Impact of non-normal return and market capitalization on estimation of VaR

Abstract

Purpose – This paper aims to investigate the effect of non-normality in returns and market capitalization of stock portfolios and stock indices on value at risk and conditional VaR estimation. It is a well-documented fact that returns of stocks and stock indices are not normally distributed, as Indian financial markets are more prone to shocks caused by regulatory changes, exchange rate fluctuations, financial instability, political uncertainty and inadequate economic reforms. Further, the relationship of liquidity represented by volume traded of stocks and the market risk calculated by VaR of the firms is studied. Design/methodology/approach – In this paper, VaR is estimated by fitting empirical distribution of returns, parametric method and by using GARCH(1,1) with Student’s t innovation method. Findings – It is observed that both the stocks, stock indices and their residuals exhibit non-normality; therefore, conventional methods of VaR calculation are not accurate in real word situation. It is observed that parametric method of VaR calculation is underestimating VaR and CVaR but, VaR estimated by fitting empirical distribution of return and finding out 1-a percentile is giving better results as non-normality in returns is considered. The distributions fitted by the return series are following Logistic, Weibull and Laplace. It is also observed that VaR violations are increasing with decreasing market capitalization. Therefore, we can say that market capitalization also affects accurate VaR calculation. Further, the relationship of liquidity represented by volume traded of stocks and the market risk calculated by VaR of the firms is studied. It is observed that the decrease in liquidity increases the value at risk of the firms. Research limitations/implications – This methodology can further be extended to other assets’ VaR calculation like foreign exchange rates, commodities and bank loan portfolios, etc. Practical implications – This finding can help risk managers and mutual fund managers (as they have portfolios of different assets size) in estimating VaR of portfolios with non-normal returns and different market capitalization with precision. VaR is used as tool in setting trading limits at trading desks. Therefore, if VaR is calculated which takes into account non-normality of underlying distribution of return then trading limits can be set with precision. Hence, both risk management and risk measurement through VaR can be enhanced if VaR is calculated with accuracy. Originality/value – This paper is considering the joint issue of non-normality in returns and effect of market capitalization in VaR estimation.

Unveiling Sri Lanka’s brain drain and labour market pressure: A study of macroeconomic factors on migration

“…Each variable in a VAR model has a linear function of its prior values and the values of other variables [ 44 , 45 ]. The mathematical Eq 1 of the VAR model, initially supported by [ 46 , 47 ] represents the relationship of variables to elucidate macroeconomic and socioeconomic factors impacting migration in Sri Lanka.…”

Keterkaitan Managerial Ownership Dan Leverage Terhadap Nilai Perusahaan: Dalam Kontek Hubungan Keagenan Perusahaan Di Indonesia

“…Menurut Sinha & Agnihotri (2015) nilai dari perusahaan (nilai perusahaan) yang terdaftar di bursa dapat dijadikan rujukan dalam setiap transaksi adalah nilai kapitalisasi pasar. Saat manajemen perusahaan dikelola sendiri oleh managerial ownership, maka manajer akan juga termotivasi untuk memperkaya diri sendiri.…”

Simulação de séries temporais correlacionadas

“…O conceito de valor em risco (Value-at-Risk; VaR) é comum na literatura como medida de risco uma variável aleatória quantificada em termos de retornos financeiros (assunto detalhadamente explicado na seção 2.3). Autores como Sinha & Agnihotri (2014) & Alegre (2006), Hu & Kercheval (2010), He et al (2012), Hsu et al (2012), Ye & Li (2012), Olson & Wu (2013), Berger & Missong (2014), Gambrah & Pirvu (2014), Lu et al (2014), Sitima & Hlatywayo (2015), Chen & Khaldoun (2015), Razak & Ismail (2015), Righi & Ceretta (2015), Siburg et al (2015), Fengler & Okhrin (2016; Krzemienowski & Szymczyk (2016), Pourbabaee et al (2016). A literatura é bastante extensa para modelos de análise de risco para horizonte de 1 dia, mas muito pouco destaque é dado para períodos maiores de investimento.…”