Are the KOSPI 200 implied volatilities useful in value-at-risk models?

Kim, Jun Sik; Ryu, Doojin

doi:10.1016/j.ememar.2014.11.001

Cited by 41 publications

(23 citation statements)

References 67 publications

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“…Numerous articles that examine the fitting and forecasting ability of the US' implied volatility index demonstrate its superiority over historical volatilities (Banerjee et al, 2007;Becker et al, 2007;Carr and Wu, 2006;Corrado and Miller, 2005;Frijns et al, 2010;Jiang and Tian, 2007;Konstantinidi et al, 2008;Simon, 2003). Some studies also investigate implied volatility indices for quantifying market risk and for risk management purposes (Giot, 2005;Kim and Ryu, 2015b). However, a thorough investigation of time-series and statistical properties of implied volatility indices based on advanced econometric approaches is relatively scant.…”

Section: Introductionmentioning

confidence: 99%

“…Han, Guo et al (2012) and Lee and Ryu (2013) investigate the asymmetric volatility phenomenon using the VKOSPI dataset. Lee and Ryu (2014a) and Kim and Ryu (2015b) examine the applicability of the VKOSPI toward constructing investment strategies and in the value-at-risk framework, respectively. Lee and Ryu (2014b) examine the lead-lag relationship between the VKOSPI and its domestic stock market index (KOSPI 200) using a two-regime threshold vector error correction model.…”

Section: _________________________mentioning

confidence: 99%

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Effects of the US Stock Market Return and Volatility on the VKOSPI

2015

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The KOSPI (Korea Composite Stock Price Index) 200 options are one of the most actively traded derivatives in the world. This paper empirically examines (a) the statistical properties of the Korea's representative implied volatility index (VKOSPI) derived from the KOSPI 200 options and (b) the macroeconomic and financial variables that can predict the implied volatility process of the index, using augmented heterogeneous autoregressive (HAR) models with exogenous covariates. The results suggest that the elaborate HAR framework is proficient at describing the dynamics of the VKOSPI and that some domestic macroeconomic variables explain the VKOSPI. More importantly, we find that the stock market return and implied volatility index of the US market (i.e., the S&P 500 spot return and the VIX from the S&P 500 options) play a key role in predicting the level of the VKOSPI and explaining its dynamics, and their explanatory power dominates that of domestic macro-finance variables. Further, while the domestic stock market return does not predict the VKOSPI, the US stock market return does so rather well. When two global factors, both the US stock market return and the US implied volatility index, are incorporated into the HAR framework, the model exhibits the best performance in terms of both in-sample fitting and out-of-sample forecasting ability. JEL C22 C50 G14 G15

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Section: Introductionmentioning

confidence: 99%

Section: _________________________mentioning

confidence: 99%

Effects of the US Stock Market Return and Volatility on the VKOSPI

2015

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“…Bakshi et al (2000), Lin et al (2011), andWang, Huang, andKuo (2014) use intraday samples that cover only 6 months (March to August 1994, July to December 2006, and January to June 2009, respectively). 4 Previous studies on Korea's derivatives markets explain the top-tier position and significance of the KOSPI 200 options market, illustrating how option products can be used (Guo, Han, & Ryu, 2013;Kim & Ryu, 2015b;Lee, Kang, & Ryu, 2015;Lee & Ryu, 2014;Ryu, Kang, & Suh, 2015). Sim et al (2016) examine KOSPI 200 options data covering 2 years (2010-2011) and ignore recent market reforms (e.g., changes in option multipliers, regulation of speculators, and significantly increased requirements for the size of individual margin accounts), which have changed the trading environment and the composition of participating investors, possibly affecting the violation rate.…”

Section: Introductionmentioning

confidence: 99%

“…For example, ITM & Large indicates large trades in the ITM option market.18 Previous studies claim that domestic individual investors prefer to invest in the KOSPI 200 options market to exploit the high leverage effect of OTM options and the lottery-like properties of the KOSPI 200 OTM options trading, whereas foreign investors, who are more sophisticated and better informed, prefer ITM options trading to exploit the high sensitivity provided by the ITM options(Ahn et al, 2008(Ahn et al, , 2010Chung et al, 2016;Kim & Ryu, 2015b). For each option moneyness, this table presents the occurrence rates of the Types I-V violations by further classifying options trades based on their sizes.…”

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confidence: 99%

Option Market Characteristics and Price Monotonicity Violations

Yang

Choi

Ryu

2016

Journal of Futures Markets

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This study reexamines whether option price monotonicity properties hold in a liquid market with little market friction and considers the validity of the monotonicity properties in light of option market characteristics. We confirm that option prices do not monotonically correlate with underlying spot prices and that call and put prices often increase or decrease together, indicating that the monotonicity properties are not consistent with the observed option price dynamics. The violations of monotonicity properties are associated with not only market microstructure effects, trade sizes, and option leverage but also other market characteristics such as trade direction, individual investor demand, foreign investor trading, and market volatility. Violation occurrences tend to be clustered, and their relationship with option market characteristics has not been affected by the recent market reform. © 2016 Wiley Periodicals, Inc. Jrl Fut Mark 37:473–498, 2017

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“…These models are used to estimate their daily VaR values for the Standard & Poor's Depositary Receipts (SPDRs). Kim and Ryu (2015) propose a modified value-at-risk (VaR) model that utilizes the implied volatilities extracted from the KOSPI 200 options. They find that the model-free implied volatility index of the KOSPI 200 (VKOSPI) does not greatly enhance the performance of suggested VaR models.…”

Section: Introductionmentioning

confidence: 99%

On Forecasting Taiwanese Stock Index Option Prices: The Role of Implied Volatility Index

Wang¹,

Liu²,

Chen³

2017

IJEF

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This paper aims to propose four volatility measures: The first is the GARCH model advocated by Bollerslev (1986); the second is the GARCH VIX model which extends the GARCH model by including the volatility index (VIX) as explanatory variable for volatility; the last two are HS 20D and HS 252D , which represent the historical volatilities generated by traditional rolling window technique with 20-and 252-day historical index returns data, respectively. We examine the price information on VIX to improve the predictive performance of GARCH model for valuing TAIEX stock index call options (TXO) over the period from January 2014 to May 2015. Empirical results firstly indicate that both the GARCH and GARCH VIX models consistently perform better than the historical volatility models for forecasting call value of TXO under different moneynesses. Secondly, the GARCH VIX model significantly outperforms the GARCH model for most cases, indicating that the GARCH-based option price forecasts can be effectively improved with the additional information contained in VIX. Finally, the use of GARCH VIX model can greatly reduce model mispricing especially for out-the-money TXO option case. Thus, volatility index is crucial for option traders to efficiently predict TXO option value with GARCH model.

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Are the KOSPI 200 implied volatilities useful in value-at-risk models?

Cited by 41 publications

References 67 publications

Effects of the US Stock Market Return and Volatility on the VKOSPI

Effects of the US Stock Market Return and Volatility on the VKOSPI

Option Market Characteristics and Price Monotonicity Violations

On Forecasting Taiwanese Stock Index Option Prices: The Role of Implied Volatility Index

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