Value-at-Risk for South-East Asian Stock Markets: Stochastic Volatility vs. GARCH

Quang, Paul Bui; Klein, Tony; Nguyen, Nam Hoang; Walther, Thomas

doi:10.3390/jrfm11020018

Cited by 5 publications

(3 citation statements)

References 36 publications

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“…a measure of risk exposure over a specified period. There is abundant literature on the applications of VaR method to measure market portfolio returns during stock market turbulent times (see, for example, Mirjana and Miletic (2015), and Quang et al (2018)). To calculate the VaR of our equity market indexes, we first compute the return series using:…”

Section: Methodsmentioning

confidence: 99%

Did COVID-19 increase equity market risk exposure? Evidence from China, the UK, and the US

Lai

Xiao

2021

Applied Economics Letters

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By studying equity market returns to China, the UK, and the US, we explore the key question of whether the COVID-19 pandemic changes the risk exposure of equity markets, which is fundamental to market stability and investor confidence. Using data from the World Health Organization and Bloomberg, our full sample covers the period 3 July 2019 to 15 December 2020 which facilities a subsample (Normal, Shock, Endurance) analysis. Utilizing Value-at-Risk (VaR) metrics as our risk exposure measure, we find that 1) There exists a sharp increase in equity market risk exposure across the three equity markets. 2) A stronger pandemic impact is found in different market capitalization segments -China, large-cap; the UK, small-cap; the US, mid-cap. 3) Generally, investors consider the number of new cases as a more worrying factor than deaths while UK investors are sensitive to both. Our observations suggest that given limited resources but rising demands from both businesses and households for government assistance, a one-size-fits-all policy to support market recovery would be sub-optimal.

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Section: Methodsmentioning

confidence: 99%

Did COVID-19 increase equity market risk exposure? Evidence from China, the UK, and the US

Lai

Xiao

2021

Applied Economics Letters

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“…We recall that the price spread is the difference between the highest and the lowest price. That reflects the randomness of volatility [15]-that is explained by many hidden events and information [16]-during a specific time period.…”

Section: Approach Demystificationmentioning

confidence: 99%

Gaussian Mixture and Kernel Density-Based Hybrid Model for Volatility Behavior Extraction From Public Financial Data

Tigani

Chaibi

Saadane

2019

Data

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This paper carried out a hybrid clustering model for foreign exchange market volatility clustering. The proposed model is built using a Gaussian Mixture Model and the inference is done using an Expectation Maximization algorithm. A mono-dimensional kernel density estimator is used in order to build a probability density based on all historical observations. That allows us to evaluate the behavior’s probability of each symbol of interest. The computation result shows that the approach is able to pinpoint risky and safe hours to trade a given currency pair.

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“…Even though risk management is considered young, the concept and the benefit of risk management had already given to the nations and enterprises is immense. There are many types of research regarding risk management, such as Hallikas et al (2004), Chen & Hong (2007), Michalski (2009), Platon & Constantinescu (2014), Ito et al (2015) and Quang et al (2018). As these references show, the concept of risk management has evolved and has its benefits regarding the estimation of risk contained in the activities of the company.…”

Section: Introductionmentioning

confidence: 99%

Exchange Rate Forecasting and Value-at-Risk Estimation on Indonesian Currency Using Copula Method

Sirait¹,

Simatupang²

2019

Proceedings of the 3rd International Conference on Accounting, Management and Economics 2018 (ICAME 2018)

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This study aims to determine the future value and the value-at-risk estimation of four selected currencies, namely United States Dollar (USD), Australian Dollar (AUD), European Union Euro (EUR) and Japanese Yen (JPY) against Indonesian Rupiah (IDR). The Monte-Carlo simulation is implemented to estimate the future value of each currency relationship and integrating it with the concept from the copula method; the risk value estimation is conducted using Value-at-Risk (VaR), and the VaR estimation is within the range of 90%, 95% and 99% confidence interval. The copula method we use in this study is Clayton copula because it has the highest log-likelihood value compared to Frank and Gumbel copula; with an addition, each currency uses their regressive model to see if the selected exchange rates have the characteristic of a seasonal or non-seasonal pattern. The result we obtain in this study are JPY/IDR, and EUR/IDR relationships have the highest simulated loss and estimated risk values in each confidence interval.

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Value-at-Risk for South-East Asian Stock Markets: Stochastic Volatility vs. GARCH

Cited by 5 publications

References 36 publications

Did COVID-19 increase equity market risk exposure? Evidence from China, the UK, and the US

Did COVID-19 increase equity market risk exposure? Evidence from China, the UK, and the US

Gaussian Mixture and Kernel Density-Based Hybrid Model for Volatility Behavior Extraction From Public Financial Data

Exchange Rate Forecasting and Value-at-Risk Estimation on Indonesian Currency Using Copula Method

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