Real estate accounts for the largest share of household wealth globally, with an average share of more than 50% of total wealth. In Korea, this share is even higher, with real estate accounting for approximately 60% of total household assets by 2020. Real estate plays a dual role as a provider of housing and as an important investment vehicle. Given the substantial financial commitment required to purchase residential property, home ownership is often seen as an important means of wealth accumulation for households. In addition, the reliance on institutional credit to finance housing purchases amplifies the impact of housing market fluctuations on household wealth, liabilities and the financial stability of banks. In addition, the historical performance of real estate returns and their relationship to business cycles have important implications for academics, investors, financial institutions, regulators, and policymakers. Despite their importance, however, long-term empirical analyses of real estate returns are scarce due to data limitations. The existing literature on real estate based on long-term data has primarily focused on US and European residential and commercial real estate. For example, Jordà, Knoll, Kuvshinov, and Sehularick (2019) highlight that while average real estate returns are slightly lower than equity returns, they exhibit significantly lower volatility. In this study, we analyze the risk-return trade-off of residential real estate in Korea using comprehensive dataset spanning 47 years from 1975 to 2021, the longest sample period to the best of our knowledge. Specifically, the main objectives of this study are threefold. First, we aim to calculate and analyze the nominal and real total returns, risk premia and Sharpe ratios of Korean real estate. Second, we seek to compare Korean real estate returns with international benchmarks. Finally, we evaluate the inflation hedging potential of real estate by examining its effectiveness in mitigating inflation risk over different investment horizons. Our analysis is based on long-term data obtained from multiple sources such as the Bank for International Settlements (BIS), the Bank of Korea, Korea Exchange, Statistics Korea, Kookmin Bank, Korea Housing Bank, and the Real Estate Board. The nominal total return on real estate is the sum of capital gains and rental income. To account for appraisal smoothing in real estate index returns, we apply the adjustment method proposed by Barkham and Geltner (1994). To calculate the rental income of real estate, weneed to use the jeonse-to-price ratio, which has been published by KB Kookmin Bank since 1998. The jeonse system, which is unique to Korea, requires tenants to pay a lump sum for the use of residential property for a specified period. For the period from 1975 to 1998, when jeonse/price ratio data were not available, we estimated a dynamic regression model using variables such as the jeonse price index from the consumer price index, the housing price index, the GDP growth rate, the expected real interest rate and the 3-year government bond rate. Our empirical results show that the average annual nominal total return for residential real estate is 10.82% with a standard deviation of 11.39%. The real return is 5.30% with a standard deviation of 9.65%. The risk premium for real estate is 2.75% and the Sharpe ratio is 0.26. For the same period, the equity risk premium is 7.64% with the Sharpe ratio of 0.25. The difference in the Sharpe ratios between stocks and real estate is not statistically significant, making it challenging to assert that real estate has outperformed equities over the long term in Korea. When compared with the returns of 16 countries, including the United States, the United Kingdom, and Japan, the Korean real estate market exhibits significantly lower risk premia and Sharpe ratios. These differences are statistically significant, suggesting that the risk-adjusted performance of Korean real estate is relatively weak. If housing provides a hedge against the risks associated with future homeownership, households may be willing to pay higher prices even if the risk-adjusted return is lower. Therefore, if the demand for hedging against future housing costs is relatively higher in Korea than in other countries, it is possible that the risk-adjusted returns may be lower. Our analysis further shows that Korean real estate returns exhibit a statistically significant positive correlation with inflation rates. The correlation coefficient increases with the investment horizon, reaching 0.68 and 0.78 for five-year and ten-year horizons, respectively. These findings suggest that real estate serves as an effective hedge against inflation risk in Korea. It is important to note that this study is limited in that it does not account for taxes and transaction costs associated with ownership and transactions. Future research should address the limitations of our analysis by incorporating transaction costs and taxes, and by exploring the implications of individual transaction-level data for a more granular understanding of the market.

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The globalization of the world economy has facilitated the transmission of risk across stock markets during financial crises. Although not directly financial in nature, macroeconomic uncertainties caused by health and geopolitical crises, such as the recent COVID-19 pandemic and the Russia-Ukraine and Israel-Hamas conflicts, have the potential to trigger shocks that may spill over into global financial markets. In this paper, we investigate the effect of macroeconomic shocks, specifically the COVID-19 pandemic and the Russia-Ukraine and Israel-Hamas wars, on the degree of stock market integration between South Korea and the United States. Previous studies present two opposing perspectives on how these recent shocks may affect global stock market integration. On the one hand, during periods of macroeconomic uncertainty, economic lockdowns or sanctions implemented to mitigate the transmission of shocks may reduce international trade volumes and capital flows, which in turn may weaken stock market linkages. On the other hand, another possibility is that stock market linkages may strengthen independently of the real economy. In the event of a global economic shock, emerging economies such as South Korea tend to implement macroeconomic policies similar to those of the United States to mitigate the impact on the real economy. This policy alignment has the potential to result in enhanced integration of stock markets, irrespective of whether these linkages directly reflect real economic conditions. Therefore, the extent to which stock market linkages are strengthened or weakened in response to macroeconomic shocks is an empirical question that merits attention. Furthermore, the degree of integration may vary depending on the nature of the shock, particularly when the shock manifests in different forms. In order to empirically investigate this issue, the DCC-MGARCH model is applied to the daily log return series of the KOSPI and S&P 500 stock market indices for the period from January 1, 2011 to January 31, 2024. We find that the shocks from the COVID-19 pandemic and geopolitical conflicts do not have uniform effects on stock market integration. In particular, during the COVID-19 pandemic crisis, the Korean stock market exhibited a greater degree of integration with the U.S. stock market than in previous years. However, the degree of stock market integration declined sharply during the Russia-Ukraine war. The results suggest that the simultaneous and multiple shutdowns in numerous countries worldwide during the COVID-19 pandemic, coupled with the implementation of quantitative easing policies to mitigate the impact of shocks, have contributed to an increase in stock market integration. In contrast, geopolitical risks, such as localized wars, appear to result in limited spillovers to stock markets. The results of our study provide valuable insights into the evolution of stock market integration during periods of uncertainty, emphasizing the importance of understanding the dynamic nature of stock market integration. Specifically, we suggest that during macroeconomic crises, both investors and policymakers should tailor their strategies according to the specific nature of the shock in order to diversify risk and optimize potential returns. While global stock market synchronization may potentially limit the efficacy of diversification, particularly during global crises, our findings underscore that the degree of integration between countries can exhibit considerable variability during periods of geopolitical risk. This variability presents opportunities for international diversification, which can assist in risk diversification and expected return enhancement. By taking into account the distinct characteristics of various shocks, policymakers and international investors can more effectively navigate macroeconomic uncertainty and implement more effective strategies for risk management. The significance of this study lies in its contribution of empirical evidence demonstrating that macroeconomic shocks exert differential effects on stock market integration, contingent on the nature of the shock. These findings bear significant ramifications for the formulation of government macroeconomic policy, the strategic portfolio composition of international investors, and the advancement of academic knowledge in this field. The study's analysis of the dynamic response of stock markets to diverse macroeconomic shocks, as influenced by their nature, provides a comprehensive framework for understanding these interactions.

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Mini KOSPI 200 options are derivatives based on the KOSPI 200 index, just like the regular KOSPI 200 options. While the contract terms, such as expiration dates and trading hours, are identical, mini options are structured to allow smaller investments by reducing the contract size to one-fifth (from a multiplier of 250,000 KRW to 50,000 KRW). This makes them accessible to smaller investors, aiming to increase retail participation. However, the question remains: have mini options successfully attracted retail investors as originally intended? As market data reveals, the share of retail investors in the mini options market is significantly lower than in the regular options market, contrary to the product’s initial purpose. Institutional investors, particularly securities firms, dominate the mini options market, contributing to its low liquidity. As a result, the liquidity premium in mini options leads to higher pricing compared to regular options with the same strike prices and expiration dates. This creates a barrier for retail investors, who generally prefer long positions and are sensitive to price disparities. Therefore, mini options have not achieved their goal of increasing retail participation and are instead primarily traded by institutional investors. The difference in liquidity and the composition of market participants leads to differing price determination mechanisms between mini and regular options. The distinct characteristics of the mini options market suggest that the optimal option pricing models suitable for regular options may not be applicable to mini options. The Black-Scholes (BS) option pricing model, introduced in 1973, has long been a fundamental tool in the options market, but its limitations have led to the development of many alternatives. The BS model, while advantageous for its simplicity and closed-form solution, fails to accurately reflect real-world variables like volatility and risk-free interest rates. Implied volatility, which is calculated based on option prices, tends to vary with strike prices and expiration times, a phenomenon known as the volatility surface, indicating that the BS model does not fully capture market realities. To overcome these limitations, various alternative models have been proposed, including stochastic interest rate models, stochastic volatility models, jump diffusion models, variance gamma models, and regime-switching models that assume sudden changes in volatility. GARCH models that assume conditional heteroscedasticity in the underlying asset are also used. Additionally, the Ad-Hoc Black-Scholes (AHBS) model is popular among market participants for estimating implied volatility using simple regression analysis. Previous research has shown that stochastic volatility models offer the greatest improvement over the BS model. Studies focused on the regular options market found that the AHBS model outperforms more mathematically complex models, including stochastic volatility models, in both pricing accuracy and hedging performance. Given that mini options display characteristics distinct from regular options, it is necessary to question whether the optimal pricing models used for regular options can still be applied effectively. This study aims to identify the optimal option pricing model for the mini KOSPI 200 options market. By comparing in-sample and out-of-sample pricing accuracy and hedging performance, we aim to recommend the most suitable model for participants in the mini options market. The models under consideration include the BS model, the AHBS model, and models that account for stochastic volatility and jumps. Through this comparison, we examine how differences in liquidity and the composition of market participants affect option pricing and hedging. The study suggests that markets with a high proportion of retail investors may favor simpler models like the BS model, while markets dominated by institutional investors might see better performance with more mathematically complex models. Given that the mini options market has a lower proportion of retail investors compared to regular options, it is expected that more sophisticated models that account for stochastic volatility and jumps will outperform simpler models like the BS model. This study provides several contributions to existing research. First, it is the first to explore the optimal options pricing model specifically for the mini KOSPI 200 options market. While mini options share many characteristics with regular options, their smaller contract sizes and different participantcomposition create unique market dynamics. The study shows that institutional investors play a larger role in the mini options market than in the regular options market, and that mini options have lower liquidity. These factors must be considered when selecting the optimal pricing model. Second, this research utilizes long-term data spanning 90 months, from the market’s inception to the present, offering a comprehensive view of the market’s evolution. Most previous studies focused on the early stages of the mini options market, where liquidity was insufficient. By incorporating a longer time frame, this study is able to capture the effects of market maturity and changes in volatility and liquidity over time. The results are as follows. Models that account for both stochastic volatility and jumps show the best performance in both in-sample and out-of-sample pricing tests. In terms of hedging performance, the AHBS model that includes both first-order and second-order strike prices performs the best. However, the differences in hedging performance across models are relatively small. When the forecasting period is extended to one week, the results remain consistent with the one-day forecast. Monthly performance also shows consistency, with statistically significant differences between the models' performance over the entire sample period. Compared to previous studies on the regular options market, which found that simpler AHBS models provided the best pricing and hedging performance, this study reaches different conclusions. The differences in liquidity and participant composition between the mini and regular options markets play a significant role in the selection of the optimal pricing model.

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