Journal of Risk and Financial Management

421 KiB

Open AccessArticle

On Setting Day-Ahead Equity Trading Risk Limits: VaR Prediction at Market Close or Open?

by Ana-Maria Fuertes and Jose Olmo

J. Risk Financial Manag. 2016, 9(3), 10; https://doi.org/10.3390/jrfm9030010 - 09 Sep 2016

Cited by 3 | Viewed by 4344

This paper investigates the information content of the ex post overnight return for one-day-ahead equity Value-at-Risk (VaR) forecasting. To do so, we deploy a univariate VaR modeling approach that constructs the forecast at market open and, accordingly, exploits the available overnight close-to-open price [...] Read more.

This paper investigates the information content of the ex post overnight return for one-day-ahead equity Value-at-Risk (VaR) forecasting. To do so, we deploy a univariate VaR modeling approach that constructs the forecast at market open and, accordingly, exploits the available overnight close-to-open price variation. The benchmark is the bivariate VaR modeling approach proposed by Ahoniemi et al. that constructs the forecast at the market close instead and, accordingly, it models separately the daytime and overnight return processes and their covariance. For a small cap portfolio, the bivariate VaR approach affords superior predictive ability than the ex post overnight VaR approach whereas for a large cap portfolio the results are reversed. The contrast indicates that price discovery at the market open is less efficient for small capitalization, thinly traded stocks. Full article

(This article belongs to the Special Issue Advances in Modeling Value at Risk and Expected Shortfall)

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819 KiB

Open AccessArticle

The Nexus between Social Capital and Bank Risk Taking

by Wenjing Xie, Haoyuan Ding and Terence Tai-Leung Chong

J. Risk Financial Manag. 2016, 9(3), 9; https://doi.org/10.3390/jrfm9030009 - 29 Jul 2016

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Abstract

This study explores social capital and its relevance to bank risk taking across countries. Our empirical results show that the levels of bank risk taking are lower in countries with higher levels of social capital, and that the impact of social capital is [...] Read more.

This study explores social capital and its relevance to bank risk taking across countries. Our empirical results show that the levels of bank risk taking are lower in countries with higher levels of social capital, and that the impact of social capital is mainly reflected by the reduced value of the standard deviation of return on assets. Moreover, the impact of social capital is found to be weaker when the legal system lacks strength. Furthermore, the study considers the impacts of social capital of the banks’ largest shareholders in these countries and finds that high levels of social capital present in these countries exert a negative effect on bank risk taking, but the effect is not strongly significant. Full article

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2019 KiB

Open AccessArticle

The Determinants of Equity Risk and Their Forecasting Implications: A Quantile Regression Perspective

by Giovanni Bonaccolto and Massimiliano Caporin

J. Risk Financial Manag. 2016, 9(3), 8; https://doi.org/10.3390/jrfm9030008 - 07 Jul 2016

Cited by 3 | Viewed by 5632

Abstract

Several market and macro-level variables influence the evolution of equity risk in addition to the well-known volatility persistence. However, the impact of those covariates might change depending on the risk level, being different between low and high volatility states. By combining equity risk [...] Read more.

Several market and macro-level variables influence the evolution of equity risk in addition to the well-known volatility persistence. However, the impact of those covariates might change depending on the risk level, being different between low and high volatility states. By combining equity risk estimates, obtained from the Realized Range Volatility, corrected for microstructure noise and jumps, and quantile regression methods, we evaluate the forecasting implications of the equity risk determinants in different volatility states and, without distributional assumptions on the realized range innovations, we recover both the points and the conditional distribution forecasts. In addition, we analyse how the the relationships among the involved variables evolve over time, through a rolling window procedure. The results show evidence of the selected variables’ relevant impacts and, particularly during periods of market stress, highlight heterogeneous effects across quantiles. Full article

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318 KiB

Open AccessArticle

Probability of Default and Default Correlations

by Weiping Li

J. Risk Financial Manag. 2016, 9(3), 7; https://doi.org/10.3390/jrfm9030007 - 05 Jul 2016

Cited by 3 | Viewed by 7462

Abstract

We consider a system where the asset values of firms are correlated with the default thresholds. We first evaluate the probability of default of a single firm under the correlated assets assumptions. This extends Merton’s probability of default of a single firm under [...] Read more.

We consider a system where the asset values of firms are correlated with the default thresholds. We first evaluate the probability of default of a single firm under the correlated assets assumptions. This extends Merton’s probability of default of a single firm under the independent asset values assumption. At any time, the distance-to-default for a single firm is derived in the system, and this distance-to-default should provide a different measure for credit rating with the correlated asset values into consideration. Then we derive a closed formula for the joint default probability and a general closed formula for the default correlation via the correlated multivariate process of the first-passage-time default correlation model. Our structural model encodes the sensitivities of default correlations with respect to the underlying correlation among firms’ asset values. We propose the disparate credit risk management from our result in contrast to the commonly used risk measurement methods considering default correlations into consideration. Full article

(This article belongs to the Special Issue Credit Risk)

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J. Risk Financial Manag., Volume 9, Issue 3 (September 2016) – 4 articles

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