1,721,162 research outputs found
Forecasting the KOSPI200 spot volatility using various volatility measures
No full textThis study examines the volatility forecasting performance of various historical and implied volatility measures. We compare the informational efficiency of lagged realized volatility, GARCH-family volatilities, out-of-the-money (OTM) and at-the-money (ATM) implied volatilities, and the market volatility index (VKOSPI) using univariate and encompassing regression analyses. We find that historical and implied volatility both have good predictive ability, but are biased estimators of future volatility. Furthermore, the information content of the implied volatility constructed from slightly OTM options encompasses that of the deep OTM and ATM options. In general, the VKOSPI exhibits the best forecasting performance among the volatility measures analyzed in this study. However, incorporating GJRGARCH volatility, which exhibits the best performance among the GARCH-family volatilities, in the prediction model possibly improves the explanatory power of the VKOSPI. (C) 2018 Published by Elsevier B.V.
Discovering the drivers of stock market volatility in a data-rich world
No full textThis study comprehensively examines the economic and financial drivers of volatility changes in terms of a cross-country perspective. We review a wide range of studies related to financial volatility forecasting and collect a diverse set of prediction variables. By analyzing them within a unified framework, we find that only a small number of variables contain significant predictive information. Most of all, we discover that among various global market indicators, Chinese stock market movements significantly predict U.S. stock market volatility. Further analyses provide evidence of the effect of Chinese stock market movements on the U.S. stock market.
Measuring corporate failure risk: Does long short-term memory perform better in all markets?
No full textRecently, various corporate failure prediction models that use machine learning techniques have received considerable attention. In particular, using a sequence of a company's historical information, rather than just the most recent information, yields better predictive performance by adopting recurrent neural networks (RNNs) and long short-term memory (LSTM) algorithms in the United States market. Similarly, we evaluate whether these results hold in emerging market contexts using listed companies in Korea. We also compare the logistic regression, random forest, RNN, LSTM, and an ensemble model combining these four techniques. The random forest model with recent information outperforms the other models, indicating that corporate failure prediction models for immature markets, unlike those for developed markets, might have to focus more on recent information rather than on the historical sequence of corporate performance.
Corporate Bankruptcy Prediction Using Machine Learning Methodologies with a Focus on Sequential Data
No full textWe examine whether corporate bankruptcy predictions can be improved by utilizing the recurrent neural network (RNN) and long short-term memory (LSTM) algorithms, which can process sequential data. Employing the RNN and LSTM methodologies improves bankruptcy prediction performance relative to using other classification techniques, such as logistic regression, support vector machine, and random forest methods. Because performance indicators, such as sensitivity and specificity, differ depending on the methodology, selecting a model that suits the purpose of the bankruptcy predictions is necessary. Our ensemble model, a synthesis of all methodologies, exhibits the best forecasting performance. In the test sample for the ensemble model, none of the observations with a default probability of less than 10% defaults within one year.
Seemingly Irrational but Predictable Price Formation in Seoul's Housing Market
No full textThis paper tests the dynamics implied by a supplied-constrained view of the relationship between market fundamentals and house prices in the case of Seoul’s condominium market. The view is that supply constraints have led to serious shortages in certain submarkets, and that these shortages have led to a rapid rise in house prices and to panic buying or inflation-induced investing and to further price increases. The estimation period of the test is November 1988–February 27. The results suggest that house prices in Seoul are highly persistent because of these supply constraints. Additionally, we do what we can with the available data to determine if house price increases serve to increase demand further, and if rent-price ratios and nominal interest rates are a good predictor of how housing prices in Seoul will evolve over time
Are Commercial Mortgage Default Affected by Tax Considerations?
No full textWe study whether tax considerations are an ｉｍｐｏｒｔant determinant of commercial mortgage default. We also study whether large lenders are better informed, ｏｒ better at interpreting information for lending purposes, and hence have lower foreclosure rates; whether lenders have more information on larger borrowers than smaller borrowers, and hence have lower foreclosure rates on larger loans; and whether commercial mortgage defaults are related to debt service coverage and loan-to-values, both initial and contemporaneous. The paper’s main findings are fourfold. First, there is evidence that tax considerations influence investors’ decisions about when to “put” assets to lenders. The results are consistent with the argument of Constantinides (1984). Second, the evidence suggests that large lenders are especially knowledgeable about commercial mortgage borrowers and commercial property markets, in that they have lower foreclosure rates than smaller lenders. Third, on the question of whether lenders have more information on larger borrowers than smaller borrowers, we find that larger loans have, on average, lower default rates than smaller loans. Fourth, the findings suggest that lower default rates are associated with higher debt service coverage ratios, both initial and contemporaneous. We find an insignificant relation between contemporaneous loan-to-value and default
metastability of deep levels
No full text학위논문(박사) - 한국과학기술원 : 물리학과, 1991.8, [ v, 114, 5 p. ]The properties of hydrogen in GaAs material has been investigated. The various states of hydrogen are detailed, together with incorporation of hydrogn atom with shallow and deep levels defects that passivate their electrical activity. The passivation and dissociation process of the hydrogen-Si donor complex in plasma-hydrogenated GaAs was presented. Also, it is demonstrated that atomic hydrogen drifts as a negatevely charged state in n-type GaAs and the high-electric field strongly affects the dissociation of the hydrogen-donor complex. During reversebias anneal experiments on the Schottky diode, it is confirmed that a negatively charged hydrogen is accelerated out of the high-field region and that there is a dissociation frequency region independet of the anneal temperature. In the dissociation frequency region dependent on the anneal temperature, the first-order kinetics gives rise to the dissociation energy for the release of the hydrogen-Si donor complex. The dissoiction energies are dependent on the applied bias voltage and are in the ranges of 1.79 to 1.1 eV. New metastable behavior of deep level defects is found in hydrogenated GaAs doped with Si. A deep level defect at 0.60 eV below the conduction band minimum() is generated during hydrogenation and shows metastable behavior for the -0.42eV trap. Thermal annealing experiments under the biased and the unbiased conditions confirm that during hydrogcnation the 0.60eV trap is generated, as a metastable defect for the native defect at 0.42 eV, and the complete passivation of the 0.42 or the 0.33 eV trap during hydrogenation is due to passivation of the trap by hydrogen atom, forming hydrogen-defect complex. The first order kinetics permits a precise estimate of the formation and annealing frequencies and of the hydrogen-defect pair. The temperature dependent values of for the 0.60 eV trap satisfy the relation exp((-1.610.04 eV)/kT) S-1. It...한국과학기술원 : 물리학과
Hedge fund market runs during financial crises
Full text linkHedge funds exit financial markets simultaneously after enormous
shocks, such as the global financial crisis. While previous studies
highlight only fund investors’ synchronized withdrawals as the
major driver of massive asset liquidations, we primarily focus on
informed and rational fund managers and suggest a theoretical
model that illustrates fund managers’ synchronized market runs.
This study shows that the possibility of runs induces panic-based
market runs not because of systematic risk itself but because of
the fear of runs. We find that when the market regime changes
from a normal to a ‘bad’ state in which runs are possible, hedge
funds reduce their investments prior to runs. In addition, market
runs are more likely to occur in markets in which hedge funds
have greater market exposure and uninformed traders are more
sensitive to past price movement
Do Higher Land Values Cause Higher House Prices,or Vice Versa?
No full textThis paper is a study of the dynamic relationship between residential land values and house prices. Little agreement exists regarding the direction of causality between house prices and residential land values. One could argue that causality is unidirectional, running from house prices to residential land values, but not vice versa. One could also argue that causality could go the other way, from high residential land values to high house prices. Still further one could argue that causality goes both ways. To examine which of these hypotheses is most likely, tests of Granger causality are applied. The tests are applied to US data from 1985-2004 for 27 MSAs. The data strongly support the view that the causality between residential land values and house prices is bidirectional. Our findings also indicate that the causality from house prices to residential land values increases in proportion to land use regulations. Finally, we find that our results are robust to employing the level of house prices to take account of the option to develop in the future
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