Liquidity-adjusted Intraday Value at Risk modeling and risk management: An application to data from Deutsche Börse

Dionne, Georges; Pacurar, Maria; Zhou, Xiaozhou

doi:10.1016/j.jbankfin.2015.06.005

Cited by 12 publications

(6 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Current work suggests that endogenous liquidity risk could also be addressed by extending the VaR risk measurement horizon (see, e.g. Emna & Chokri, 2014;Dionne, Pacurar & Zhou, 2014).…”

Section: Scaling Of Liquidity Horizonmentioning

confidence: 99%

Trading book risk metrics: A South African perspective

Visser

Vuuren²

2016

S. Afr. j. econ. manag. sci. (Online)

View full text Add to dashboard Cite

“…Current work suggests that endogenous liquidity risk could also be addressed by extending the VaR risk measurement horizon (see, e.g. Emna & Chokri, 2014;Dionne, Pacurar & Zhou, 2014).…”

Section: Scaling Of Liquidity Horizonmentioning

confidence: 99%

Trading book risk metrics: A South African perspective

Visser

Vuuren²

2016

S. Afr. j. econ. manag. sci. (Online)

View full text Add to dashboard Cite

“…Muela, Martín, and Sanz (2017) focus on modeling exogenous liquidity risk and show that extreme value theory (EVT) is superior to the standard approach on the basis of five selected equities for the period between 2000 and 2015. Further studies in the area of LVaR models can be found, among others, by Weiß and Supper (2013), Dionne et al (2015) and Gong et al (2017). 1 Also, Angelidis and Benos (2006) asses intra-day VaR and underline the impact of liquidity risk for empirical backtesting.…”

Section: Introductionmentioning

confidence: 99%

Assessing liquidity‐adjusted risk forecasts

Berger

Uffmann

2021

Journal of Forecasting

View full text Add to dashboard Cite

In this paper, we provide a thorough study on the relevance of liquidity-adjusted value-at-risk (LVaR) and expected shortfall (LES) forecasts. We measure additional liquidity of an asset via the difference between its respective bid and ask prices and we assess the non-normality of bid-ask spreads, especially in turbulent market times. The empirical assessment comprises German stocks in both calm and turmoil market times, and our results provide evidence that liquidity risk turns out to be crucial for the quality of regulatory risk assessment in turmoil market times. We find that a Cornish-Fisher approximation describes a sensible choice for LVaR forecasts whereas an extreme value approach results in adequate LES forecasts.

show abstract

“…These versions take into account the following aspects: (A1) the shape of the HR of TD data; (A2) the conditional dynamics established in terms of mean or median; (A3) the linear form of the conditional mean or median dynamics; and (A4) the time series properties. Some recent applications of ACD models are discussed in Diana (2015) and Dionne et al (2015).…”

Section: Introductionmentioning

confidence: 99%

Birnbaum–Saunders autoregressive conditional duration models applied to high-frequency financial data

et al. 2017

View full text Add to dashboard Cite

Modern financial markets now record the precise time of each stock trade, along with price and volume, with the aim of analysing the structure of the times between trading events -leading to a big data problem. In this paper, we propose and compare two Birnbaum-Saunders autoregressive conditional duration models specified in terms of time-varying conditional median and mean durations. These models provide a novel alternative to the existing autoregressive conditional duration models due to their flexibility and ease of estimation. Influence diagnostic tools are developed to allow goodness-of-fit assessment and to detect departures from assumptions, including the presence of outliers and influential cases. Both global and local influence tools are considered based on the parameter estimates under different perturbation schemes. A thorough Monte Carlo study is presented to evaluate the performance of the maximum likelihood estimators, and the forecasting ability of the models is assessed using the traditional and density forecast evaluation techniques. The Monte Carlo study suggests that the parameter estimators are asymptotically unbiased, consistent and normally distributed. Finally, a full analysis of a real-world financial transaction data set, from the German DAX in 2016, is presented to illustrate the proposed approach and to compare the fitting and forecasting performances with existing models in the literature. One case related to the duration time is identified as potentially influential, but its removal does not change resulting inferences demonstrating the robustness of the proposed approach. Fitting and forecasting performances favor the proposed models and, in particular, the median-based approach gives additional protection against outliers, as expected.

show abstract

Liquidity-adjusted Intraday Value at Risk modeling and risk management: An application to data from Deutsche Börse

Cited by 12 publications

References 47 publications

Trading book risk metrics: A South African perspective

Trading book risk metrics: A South African perspective

Assessing liquidity‐adjusted risk forecasts

Birnbaum–Saunders autoregressive conditional duration models applied to high-frequency financial data

Contact Info

Product

Resources

About