• The Korean Journal of Applied Statistics
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• v.31 no.2
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• pp.205-216
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• 2018

This paper is concerned with volatility computations for high frequency time series. A threshold-asymmetric realized volatility (T-RV) is suggested to capture a leverage effect. The T-RV is compared with various conventional volatility computations including standard realized volatility, GARCH-type volatilities, historical volatility and exponentially weighted moving average volatility. High frequency KOSPI data are analyzed for illustration.

• 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.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.7
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• pp.1-15
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• 2022

Combining the strength of both Mixed Data Sampling (MIDAS) Regression and realized variance measures, this paper seeks to investigate two objectives: (1) evaluate the post-sample performance of the proposed weekly Realized Variance-MIDAS (RVar-MIDAS) in one-week ahead volatility forecasting against the established Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model and the less explored but robust STES (Smooth Transition Exponential Smoothing) methods. (2) comparing forecast error performance between realized variance and squared residuals measures as a proxy for actual volatility. Data of seven private equity mutual fund indices (generated from 57 individual funds) from two different time periods (with and without financial crisis) are applied to 21 models. Robustness of the post-sample volatility forecasting of all models is validated by the Model Confidence Set (MCS) Procedures and revealed: (1) The weekly RVar-MIDAS model emerged as the best model, outperformed the robust DAILY-STES methods, and the weekly DAILY-GARCH models, particularly during a volatile period. (2) models with realized variance measured in estimation and as a proxy for actual volatility outperformed those using squared residual. This study contributes an empirical approach to one-week ahead volatility forecasting of mutual funds return, which is less explored in past literature on financial volatility forecasting compared to stocks volatility.

• 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.

• 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.

• Journal of Korea Trade
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• v.23 no.2
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• pp.110-126
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• 2019

Purpose - This paper examines the recently realized continuous volatility and discrete jumps of US Dollar/Euro returns using the frequency of five minute returns spanning the period from February 2010 through February 2018with periodicity filters. Design/Methodology - This paper adopts the nonparametric estimation. The realized volatility and Realized Outlying Weighted variations show non-Gaussian, fat-tailed, and leptokurtic distributions. Some significant volatility jumps in returns occurred from 2010 through 2018, and the very exceptionally large and irregular jumps occurred around 2010-2011, after the EU financial crisis, and 2015-2016. The outliers occurred somewhat frequently around the years of 2015 and 2016. Originality/value - When we include periodicity filters of volatility such as MAD, Short Half Scale, and WSD, the five minute returns of US Dollar/Euro exchange rates have smaller daily jump probabilities by 20-30% than when we do not include the periodicity filters of volatility. Thus, when we consider the periodicity filters of volatility such as MAD, Short Half Scale, and WSD, the five minute returns of US Dollar/Euro have considerably smaller jump probabilities.

• Communications for Statistical Applications and Methods
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• v.23 no.3
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• pp.203-213
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• 2016

We investigate volatility spillover aspects of realized volatilities (RVs) for the log returns of the Korea Composite Stock Price Index (KOSPI) and the Hang Seng Index (HSI) from 2009-2013. For all RVs, significant long memories and asymmetries are identified. For a model selection, we consider three commonly used time series models as well as three models that incorporate long memory and asymmetry. Taking into account of goodness-of-fit and forecasting ability, Leverage heteroskedastic autoregressive realized volatility (LHAR) model is selected for the given data. The LHAR model finds significant decompositions of the spillover effect from the HSI to the KOSPI into moderate negative daily spillover, positive weekly spillover and positive monthly spillover, and from the KOSPI to the HSI into substantial negative weekly spillover and positive monthly spillover. An interesting result from the analysis is that the daily volatility spillover from the HSI to the KOSPI is significant versus the insignificant daily volatility spillover of the KOSPI to HSI. The daily volatility in Hong Kong affects next day volatility in Korea but the daily volatility in Korea does not affect next day volatility in Hong Kong.

• 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.

• The Korean Journal of Applied Statistics
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• v.35 no.1
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• pp.93-104
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• 2022

In forecasting realized volatility of the major US stock price indexes (S&P 500, Russell 2000, DJIA, Nasdaq 100), internet search volume reflecting investor's interests and implied volatility are used to improve forecast via a deep learning method of the LSTM. The LSTM method combined with search volume index produces better forecasts than existing standard methods of the vector autoregressive (VAR) and the vector error correction (VEC) models. It also beats the recently proposed vector error correction heterogeneous autoregressive (VECHAR) model which takes advantage of the cointegration relation between realized volatility and implied volatility.

• Journal of the Korean Data and Information Science Society
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• v.22 no.6
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• pp.1199-1215
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• 2011

Relaxing an unrealistic assumption of a representative percolation model, this paper demonstrates that herd behavior leads to a high increase in volatility but not trading volume, in contrast with information flows that give rise to increases in both volatility and trading volume. Although detecting herd behavior has posed a great challenge due to its empirical difficulty, this paper proposes a new methodology for detecting trading days with herding. Furthermore, this paper suggests a herd-behavior-stochastic-volatility model, which accounts for herding in financial markets. Strong evidence in favor of the model specification over the standard stochastic volatility model is based on empirical application with high frequency data in the Korean equity market, strongly supporting the intuition that herd behavior causes excess volatility. In addition, this research indicates that strong persistence in volatility, which is a prevalent feature in financial markets, is likely attributed to herd behavior rather than news.