Beran,
Jan and Feng, Yuanhua and Ghosh, Sucharita}, year={2014}, pages={431–451} }'
chicago: 'Beran, Jan, Yuanhua Feng, and Sucharita Ghosh. “Modelling Long-Range Dependence
and Trends in Duration Series: An Approach Based on EFARIMA and ESEMIFAR Models.”
Statistical Papers 56, no. 2 (2014): 431–51. https://doi.org/10.1007/s00362-014-0590-x.'
ieee: 'J. Beran, Y. Feng, and S. Ghosh, “Modelling long-range dependence and trends
in duration series: an approach based on EFARIMA and ESEMIFAR models,” Statistical
Papers, vol. 56, no. 2, pp. 431–451, 2014.'
mla: 'Beran, Jan, et al. “Modelling Long-Range Dependence and Trends in Duration
Series: An Approach Based on EFARIMA and ESEMIFAR Models.” Statistical Papers,
vol. 56, no. 2, Springer Nature, 2014, pp. 431–51, doi:10.1007/s00362-014-0590-x.'
short: J. Beran, Y. Feng, S. Ghosh, Statistical Papers 56 (2014) 431–451.
date_created: 2018-10-10T09:55:25Z
date_updated: 2022-01-06T07:01:14Z
department:
- _id: '206'
doi: 10.1007/s00362-014-0590-x
intvolume: ' 56'
issue: '2'
language:
- iso: eng
page: 431-451
publication: Statistical Papers
publication_identifier:
issn:
- 0932-5026
- 1613-9798
publication_status: published
publisher: Springer Nature
status: public
title: 'Modelling long-range dependence and trends in duration series: an approach
based on EFARIMA and ESEMIFAR models'
type: journal_article
user_id: '10075'
volume: 56
year: '2014'
...
---
_id: '4602'
author:
- first_name: Jan
full_name: Beran, Jan
last_name: Beran
- first_name: Yuanhua
full_name: Feng, Yuanhua
id: '20760'
last_name: Feng
- first_name: Sucharita
full_name: Ghosh, Sucharita
last_name: Ghosh
citation:
ama: 'Beran J, Feng Y, Ghosh S. On EFARIMA and ESEMIFAR Models. In: Empirical
Economic and Financial Research. Cham: Springer International Publishing;
2014:239-253. doi:10.1007/978-3-319-03122-4_15'
apa: 'Beran, J., Feng, Y., & Ghosh, S. (2014). On EFARIMA and ESEMIFAR Models.
In Empirical Economic and Financial Research (pp. 239–253). Cham: Springer
International Publishing. https://doi.org/10.1007/978-3-319-03122-4_15'
bibtex: '@inbook{Beran_Feng_Ghosh_2014, place={Cham}, title={On EFARIMA and ESEMIFAR
Models}, DOI={10.1007/978-3-319-03122-4_15},
booktitle={Empirical Economic and Financial Research}, publisher={Springer International
Publishing}, author={Beran, Jan and Feng, Yuanhua and Ghosh, Sucharita}, year={2014},
pages={239–253} }'
chicago: 'Beran, Jan, Yuanhua Feng, and Sucharita Ghosh. “On EFARIMA and ESEMIFAR
Models.” In Empirical Economic and Financial Research, 239–53. Cham: Springer
International Publishing, 2014. https://doi.org/10.1007/978-3-319-03122-4_15.'
ieee: 'J. Beran, Y. Feng, and S. Ghosh, “On EFARIMA and ESEMIFAR Models,” in Empirical
Economic and Financial Research, Cham: Springer International Publishing,
2014, pp. 239–253.'
mla: Beran, Jan, et al. “On EFARIMA and ESEMIFAR Models.” Empirical Economic
and Financial Research, Springer International Publishing, 2014, pp. 239–53,
doi:10.1007/978-3-319-03122-4_15.
short: 'J. Beran, Y. Feng, S. Ghosh, in: Empirical Economic and Financial Research,
Springer International Publishing, Cham, 2014, pp. 239–253.'
date_created: 2018-10-10T10:27:24Z
date_updated: 2022-01-06T07:01:14Z
department:
- _id: '206'
doi: 10.1007/978-3-319-03122-4_15
language:
- iso: eng
page: 239-253
place: Cham
publication: Empirical Economic and Financial Research
publication_identifier:
isbn:
- '9783319031217'
- '9783319031224'
issn:
- 1570-5811
- 2214-7977
publication_status: published
publisher: Springer International Publishing
status: public
title: On EFARIMA and ESEMIFAR Models
type: book_chapter
user_id: '10075'
year: '2014'
...