Evaluating Interest Rate Covariance Models Within a Value-at-Risk Framework

Ferreira, Miguel A.; López, José A.

doi:10.1093/jjfinec/nbi005

Cited by 32 publications

(3 citation statements)

References 64 publications

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“…where lrv ðdÞ t ¼ logðRV p;t Þ is the logarithm of the daily portfolio realized variance defined in Eq (7);…”

Section: Volatility Modelsmentioning

confidence: 99%

“…[ 5 ] suggest that VaR forecast based on the univariate GARCH model is at least as good as the forecasts from the multivariate counterparts within identical innovation distribution families. The complex multivariate models tend to overfit the data [ 7 ]. The relative performance of multivariate volatility in VaR forecasts depends more on the distributional assumptions than on the parametric specification of the volatility models [ 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

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High-frequency enhanced VaR: A robust univariate realized volatility model for diverse portfolios and market conditions

Kuang

2024

PLoS ONE

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In the field of financial risk management, the accuracy of portfolio Value-at-Risk (VaR) forecasts is of critical importance to both practitioners and academics. This study pioneers a comprehensive evaluation of a univariate model that leverages high-frequency intraday data to improve portfolio VaR forecasts, providing a novel contrast to both univariate and multivariate models based on daily data. Existing research has used such high-frequency-based univariate models for index portfolios, it has not adequately studied their robustness for portfolios with diverse risk profiles, particularly under changing market conditions, such as during crises. Our research fills this gap by proposing a refined univariate long-memory realized volatility model that incorporates realized variance and covariance metrics, eliminating the necessity for a parametric covariance matrix. This model captures the long-run dependencies inherent in the volatility process and provides a flexible alternative that can be paired with appropriate return innovation distributions for VaR estimation. Empirical analyses show that our methodology significantly outperforms traditional univariate and multivariate Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) models in terms of forecasting accuracy while maintaining computational simplicity and ease of implementation. In particular, the inclusion of high-frequency data in univariate volatility models not only improves forecasting accuracy but also streamlines the complexity of portfolio risk assessment. This research extends the discourse between academic research and financial practice, highlighting the transformative impact of high-frequency data on risk management strategies within the financial sector.

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“…where lrv ðdÞ t ¼ logðRV p;t Þ is the logarithm of the daily portfolio realized variance defined in Eq (7);…”

Section: Volatility Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

High-frequency enhanced VaR: A robust univariate realized volatility model for diverse portfolios and market conditions

Kuang

2024

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…where F ´1 α is the critical value from an inverse normal distribution corresponding to the α% level, while ŵi 1 d are the optimized portfolio weights from factor model i on day d (see equation ( 4)), and Σi d is the matrix of day-ahead out-of-sample covariance forecasts from factor model i. We focus on the 1-day VaR as this is a very common choice by academics and practitioners (see, for example, Engle, 2002;Ferreira and Lopez, 2005). 29 We then calculate for each model i, the number of VaR exceedances, often called Hit, as follows…”

Section: Economic Gainsmentioning

confidence: 99%