• The Korean Journal of Financial Studies
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• v.3 no.2
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• pp.329-364
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• 1996

Writing naked put options is a strategy employed either as a speculation to capture premium income, or as a method of placing a limit order to buy the underlying at the strike price in return for premium received. Using a Monte Carlo simulation, twenty thousand equity prices are generated under known volatility and return parameters. A binomial tree is constructed using the same volatility and return parameters. Put options on these 'equities' are valued with the binomial methodology. The performance of various put writing strategies is evaluated on a risk-adjusted basis. Evidence presented suggests that the judicious use of put options may enhance returns during portfolio construction.

• East Asian Economic Review
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• v.18 no.4
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• pp.367-394
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• 2014

In this study, we examined the fractal structure of the Nikkei225, HangSeng, Shanghai Stock Exchange and Straits Times Index of Singapore. Empirical analysis was performed via non-parametric, semi-parametric long memory tests and also fractal dimension calculations. In order to avoid spurious long memory features, besides the Detrended Fluctuations Analysis (DFA), we also used Smith's (2005) modified GPH method. As for fractal dimension calculations, they were conducted via Box-Counting and Variation (p=1) tests. According to the results, while there is no long memory property in log returns of any index, we found evidence for long memory properties in the volatility of the HangSeng, the Shanghai Stock Exchange and the Straits Times Index. However, we could not find any sign of long memory in the volatility of Nikkei225 index using either the DFA or modified GPH test. Fractal dimension analysis also demonstrated that all raw index prices have fractal structure properties except for the Nikkei225 index. These findings showed that the Nikkei225 index has the most efficient market properties among these markets.

• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1029-1035
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• 2019

In recent years, artificial intelligence (AI) services have become one of the most essential parts to extend human capabilities in various fields such as face recognition for security, weather prediction, and so on. Various learning algorithms for existing AI services are utilized, such as classification, regression, and deep learning, to increase accuracy and efficiency for humans. Nonetheless, these services face many challenges such as fake news spread on social media, stock selection, and volatility delay in stock prediction systems and inaccurate movie-based recommendation systems. In this paper, various algorithms are presented to mitigate these issues in different systems and services. Convolutional neural network algorithms are used for detecting fake news in Korean language with a Word-Embedded model. It is based on k-clique and data mining and increased accuracy in personalized recommendation-based services stock selection and volatility delay in stock prediction. Other algorithms like multi-level fusion processing address problems of lack of real-time database.

• International Area Studies Review
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• v.20 no.3
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• pp.123-144
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• 2016

This paper investigates the linkages between returns both in foreign exchange and stock markets, and uncertainties in two markets using daily data for the period of 16 July 2004 to 30 June 2014 in Nepalese economy. Four hypotheses are tested about how uncertainty influences the stock index and exchange rates. From the empirical results, a bivariate EGARCH-M model is the best to explain the volatility in the two markets. There is a negative relationship from the exchange rates return to stock price return. Empirical results do provide strong empirical confirmation that negative effect of stock index uncertainty and positive effect of exchange rates uncertainty on average stock index. GARCH-in-mean variables in AR modeling are significant and shows that there is positive effect of exchange rates uncertainty and negative effect of stock index uncertainty on average exchange rates. Stock index shocks have longer lived effects on uncertainty in the stock market than exchange rates shock have on uncertainly in the foreign exchange market. The effect of the last period's shock, volatility is more sensitive to its own lagged values.

• East Asian Economic Review
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• v.25 no.1
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• pp.73-97
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• 2021

China has experienced many structural changes in the process of economic development over the past three decades. Using a time-varying parameter VAR model with stochastic volatility and mixture innovations, this study investigates whether such structural changes in, especially tools and operational aims of monetary policy, affect the monetary transmission mechanism. We find that impulse responses of output growth and inflation to monetary shocks have substantially increased and then reversed to decrease around 2005-2006. This time variation is mainly caused by changes in the monetary transmission mechanism, i.e., the manner in which main macroeconomic variables respond to policy shocks, rather than by changes in volatilities of exogenous shocks. The result implies that aggressive monetary policy to facilitate economic growth in the developing economies may be legitimized, unless it causes inflation seriously.

• Journal of the Korean Mathematical Society
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• v.59 no.6
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• pp.1153-1170
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• 2022

In this paper, we investigate the portfolio optimization problem under the SVCEV model, which is a hybrid model of constant elasticity of variance (CEV) and stochastic volatility, by taking into account of minimum-entropy robustness. The Hamilton-Jacobi-Bellman (HJB) equation is derived and the first two orders of optimal strategies are obtained by utilizing an asymptotic approximation approach. We also derive the first two orders of practical optimal strategies by knowing that the underlying Ornstein-Uhlenbeck process is not observable. Finally, we conduct numerical experiments and sensitivity analysis on the leading optimal strategy and the first correction term with respect to various values of the model parameters.

• Analyses & Alternatives
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• v.3 no.1
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• pp.135-157
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• 2019

The linkages between asset prices and macroeconomic outcomes are long-standing issue to both economists and monetary authorities. This paper explores the impact of asset prices on output and price in China. It focuses on the impacts of asset prices on the low quantiles of GDP gap and high quantiles of price gaprespectively. The main findings are the following: the influence of stock price gap, stock returns, and money growth on the different quantile of GDP gap and price gap are noticeable different, and there are significant impacts on the left tail of GDP gap distribution and on the right tail of price gap distribution. This implies that the results coming from simple regression will underestimate the economic risk imposed by asset price volatility. Moreover, these results also provide the caveat that one should cautiously distinguish the meaning of asset price gap and asset price growth rate and use them, through their contents are similar in some sense. One implication for monetarypolicy is that authority should interpret the relationship between asset prices and macro-economy in wider perspectives, and make the policy decision taking the impacts of asset prices on the tails of economy.

• Journal of Information Technology Applications and Management
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• v.19 no.1
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• pp.1-12
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• 2012

Data mining is the process of searching and analyzing large quantities of data for finding out meaningful patterns and rules. Artificial Neural Network (ANN) is one of the tools of data mining which is becoming very popular in forecasting the future values. Some of the areas where it is used are banking, medicine, retailing and fraud detection. In finance, artificial neural network is used in various disciplines including stock market forecasting. In the stock market time series, due to high volatility, it is very important to choose a model which reads volatility and forecasts the future values considering volatility as one of the major attributes for forecasting. In this paper, an attempt is made to develop two models - one using feed forward back propagation Artificial Neural Network and the other using Autoregressive Conditional Heteroskedasticity (ARCH) technique for forecasting stock market returns. Various parameters which are considered for the design of optimal ANN model development are input and output data normalization, transfer function and neuron/s at input, hidden and output layers, number of hidden layers, values with respect to momentum, learning rate and error tolerance. Simulations have been done using prices of daily close of Sensex. Stock market returns are chosen as input data and output is the forecasted return. Simulations of the Model have been done using MATLAB$^{(R)}$ 6.1.0.450 and EViews 4.1. Convergence and performance of models have been evaluated on the basis of the simulation results. Performance evaluation is done on the basis of the errors calculated between the actual and predicted values.

• Journal of Digital Convergence
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• v.13 no.8
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• pp.153-160
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• 2015

The domestic stock market has been subjected to a major change since the September 1997 financial crisis. Foreign capital came repeat themselves in the stock market and bond market, foreign exchange market opening up domestic financial markets after the financial crisis. The domestic stock market has been most affected by domestic capital before the financial crisis. But it has been receiving an absolute influenced by foreign capital after the financial crisis. The purpose of this study is to analyze the trends in the two sections that look at any changes in the volatility of the KOSPI appears after the crisis. To this, obtained a daily weekly monthly normal distribution and kurtosis, skewness degree it should be analyze the tilt phenomenon and variability of the two intervals. This study also predict the future movement of the domestic stock market Based on this, look at the difference between the two sections. Analysis result, after the financial crisis change width has a reduction but direction of the KOSPI has appeared relatively distinct in the medium to long term. Based on this future market seems desirable the mid- to long-term investment looking for direction.

• The Korean Journal of Applied Statistics
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• v.24 no.6
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• pp.1033-1043
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• 2011

In this paper, we use a nested family of models of Generalized Autoregressive Conditional Heteroscedasticity(GARCH) to verify asymmetric conditional heteroscedasticity in the KOSPI and Won-Dollar exchange rate. This study starts from an investigation of whether time series data have asymmetric features not explained by standard GARCH models. First, we use kernel density plot to show the non-normality and asymmetry in data as well as to capture asymmetric conditional heteroscedasticity. Later, we use three representative asymmetric heteroscedastic models, EGARCH(Exponential Garch), GJR-GARCH(Glosten, Jagannathan and Runkle), APARCH(Asymmetric Power Arch) that are improved from standard GARCH models to give a better explanation of asymmetry. Thereby we highlight the fact that volatility tends to respond asymmetrically according to positive and/or negative values of past changes referred to as the leverage effect. Furthermore, it is verified that how the direction of asymmetry is different depending on characteristics of time series data. For the KOSPI and Korean won-US dollar exchange rate, asymmetric heteroscedastic model analysis successfully reveal the leverage effect. We obtained predictive values of conditional volatility and its prediction standard errors by using moving block bootstrap.