• Management Science and Financial Engineering
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• v.17 no.2
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• pp.99-129
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• 2011

This paper analytically studies how to choose hedging instrument for firms with steady operating cash flows from value maximization perspective. I derive a formula to determine option's optimal strike that makes hedged cash flow have the best monetary payoff given a hedger's view on the underlying asset. I find that not only the expected mean but also the expected standard deviation of the underlying asset in relation to the forward price and the implied volatility play a crucial role in making optimal hedging decision. Higher moments play a certain part in hedging decision but to a lesser degree.

• East Asian Economic Review
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• v.22 no.1
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• pp.29-54
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• 2018

Using the indicators of economic policy uncertainty developed by Baker et al. (2016), this paper investigates the effects of the US economic policy uncertainty on the Korea economic uncertainty as well as Korea-US foreign exchange risk. The key findings are that: (i) the degree of spillovers of policy uncertainty from the US to Korea is considerable but not comparatively high; (ii) the US policy uncertainty plays a stronger and more consistent role in Korean currency risk than Korea policy uncertainty and other macro variables. It implies that the economic policy uncertainty in the US is an important contributor to Korea-US exchange rates.

• The Korean Journal of Financial Management
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• v.22 no.2
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• pp.91-105
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• 2005

Using Campa and Chang's Expectations Hypothesis model, We test the expectations hypothesis in the term structure of volatilities in options on KOSPI 200 by using daily dosing prices from January 1999 to December 2003. In particular, it addresses whether long-dated volatilities are consistent with expected future short-dated volatilities, assuming rational expectation. Our results do not support the expectations hypothesis : long-term volatilities rise relative to short-term volatilities, but the increases are not matched as predicted by the expectations hypothesis. In addition, an increase in the current long-term volatilities relative to the current short-term volatilities is followed by at a random.

• Information Systems Review
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• v.23 no.1
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• pp.23-43
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• 2021

Recently, many studies have been conducted to increase the accuracy of stock price prediction by analyzing candlestick charts using artificial intelligence techniques. However, these studies failed to consider the time-series characteristics of candlestick charts and to take into account the emotional state of market participants in data learning for stock price prediction. In order to overcome these limitations, this study produced input data by combining volatility index and candlestick charts to consider the emotional state of market participants, and used the data as input for a new method proposed on the basis of combining variantion autoencoder (VAE) and attention mechanisms for considering the time-series characteristics of candlestick chart. Fifty firms were randomly selected from the S&P 500 index and their stock prices were predicted to evaluate the performance of the method compared with existing ones such as convolutional neural network (CNN) or long-short term memory (LSTM). The results indicated the method proposed in this study showed superior performance compared to the existing ones. This study implied that the accuracy of stock price prediction could be improved by considering the emotional state of market participants and the time-series characteristics of the candlestick chart.

• The Journal of Asian Finance, Economics and Business
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• v.3 no.3
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• pp.79-91
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• 2016

This paper is the first of its kind using a non-linear approach based on cross-correlation function (CCF) to investigate the information arrival hypothesis in crude palm oil (CPO) futures market. Based on daily data from 1986 to 2010, our empirical results reveal that: First, the volume of volatility is not a proxy of information flow. Second, dependence causality running from current return to future volume in conditional variance exhibit an asymmetric pattern of time span with different signs of correlation between price and volume series. This finding indicates the presence of noise traders' hypothesis of price-volume interaction in CPO futures market. Both findings suggest that this futures market is weak-form inefficiency. In terms of investors' behavior, they tend to change their expectations on current return based on errors made in previous trade in generating abnormal volume in the subsequent period. As implied, it is advisable for the investors devise their future trading strategies according to time span and changes of return.

• East Asian Economic Review
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• v.27 no.3
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• pp.213-242
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• 2023

This paper investigates whether the price of cryptocurrency is determined by the US dollar index, the price of investment assets such gold and oil, and the implied volatility of the KOSPI. Overall, the returns on cryptocurrencies are best predicted by the trading volume of the cryptocurrency both in-sample and out-of-sample. The estimates of gold and the dollar index are negative in the return prediction, though they are not significant. The dollar index, gold, and the cryptocurrencies seem to share characteristics which hedging instruments have in common. When investors take notice of the imminent market risks, they increase the demand for one of these assets and thereby increase the returns on the asset. The most notable result in the out-of-sample predictability is the predictability of the returns on value-weighted portfolio by gold. The empirical results show that the restricted model fails to encompass the unrestricted model. Therefore, the unrestricted model is significant in improving out-of-sample predictability of the portfolio returns using gold. From the empirical analyses, we can conclude that in-sample predictability cannot guarantee out-of-sample predictability and vice versa. This may shed light on the disparate results between in-sample and out-of-sample predictability in a large body of previous literature.

• Economic Analysis
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• v.25 no.1
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• pp.1-33
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• 2019

This paper examines the economic factors that are related to the dynamics of the variance risk premium, and specially, which economic factors are related to the forecasting power of the variance premium regarding future index returns. Eleven general economic variables, eight interest rate variables, and eleven sentiment-associated variables are used to figure out the relevant economic variables that affect the variance risk premium. According to our empirical results, the won-dollar exchange rates, foreign reserves, the historical/implied volatility, and interest rate variables all have significant coefficients. The highest adjusted R-squared is more than 65 percent, indicating their significant explanatory power of the variance risk premium. Next, to verify the economic variables associated with the predictability of the variance risk premium, we conduct forecasting regressions to predict future stock returns and volatilities for one to six months. Our empirical analysis shows that only the won-dollar exchange rate, among the many variables associated with the dynamics of the variance risk premium, has a significant forecasting ability regarding future index returns. These results are consistent with results found in previous studies, including Londono (2012) and Bollerslev et al. (2014), which show that the variance risk premium is related to global risk factors.