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History of remote sensing studies of the Moon and asteroids changed when lunar samples were returned by the Apollo 11 mission and many meteorites were discovered on Antarctica starting in 1969. Discovery of the isotopic similarity between lunar and terrestrial materials led us to the giantimpact model to form the Moon. In addition, the existence and nature of space weathering were also discovered in 1993 by analyzing the Apollo samples. Another change occurred in 2010 when the Hayabusa spacecraft returned particles of asteroid Itokawa that proved the identity between many S-type asteroids and ordinary chondrites and the existence of space weathering similar to the Moon. The second sample return from asteroids occurred in 2020 when the Hayabusa2 spacecraft returned samples of Ctype asteroid Ryugu. In spite of some expectations, it was a pristine CI1 chondrite material that was free from terrestrial contaminations suffered by known CI1 chondrite meteorites. Sample return missions drastically improved the accuracy of our knowledge on the raw materials of solar system planets and will surely keep revealing the secrets behind the birth of this special planet Earth. This part of history also teaches us that scientists should proclaim the truth against denial or persecution by others.

History of remote sensing studies of the Moon and asteroids changed when lunar samples were returned by the Apollo 11 mission and many meteorites were discovered on Antarctica starting in 1969. Discovery of the isotopic similarity between lunar and terrestrial materials led us to the giant-impact model to form the Moon. In addition, the existence and nature of space weathering were also discovered in 1993 by analyzing the Apollo samples. Another change occurred in 2010 when the Hayabusa spacecraft returned particles of asteroid 25143 Itokawa that proved the identity between many S-type asteroids and ordinary chondrites and the existence of space weathering similar to the Moon. The second sample return from asteroids occurred in 2020 when the Hayabusa2 spacecraft returned samples of C-type asteroid 162173 Ryugu. In spite of some expectations, it was a pristine CI1 chondrite material that was free from terrestrial contaminations suffered by known CI1 chondrite meteorites. The third sample return from asteroids was accomplished by NASA OSIRIS-REx mission, which recovered the greatest amount of samples from asteroid 101955 Bennu. These sample return missions have and will drastically change the accuracy of our knowledge on the composition and surface processes of small bodies and therefore asteroidal remote-sensing researchers will be asked for more accountability as happened to lunar remote sensing studies.

As the world becomes flatter, the stock market volatilities in one area tend to spread quickly to other regions. In this paper, we aim to examine the characteristics of daily return transmissions across six Gulf Cooperation Council countries (Kuwait, Saudi Arabia, UAE, Qatar, Oman, and Bahrain). For empirical methodology, we employ the generalized spillover definition and measurement proposed by Diebold, Yilmaz (2012). From a static full-sample analysis, we find that 41.4% of forecast error variance comes from return spillovers under time-invariant assumption. In line with existing literature on return volatility using a rolling-sample analysis, we find that the total return spillovers reveal time-varying characteristics during the global financial market turmoils such as the US credit crisis (2008-2009), the ongoing European debt crisis (2009-to date), and the recent plunging oil price (2015-present). These findings can help individual and institutional investors extend their investment horizon into the GCC stock markets and increase their global diversification benefits.

We compare the out-of-sample forecasting performance of volatility models using daily exchange rate for the KRW/USD during the period from 1992 to 2008. For various forecasting horizons, historical volatility models with a long memory tend to make more accurate forecasts. Especially, we carefully observe the difference between the EWMA and the GARCH(1,1) model. Our empirical finding that the GARCH model puts too much weight on recent observations relative to those in the past is consistent with prior evidence showing that asset market volatility has a long memory, such as Ding and Granger (1996). The forecasting model with the lowest MSFE and VaR forecast error among the models we consider is the EWMA model in which the forecast volatility for the coming period is a weighted average of recent squared return with exponentially declining weights. In terms of forecast accuracy, it clearly dominates the widely accepted GARCH and rolling window GARCH models. We also present a multiple comparison of the out-of-sample forecasting performance of volatility using the stationary bootstrap of Politis and Romano (1994). We find that the White's reality check for the GARCH(1,1) expanding window model and the FIGARCH(1,1) expanding window model clearly reject the null hypothesis and there exists a better model than the two benchmark models. On the other hand, when the EWMA model is the benchmark, the White's for all forecasting horizons are very high, which indicates the null hypothesis may not be rejected. The Hansen's report the same results. The GARCH(1,1) expanding window model and the FIGARCH(1,1) expanding window model are dominated by the best competing model in most of the forecasting horizons. In contrast, the RiskMetrics model seems to be the most preferred. We also consider combining the forecasts generated by averaging the six raw forecasts and a trimmed set of forecasts which calculate the mean of the four forecasts after disregarding the highest and lowest forecasts from the six models. This experiment confirms that the forecast combinations always outperform forecasts from a single model.

본 연구는 한국 주식시장에서 12개의 주요 거시경제변수 및 금융변수들을 이용하여 미래 장·단기 주식수익률을 예측할 수 있는지 실증적으로 검정하였다. 특히, 선행연구에서 많이 사용되었던 표본 내 예측가능성 검정뿐만 아니라 표본 외 예측가능성 검정을 함께 시행함으로써 각 예측변수들의 예측력을 보다 강건하게 검정하였다. 이를 위해 내포모형(Nested model)의 예측치를 검정할 수 있도록 McCracken (2007)이 제안한 MSE-F 검정기법과 Clark and McCracken (2001)이 제안한 ENC-NEW 검정기법을 사용하였다. 이와 함께, 부트스트랩을 통한 재표본추출(Resampling through bootstrapping)을 통해 임계치와 p-value를 산출함으로써 주식수익률 예측에서 일어나는 소표본 편차(Finite-sample bias)와 장기수익률 잔차항의 자기상관성(Autocorrelation) 문제를 해결하였다. 검정 결과, 주가순자산비율(Book-to-market ratio) 변수가 표본 내 검정과 표본 외 검정에서 모두 주식수익률 예측력을 갖고 있는 것으로 나타나 가장 일관성 있는 예측변수로 나타났다.

This study evaluates the predictive power of 12 financial and macroeconomic variables for Korean stock market returns of different horizons. Both the return predictability of in-sample and out-of-sample tests are considered to examine each variable’s predictive ability more robustly. For this purpose, this article employs the MSE-F statistic developed by McCracken (2007) and the ENC-NEW statistic developed by Clark and McCracken (2001) to compare nested forecast models. In addition, the bootstrapping procedure is applied for both in-sample and out-of-sample inferences to address the finite-sample bias and the autocorrelated disturbances from overlapping observations. As a result, the book-to-market ratio variable is found to be the most consistent and significant predictor as it rejects the null of no predictability for both in-sample and out-of-sample tests.