Value-at-risk and conditional Value-at-risk estimation: A comparative study of risk performance for selected Malaysian sectoral indices

Thim, Chan Kok; Nourani, Mohammad Reza; Choong, Yap Voon

doi:10.1109/icssbe.2012.6396561

Cited by 4 publications

(4 citation statements)

References 7 publications

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“…Li and Xia (2011) applied extreme value theory to an empirical analysis of VaR for the Chinese stock market, suggesting that extreme value theory can effectively assess tail risks in financial series under market volatility as it better captures the tail behaviour of distributions. Thim et al (2012) compared the risk performances of VaR and Conditional VaR (CVaR) for selected industry indices in Malaysia and found that the technology sector had the highest risk, whereas the consumer goods sector had the lowest. Jiang et al (2015) proposed a model aimed at minimising VaR to describe and quantify project delay risks and demonstrated the effectiveness of this approach in measuring delay risks through comparison with Monte Carlo simulations.…”

Section: Literature Reviewmentioning

confidence: 99%

Model for Technology Risk Assessment in Commercial Banks

Kang,

Cheung

2024

Risks

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As the complexity of banking technology systems increases, the prevention of technological risk becomes an endless battle. Currently, most banks rely on the experience and subjective judgement of experts and employees to allocate resources for technological risk management, which does not effectively reduce the frequency of technology-related incidents. Through an analysis of mainstream risk management models, this study proposes a technology-based risk assessment system based on machine learning. It first identifies risk factors in bank IT, preprocesses the sample data, and uses different regression prediction models to train the processed data to build an intelligent assessment model. The experimental results indicated that the Genetic Algorithm–Backpropagation Neural Network model achieved the best performance. Based on assessment indicators, indicator weight values, and risk levels, commercial banks can develop targeted prevention and control measures by applying limited resources to the most critical corrective actions, thereby effectively reducing the frequency of technology-related incidents.

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Section: Literature Reviewmentioning

confidence: 99%

Model for Technology Risk Assessment in Commercial Banks

Kang,

Cheung

2024

Risks

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“…Even though, the histogram and the statistics provide comparative information about the behavior of the model and the loss rate prediction, Value at Risk (VaR) analysis could provide more deep analysis based on some confidence [33]. The Monte Carlo simulation model considered as one of the three common types of VaR.…”

Section: Value At Risk (Var) Analysismentioning

confidence: 99%

“…In this research and for better generalizability, the chosen confidence level was 95%, since outlier results would appear with a more significant percentage, especially for Hedera and ECMP. Note that we calculated the probability of the confidence level by considering the quantile function, as in equation 11 [33].…”

Section: Value At Risk (Var) Analysismentioning

confidence: 99%

Methods for Predicting Behavior of Elephant Flows in Data Center Networks

Alawadi¹,

Zaher²,

Molnár³

2019

Infocommunications journal

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Several Traffic Engineering (TE) techniques based on SDN (Software-defined networking) proposed to resolve flow competitions for network resources. However, there is no comprehensive study on the probability distribution of their throughput. Moreover, there is no study on predicting the future of elephant flows. To address these issues, we propose a new stochastic performance evaluation model to estimate the loss rate of two state-of-art flow scheduling algorithms including Equalcost multi-path routing (ECMP), Hedera besides a flow congestion control algorithm which is Data Center TCP (DCTCP). Although these algorithms have theoretical and practical benefits, their effectiveness has not been statistically investigated and analyzed in conserving the elephant flows. Therefore, we conducted extensive experiments on the fat-tree data center network to examine the efficiency of the algorithms under different network circumstances based on Monte Carlo risk analysis. The results show that Hedera is still risky to be used to handle the elephant flows due to its unstable throughput achieved under stochastic network congestion. On the other hand, DCTCP found suffering under high load scenarios. These outcomes might apply to all data center applications, in particular, the applications that demand high stability and productivity. Index Terms-Elephant flow, SDN, Risk analysis, Value-at-Risk, Flow scheduling, Congestion control. I. INTRODUCTION Nowadays, many enterprises leverage data center fabrics to manage highly-demanded bandwidth applications. Applications like Hadoop [1] and MapReduce [2] rely on hundreds or thousands of servers to provide high availability and scalability; therefore large data is transferred through the data center network to achieve these requirements. However, other types of data center applications such as regular web services are hosted inside the data center as well, due to the guaranteed availability and reliability. Because of these substantial requirements, many data center topologies evolved like hyperx [3], flattened butterfly [4], and fat-tree [5]. On the other hand, many traffic management techniques emerged, like throughput-based forwarding and load balancing [6]. Typically, the applications of data center produce two types of flows which are mice and elephant flows [6]. Mice flows are known as the smallest and shortest-lived TCP flows in the network and more sensitive to the communication delay. Whereas the most massive and longlived TCP flows, elephant flows, are more affected by the residual link bandwidth [6].

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“…Artzner et al (1999) not only showed the incoherence of VaR but also introduced the Conditional Value at Risk and called it a perfect risk measure, in his paper "Coherent Measures of Risk". In 2000, Pflug (2000) proved that CVaR is a coherent risk measure, based on the coherent risk measure theory (Artzner et al 1999;Thim et al 2012). Furthermore, Du and Escanciano (2016) showed that the advantages of Conditional Value at Risk over VaR are not only theoretical but also have empirical manifestations.…”

Section: Introductionmentioning

confidence: 99%

Estimating Conditional Value at Risk in the Tehran Stock Exchange Based on the Extreme Value Theory Using GARCH Models

Tabasi

Yousefi

Tamošaitienė

et al. 2019

Administrative Sciences

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This paper attempted to calculate the market risk in the Tehran Stock Exchange by estimating the Conditional Value at Risk. Since the Conditional Value at Risk is a tail-related measure, Extreme Value Theory has been utilized to estimate the risk more accurately. Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models were used to model the volatility-clustering feature, and to estimate the parameters of the model, the Maximum Likelihood method was applied. The results of the study showed that in the estimation of model parameters, assuming T-student distribution function gave better results than the Normal distribution function. The Monte Carlo simulation method was used for backtesting the Conditional Value at Risk model, and in the end, the performance of different models, in the estimation of this measure, was compared.

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Value-at-risk and conditional Value-at-risk estimation: A comparative study of risk performance for selected Malaysian sectoral indices

Cited by 4 publications

References 7 publications

Model for Technology Risk Assessment in Commercial Banks

Model for Technology Risk Assessment in Commercial Banks

Methods for Predicting Behavior of Elephant Flows in Data Center Networks

Estimating Conditional Value at Risk in the Tehran Stock Exchange Based on the Extreme Value Theory Using GARCH Models

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