Impact of Expected Shortfall Approach on Capital Requirement Under Basel

Siu, Yam Wing

doi:10.1142/s0219091519500255

Rev. Pac. Basin Finan. Mark. Pol.

2019

DOI: 10.1142/s0219091519500255

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Impact of Expected Shortfall Approach on Capital Requirement Under Basel

Yam Wing Siu

Abstract: This paper proposes a method that uses volatility index of US and six other markets of Pacific Basin, namely Hong Kong, Australia, India, Japan, Korea, and China, to provide value-at-risk (VaR) and expected shortfall (ES) forecasts. Empirical constants that are used to multiply the levels of volatility indexes for estimating VaR and ES of various significance levels for 1–22 days ahead, one by one, for seven market indexes have been statistically determined using daily data spanning from 4.75 to 16 years. It i… Show more

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Cited by 2 publications

References 21 publications

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Risk exposures of European cooperative banks: a comparative analysis

Mare

Gramlich

2020

Rev Quant Finan Acc

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This study sheds light on risk exposures of cooperative banks in Austria, Germany and Italy. We investigate how major risk elements of banks in these countries have evolved over time, across countries and institutions. Cooperative banks’ exposure to risk is analyzed looking at aggregate risk categories. In detail, we address the questions of (a) how single risk categories can be assessed in a consistent way, (b) how the different risk categories behaved over time, (c) what factors drive the diverse risks and (d) if there are similarities of risk characteristics for specific clusters of cooperative banks. We find that credit, interest rate and residual risk have a high degree of commonality. Liquidity risk is somehow dissociated from the other categories. Nevertheless, the risk behavior appears to vary over time and for different countries. This feature is relevant for the prudent management of cooperative banks and for the assessment of systemic financial risk.

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Risk exposures of European cooperative banks: a comparative analysis

Mare

Gramlich

2020

Rev Quant Finan Acc

View full text Add to dashboard Cite

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Convolution Approach for Value at Risk Estimation

Siu

2022

Rev. Pac. Basin Finan. Mark. Pol.

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Formally, Value at risk (VaR) measures the worst expected loss over a given horizon under normal market conditions at a given confidence level. Very often, daily data are used to compute VaR and scale up to the required time horizon with the square root of time adjustment. This gives rise to an important question when we perform VaR estimation: whether the values of VaR (i.e., “loss”) should be interpreted as (1) exactly on [Formula: see text]th day and (2) within i days. This research attempted to answer the above question using actual data of SPX and HSI. It was found that, in determining the proportionality of the values, (i.e., slopes) of VaR versus the holding period, the slopes for within i days are generally greater than those for exactly on [Formula: see text]th day. This has great implications to risk managers as it would be inappropriate to simply scale up the one-day volatility by multiplying the square root of time (or the number of days) ahead to determine the risk over a longer horizon of [Formula: see text] days. The evolution of log return distribution over time using actual data has also been performed empirically. It provides a better understanding than a series of VaR values. However, the number of samples in actual data is limited. There may not be enough data to draw reliable observation after it has been evolved a few times. Numerical simulation can help solve the problem by generating, say, one million log returns. It has been used to provide many insights as to how the distribution evolves over time and reveals an interesting dynamic of minimum cumulative returns. Numerical simulation is time consuming for performing evolution of distribution. The convolution approach provides an efficient way to analyze the evolution of the whole data distribution that encompasses VaR and others. The convolution approach with modified/scaled input distribution has been developed and it matches the results of numerical simulation perfectly for independent data for both exactly on [Formula: see text]th day and within i days. As the autocorrelation of the single index is mostly close to zero, results show that the convolution approach is able to match empirical results to a large extent.

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Impact of Expected Shortfall Approach on Capital Requirement Under Basel

Cited by 2 publications

References 21 publications

Risk exposures of European cooperative banks: a comparative analysis

Risk exposures of European cooperative banks: a comparative analysis

Convolution Approach for Value at Risk Estimation

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