• Communications for Statistical Applications and Methods
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• v.17 no.2
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• pp.293-308
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• 2010

In this paper, we examine the forecasting KOSPI 200 realized volatility by volatility measurements. The empirical investigation for KOSPI 200 daily returns is done during the period from 3 January 2003 to 29 June 2007. Since Korea Exchange(KRX) will launch VKOSPI futures contract in 2010, forecasting VKOSPI can be an important issue. So we analyze which volatility measurements forecast VKOSPI better. To test this hypothesis, we use 5-minute interval returns to measure realized volatilities. Also, we propose a new methodology that reflects the synchronized bidding and simultaneously takes it account the difference between overnight volatility and intra-daily volatility. The t-test and F-test show that our new realized volatility is not only different from the realized volatility by a conventional method at less than 0.01% significance level, also more stable in summary statistics. We use the correlation analysis, regression analysis, cross validation test to investigate the forecast performance. The empirical result shows that the realized volatility we propose is better than other volatilities, including historical volatility, implied volatility, and convention realized volatility, for forecasting VKOSPI. Also, the regression analysis on the predictive abilities for realized volatility, which is measured by our new methodology and conventional one, shows that VKOSPI is an efficient estimator compared to historical volatility and CRR implied volatility.

• Management & Information Systems Review
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• v.33 no.5
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• pp.249-260
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• 2014

This paper explores the relationship between firm-specific volatility and some firm characteristics such as size, the market-to-book ratio of equity, PER, PBR, PCR, PSR and turnover in KOSDAQ market. In addition, I investigate whether portfolios with difference to realized range-based volatility and firm-specific volatility have different investment performance using CAPM and FF-3 factor model. The main findings of this study can be summarized as follows. First, firm-specific volatility have mostly positive relationship between firm-specific volatility and some firm characteristics. Second, this study found that realized range-based volatility and firm-specific volatility are positively related to expected return. It means that portfolios with high idiosyncratic volatility have significantly higher expected return than portfolios with low firm-specific volatility.

• Industrial Engineering and Management Systems
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• v.10 no.1
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• pp.84-94
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• 2011

In this paper, we propose a method to provide the distribution of option price under local volatility model when market-provided implied volatility data are given. The local volatility model is one of the most widely used smile-consistent models. In local volatility model, the volatility is a deterministic function of the random stock price. Before estimating local volatility surface (LVS), we need to estimate implied volatility surfaces (IVS) from market data. To do this we use local polynomial smoothing method. Then we apply the Dupire formula to estimate the resulting LVS. However, the result is dependent on the bandwidth of kernel function employed in local polynomial smoothing method and to solve this problem, the proposed method in this paper makes use of model averaging approach by means of bandwidth priors, and then produces a robust local volatility surface estimation with a confidence interval. After constructing LVS, we price barrier option with the LVS estimation through Monte Carlo simulation. To show the merits of our proposed method, we have conducted experiments on simulated and market data which are relevant to KOSPI200 call equity linked warrants (ELWs.) We could show by these experiments that the results of the proposed method are quite reasonable and acceptable when compared to the previous works.

• The Journal of Asian Finance, Economics and Business
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• v.4 no.4
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• pp.27-37
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• 2017

This study examines the behaviour of staple food price using Multivariate BEKK-GARCH Model. Understanding of staple food price behaviour is important for determining the unpredictability of staple food market and also for policy making. In this paper, we focus on the commodity prices of sugar, rice, soybean and wheat to examine the volatility behaviour of those commodities. The empirical results show that the own-volatility spillover are relatively significant for all food prices. The own-volatility spillover effect for sugar price is relatively large compared with the volatility spillover of other staple food commodities. The findings also highlight that the price volatility of wheat increases during food crisis more than it does when the condition is stable. Also, the own-volatility of rice and wheat in the period of the food crisis is significant and higher compared to the period before food crisis indicates that the past own-volatility effects during food crisis are relatively more difficult to predict because of the uncertainty and high price volatility. Policy recommendations that can be proposed based on the findings are: (1) a better trade agreement in food commodity trade, (2) lower the dependence on wheat importation in Indonesia, and (3) reliable system to minimize food price volatility risks.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.8
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• pp.41-49
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• 2020

This study investigates the short-run and long-run causal behavior of the Philippine stock market index volatility under vector error correction environment. The variables were tested first for stationarity and then long-run equilibrium relationship. Moreover, an impulse response function was estimated to examine the extent of innovations in the independent variables in explaining the Philippine stock market index volatility. The results reveal that the volatility of the Philippine stock market index exhibit long-run equilibrium relationship with Peso-Dollar exchange rate, London Interbank Offered Rate, and crude oil prices. The short-run dynamics-based VECM estimates indicate that in the short-run, increases (i.e., depreciation) in Peso-Dollar exchange rate cause PSEI volatility to increase. As for the London Interbank Offered Rate, it causes increases in PSEI volatility in the short-run. The adjustment coefficients used with the long-run dynamics validates the presence of unidirectional causal long-run relationship from Peso-Dollar exchange rate, London Interbank Offered Rate, and crude oil prices to PSEI volatility, and bidirectional causal long-run relationship between PSEI volatility and London Interbank Offered Rate. The impulse response functions developed within the VECM framework demonstrate the positive and negative reactions of PSEI volatility to unanticipated Peso-Dollar exchange rate, London Interbank Offered Rate, and crude oil price shocks.

• Communications for Statistical Applications and Methods
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• v.18 no.2
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• pp.229-236
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• 2011

A continuous time stochastic volatility model for financial assets suggested by Barndorff-Nielsen and Shephard (2001) is considered, where the volatility process is modelled as an Ornstein-Uhlenbeck type process driven by a general L$\'{e}$vy process and the price process is then obtained by using an independent Brownian motion as the driving noise. The uniform ergodicity of the volatility process and exponential ${\alpha}$-mixing properties of the log price processes of given continuous time stochastic volatility models are obtained.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.10a
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• pp.582-586
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• 2004

The purpose of this study is to examine the lead-lag relationship between volatility and returns in high frequency stock market data to see the validity of two hypotheses that explain volatility asymmetry. Specifically, wavelet analysis is applied to decompose the volatility process into permanent and transitory components and then each component is investigated in conjunction with returns. The results from cross-correlation analysis between volatility and returns support the leverage effect hypothesis rather than the volatility feedback hypothesis in all cases.

• Korean Management Science Review
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• v.27 no.3
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• pp.33-41
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• 2010

A lot of researches find negative volatility risk premium in options market. We can make a trading profit by exploiting the negative volatility premium. This study proposes negative volatility risk premium hypotheses in the KOSPI 200 stock price index options market and empirically test the proposed hypotheses with intra-day short straddle strategy. This strategy sells both at-the-money call option and at-the-money put option at market open and exits the position at market close. Using MySQL 5.1, we create our database with 1 minute option price data of the KOSPI 200 index options from 2004 to 2009. Empirical results show that negative volatility risk premium exists in the KOSPI 200 stock price index options market. Furthermore, intra-day short straddle strategy consistently produces annual profits except one year.

• The Korean Journal of Applied Statistics
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• v.29 no.3
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• pp.505-512
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• 2016

The paper is concerned with high frequency financial time series. A weighted hybrid volatility is suggested to compute daily volatilities based on high frequency data. Various realized volatility (RV) computations are reviewed and the weights are chosen by minimizing the differences between the hybrid volatility and the realized volatility. A high frequency time series of KOSPI200 index is illustrated via QLIKE and Theil-U statistics.

• Communications for Statistical Applications and Methods
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• v.25 no.6
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• pp.659-671
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• 2018

In this study, we consider the extension of the heterogeneous autoregressive (HAR) model for realized volatility by incorporating a neural network (NN) structure. Since HAR is a linear model, we expect that adding a neural network term would explain the delicate nonlinearity of the realized volatility. Three neural network-based HAR models, namely HAR-NN, $HAR({\infty})-NN$, and HAR-AR(22)-NN are considered with performance measured by evaluating out-of-sample forecasting errors. The results of the study show that HAR-NN provides a slightly wider interval than traditional HAR as well as shows more peaks and valleys on the turning points. It implies that the HAR-NN model can capture sharper changes due to higher volatility than the traditional HAR model. The HAR-NN model for prediction interval is therefore recommended to account for higher volatility in the stock market. An empirical analysis on the multinational realized volatility of stock indexes shows that the HAR-NN that adds daily, weekly, and monthly volatility averages to the neural network model exhibits the best performance.