The aim of this paper is to propose a real options framework to measure and manage bubbles in the Korean real estate market. The proposed framework carefully defines and utilizes the unique leasing mechanism in Korea, called the Jeonse system, a tentative contract for one or two years with a large amount of deposit, to represent the value of residence. Furthermore, the proposed framework applies the volatility with heteroscedasticity to improve the numerical accuracy in comparison to the traditional real options valuation model. The results of the model ultimately suggest the investment strategy that takes into account the measured bubbles in the market. Specifically, given that the Korean real estate market could be regarded as an American option, the investment strategy with early exercise completely eliminates the existing arbitrage opportunities in both long and short positions. In this context, the investment decisions based on the results of the proposed framework are expected to encourage the reflection of bubble-related information in the market, which eventually reduces the formation of bubbles via market mechanism for arbitrage elimination. In conclusion, the bubble-related information obtained from the model is expected to contribute to the stability of the real estate market by reducing the volatility of house price and quick price adjustment to new information.
The global economy is under great shock again in 2020 due to the COVID-19 pandemic; it has not been long since the global financial crisis in 2008. Therefore, we investigate the evolution of the complexity of the cryptocurrency market and analyze the characteristics from the past bull market in 2017 to the present the COVID-19 pandemic. To confirm the evolutionary complexity of the cryptocurrency market, three general complexity analyses based on nonlinear measures were used: approximate entropy (ApEn), sample entropy (SampEn), and Lempel-Ziv complexity (LZ). We analyzed the market complexity/unpredictability for 43 cryptocurrency prices that have been trading until recently. In addition, three non-parametric tests suitable for non-normal distribution comparison were used to cross-check quantitatively. Finally, using the sliding time window analysis, we observed the change in the complexity of the cryptocurrency market according to events such as the COVID-19 pandemic and vaccination. This study is the first to confirm the complexity/unpredictability of the cryptocurrency market from the bull market to the COVID-19 pandemic outbreak. We find that ApEn, SampEn, and LZ complexity metrics of all markets could not generalize the COVID-19 effect of the complexity due to different patterns. However, market unpredictability is increasing by the ongoing health crisis.
The aim of this research is to propose a binary segmentation algorithm to detect the change points in financial time-series based on the Iterative Cumulative Sum of Squares (ICSS). The proposed algorithm, entitled KW-ICSS, utilizes the non-parametric Kruskal-Wallis test in cross-validation procedures. In this regard, KW-ICSS can quickly detect the change points in non-normally distributed time-series with a small number of observations after the change points than the state-of-the-art ICSS algorithm, entitled AIT-ICSS. For the simulated financial time-series whose true location of the change point is known, KW-ICSS detects the change points with the average true positive rate of 81% for the different number of change points, whereas AIT-ICSS only exhibits 72.57%. Also, KW-ICSS's mean absolute deviation between the true and detected change points is less than that of AIT-ICSS for different significance levels. The experiment also finds that the significance level, the model parameter, should be set to less than 10%. For the real-world financial time-series whose true location of change points is unknown, KW-ICSS's robust detection of change points is observed from fewer detected change points and longer intervals between them. Furthermore, KW-ICSS's trend prediction for the short-term future performs with an average of 92.47% accuracy, whereas AIT-ICSS shows 90.69%. Therefore, we claim that KW-ICSS successfully improves AIT-ICSS.INDEX TERMS Unsupervised learning, change point detection, iterative cumulative sum of squares, Kruskal-Wallis.
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