Aarhus University Seal

Dansk

You are here: » Leopoldo Catania » Research outputs » Density Forecasts and the Leverage Effect

About Aarhus University

Leopoldo Catania

Density Forecasts and the Leverage Effect: Evidence from Observation and Parameter–Driven Volatility Models

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Conference article › Research › peer-review

Standard

Density Forecasts and the Leverage Effect: Evidence from Observation and Parameter–Driven Volatility Models. / Catania, Leopoldo; Nonejad, Nima.
In: The European Journal of Finance, Vol. 26, No. 2-3, 11.02.2020, p. 100-118.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Conference article › Research › peer-review

Harvard

Catania, L & Nonejad, N 2020, 'Density Forecasts and the Leverage Effect: Evidence from Observation and Parameter–Driven Volatility Models', The European Journal of Finance, vol. 26, no. 2-3, pp. 100-118. https://doi.org/10.1080/1351847X.2019.1586744

APA

Catania, L., & Nonejad, N. (2020). Density Forecasts and the Leverage Effect: Evidence from Observation and Parameter–Driven Volatility Models. The European Journal of Finance, 26(2-3), 100-118. https://doi.org/10.1080/1351847X.2019.1586744

CBE

Catania L, Nonejad N. 2020. Density Forecasts and the Leverage Effect: Evidence from Observation and Parameter–Driven Volatility Models. The European Journal of Finance. 26(2-3):100-118. https://doi.org/10.1080/1351847X.2019.1586744

MLA

Catania, Leopoldo and Nima Nonejad. "Density Forecasts and the Leverage Effect: Evidence from Observation and Parameter–Driven Volatility Models". The European Journal of Finance. 2020, 26(2-3). 100-118. https://doi.org/10.1080/1351847X.2019.1586744

Vancouver

Catania L, Nonejad N. Density Forecasts and the Leverage Effect: Evidence from Observation and Parameter–Driven Volatility Models. The European Journal of Finance. 2020 Feb 11;26(2-3):100-118. doi: 10.1080/1351847X.2019.1586744

Author

Catania, Leopoldo ; Nonejad, Nima. / Density Forecasts and the Leverage Effect : Evidence from Observation and Parameter–Driven Volatility Models. In: The European Journal of Finance. 2020 ; Vol. 26, No. 2-3. pp. 100-118.

Bibtex

@inproceedings{c95ad384c1c34b31ba9dc98a896c7e98,
title = "Density Forecasts and the Leverage Effect: Evidence from Observation and Parameter–Driven Volatility Models",
abstract = "The leverage effect refers to the well-known relationship between returns and volatility for an equity. When returns fall, volatility increases. We evaluate the role of the leverage effect with regards to generating density forecasts of equity returns using well-known observation and parameter-driven conditional volatility models. These models differ in their assumptions regarding: The parametric specification, the evolution of the conditional volatility process and how the leverage effect is specified. The ability of a model to generate accurate density forecasts when the leverage effect is incorporated or not as well as a comparison between different model-types is analyzed using a large number of financial time series. For each model type, the specification with the leverage effect tends to generate more accurate density forecasts than its no-leverage counterpart. Among the specifications considered, the Beta-t-EGARCH model is the top performer, regardless of whether we attach the same weight to each region of the conditional distribution or emphasize the left tail.",
keywords = "Conditional volatility, density forecasts, leverage effect, wCRPS",
author = "Leopoldo Catania and Nima Nonejad",
year = "2020",
month = feb,
day = "11",
doi = "10.1080/1351847X.2019.1586744",
language = "English",
volume = "26",
pages = "100--118",
journal = "The European Journal of Finance",
issn = "1351-847X",
publisher = "Taylor & Francis Online",
number = "2-3",
note = "International Conference on Mathematical and Statistical Methods for Actuarial Sciences and Finance ; Conference date: 04-04-2018 Through 06-04-2018",

}

RIS

TY - GEN

T1 - Density Forecasts and the Leverage Effect

T2 - International Conference on Mathematical and Statistical Methods for Actuarial Sciences and Finance

AU - Catania, Leopoldo

AU - Nonejad, Nima

N1 - Conference code: 8

PY - 2020/2/11

Y1 - 2020/2/11

N2 - The leverage effect refers to the well-known relationship between returns and volatility for an equity. When returns fall, volatility increases. We evaluate the role of the leverage effect with regards to generating density forecasts of equity returns using well-known observation and parameter-driven conditional volatility models. These models differ in their assumptions regarding: The parametric specification, the evolution of the conditional volatility process and how the leverage effect is specified. The ability of a model to generate accurate density forecasts when the leverage effect is incorporated or not as well as a comparison between different model-types is analyzed using a large number of financial time series. For each model type, the specification with the leverage effect tends to generate more accurate density forecasts than its no-leverage counterpart. Among the specifications considered, the Beta-t-EGARCH model is the top performer, regardless of whether we attach the same weight to each region of the conditional distribution or emphasize the left tail.

AB - The leverage effect refers to the well-known relationship between returns and volatility for an equity. When returns fall, volatility increases. We evaluate the role of the leverage effect with regards to generating density forecasts of equity returns using well-known observation and parameter-driven conditional volatility models. These models differ in their assumptions regarding: The parametric specification, the evolution of the conditional volatility process and how the leverage effect is specified. The ability of a model to generate accurate density forecasts when the leverage effect is incorporated or not as well as a comparison between different model-types is analyzed using a large number of financial time series. For each model type, the specification with the leverage effect tends to generate more accurate density forecasts than its no-leverage counterpart. Among the specifications considered, the Beta-t-EGARCH model is the top performer, regardless of whether we attach the same weight to each region of the conditional distribution or emphasize the left tail.

KW - Conditional volatility

KW - density forecasts

KW - leverage effect

KW - wCRPS

U2 - 10.1080/1351847X.2019.1586744

DO - 10.1080/1351847X.2019.1586744

M3 - Conference article

VL - 26

SP - 100

EP - 118

JO - The European Journal of Finance

JF - The European Journal of Finance

SN - 1351-847X

IS - 2-3

Y2 - 4 April 2018 through 6 April 2018

ER -