This study comprehensively analyzes the patterns of risk and return relationship in the Korean stock market and applies them to portfolio management strategies. Total eight number of risk measures - long and short term total volatility, systematic volatility, idiosyncratic volatility, and market beta - are employed to analyze the risk-return relationship in the KOSPI market from July 1990 to December 2021. Furthermore, based on the results of the analysis, we conduct various empirical experiment to improve the profitability of the low-risk related ETFs traded in the Korean stock market. The key findings are as follows: First, we observe that the low-risk anomalies are significant over the entire period only for the short-term volatility measures. Small-growth groups show greater low-risk premiums for long-short portfolios, whereas small-value groups exhibit higher low-risk premiums for long-only portfolios. Second, the low-risk anomalies are not robust across the risk measures and measurement periods. Third, as a result of the causality analysis between the risk premiums from the different risk measures, we find that the low-risk anomalies in the Korean stock market are primarily driven by idiosyncratic volatility anomaly. Lastly, we find that the definition of underlying indices of the low-risk related ETFs currently traded in the Korean stock market does not adequately reflect the empirical patterns of the risk-return relationship. If we modify them in accordance with our empirical findings, the profitability of the low-risk indices significantly improve with robustness. This study has contributions in that it deepen the understanding of the low-risk anomalies in the Korean stock market and proposes practically useful applications of them to enhance portfolio management strategies.

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Motivated by the mixed findings of the existing literature, we investigate the performance of the volatility targeting strategy in four Asian equity markets based on a block-bootstrap simulation. We found no clear evidence of outperformance of the strategy against a 50/50 constant-mix portfolio, except in the Chinese market where volatility and tail risk are highest. The lack in performance has been attributed to an inability to detect the negative relationship between volatility and returns by all of the volatility forecasting models we consider. This may affect the capacity to reduce downside risk and tail risk but consistently causes a substantial drag on performance. Owing to forecasting errors, the strategy appears to perform considerably better and easier to implement in practice when using a model with low sensitivity to volatility changes along with a rebalancing buffer.

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Virtual assets have experienced dramatic ups and downs over the past few years, since an anonymous developer named Satoshi Nakamoto introduced Bitcoin in 2008. Bitcoin is a peer-to-peer electronic cash system based on blockchain technology, enabling payments and transactions verification without a centralized custodian. Despite controversies surrounding Bitcoin's currency status (IMF, 2016; Yermack, 2015; Hendrickson et al., 2015), there is a consensus that virtual assets function as alternative investment assets providing portfolio diversification, inflation hedging, and exposure to new technology effects. (Glaser et al., 2014; Briere et al., 2015; Bouri et al., 2017; Choi, 2022) Consequently, the market for virtual assets has evolved significantly, with over 26,346 types of virtual assets issued by the end of June 2023, and their total market capitalization estimated to exceed $11.7billion (coinmarketcap). Despite recognition of the legitimacy of virtual assets by major countries such as the US, EU, Japan, and Korea, attempts to bring them under policy consideration have led to differing regulatory systems and relative scales of supply and demand across countries, resulting in price deviations. Notably, the daily average price ratio between the USD and KRW Bitcoin market exceeded 15% from December 2017 to February 2018, a phnomenon referred to as the 'Kimchi premium' in both popular press and academic research (Makarov ans Schoar, 2020; Yang, 2019). These deviations from the law of one prices is even more pronounced due to the untraceable cross-border trading of virtual assets via peer-to-peer (P2P) transaction facilitated by advanced blockchain technology (Nakanoto, 2008). It seems that constraints and frictions in the arbitrage capital flow have led to market segmentation and significant, persistent price deviations between countries. However, there has not been a systematic analysis of the arbitrage trading across borders and the price deviation between countries due to the lack of transaction data in the virtual asset market. This paper attempts to fill this gap in the previous studies. In this study, daily trading data from September 2017 to December 2022 for virtual assets such as Bitcoin(BTC), Ethereum(ETH), Ripple(XRP), Cardano(ADA), and Dogecoin(DOGE) were used. For each coin, the price deviations between the KRW domestic and the USD international market were calculated by subtracting the KRW/USD spot exchange rate from each coin's exchange rate, which is the ratio of its KRW closing price to its USD closing price on the same trading day. Subsequently, a price disparity index was constructed by extracting a common factor from the price deviations of each coin using principal componant analysis(PCA) methodology. This study examined the time-series characteristics of the disparity index and the economic factors related to the index. Our analysis showed that the disparity index co-moved with the KRW price index, indicating that price deviations occur during periods of a rapid appreciation in the KRW coin market. The disparity index increased significantly after the implementation of a new regulation such as the Travel Rule, implying that frictions in the arbitrage activities lead to a market segmentation and price deviations across countries. In the multi-variate regression analysis, where the price disparity index was regressed on cryptocurrency market sentiment variables, financial market variables, commodity and stock market variables, the disparity index was closely related to investor sentiment in both the KRW and USD coin market, as well as to the movement of international financial markets, while showing no significant relationship with commodity and stock markets. Next, for each coin, the idiosyncratic price deviation was extracted by subtracting the disparity index from each coin's price deviations. The unique price deviations between the KRW and the USD market observed in individual coins were influenced by demand factors specific to each coin, unlike the disparity index. Specifically, the idiosyncratic price deviations on the previous day, daily returns, and KRW trading volume exhibited positive relationships with idiosyncratic price deviations in the four coins, and these results were maintained even after controlling for other variable. Meanwhile, using the daily change in price deviation as a proxy variable, this study divided the entire sample into two groups: one estimated to have arbitrage trading and one without, and examined whether arbitrage trading contributes to resolving the price disparity. The sample estimated to have no arbitrage trading showed a higher Kimchi premium than the sample estimated to have arbitrage trading, indicating that arbitrage transactions contribute to resolving price deviations between the KRW and the USD market. The sample without arbitrage trading showed larger KRW trading volume and lower Amihud measure compared to the sample with arbitrage trading, exhibiting a stronger tendency towards momentum trading that follows price deviations, rather than engaging in contrarian trading for arbitrage purposes.

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We investigate the impact of price limit expansion on stocks’ realized volatility, uniquely integrating both magnet effect and correction effect into our model. Our findings reveal that price limit expansion may increase or decrease stock volatility, with the magnet effect and stocks’ inherent volatility levels as primary driving forces, and the correction effect playing a minor role. Typically, a decrease in volatility occurs when a stock’s inherent volatility is moderate (i.e., neither too high nor too low), while an increase takes place when it is relatively high. We offer an insightful explanation of these contrasting behaviors in relation to the magnet effect and stocks’ inherent volatility.

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