Determinants of Put-Call Disparity: Kospi 200 Index Options

Kim, Seongseop; Lockwood, Jimmy; Lockwood, Larry J.; Miao, Hong

doi:10.1080/15427560.2021.1974442

Cited by 1 publication

(3 citation statements)

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“…We build a day-ahead forecasting model using four loss functions-MAE, MSE, HMAE, and HMSE-and a 22-day window and explanatory variables as input data. 8), and 0.41203386 (8), respectively. Furthermore, we discover that the Ann model successfully picks up the features required to forecast volatility from the input GARCH-type model parameters.…”

Section: Results Of Out-of-sample Forecastsmentioning

confidence: 98%

“…The market capitalization approach is used in index computation. In 1983, KOSPI took the place of the Dow-inspired KCSPI (Korea Composite Stock Price Index) [8]. The SPX index, which combines the Dow Jones Industrial Average and the S&P 500, is a composite index based on the American stock market.…”

Section: Literature Reviewmentioning

confidence: 99%

“…However, the prediction effect of combining two GARCH models (GW-LSTM) is better than that of GEW-LSTM, and the prediction effect of combining one GARCH model (EWMA) is better than that of combining two GARCH models (EW-LSTM and GE-LSTM). As long as the GARCH model is mixed, its effect is better than the LSTM model 0.42311022 (8) 0.4271541 (8) 0.44767743 (8) 0.41203386 (8). The reason why the experimental results do not fully reach the expectation is that, in addition to the overlap of some parameter features between the models and the similar effect of two or more GARCH mixed models, it is also possible that some types of features of the training data are more significant, thus improving the performance of the model in prediction.…”

Section: Error Comparison Of Different Modelsmentioning

confidence: 99%

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The performance and error analysis of LSTM model combined with various GARCH models in stock forecasting

Chen¹,

Yang²,

Zhu³

et al. 2023

Second International Conference on Green Communication, Network, and Internet of Things (CNIoT 2022)

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Volatility is a measure of the asset return rate's estimated level of uncertainty and may be used to assess the riskiness of financial assets. We use the market capitalization of 200 stocks representing Korea as the primary analytical aim and evaluate the accuracy between them by analyzing the impacts of different hybrid models' hybrid neural networks, which are based on the returns of the KOSPI 200 stock index. By measuring the effectiveness of these models using four dissimilarity measures, we contrasted the performance of hybrid models that combine a single neural network and a single GARCH type model with that of hybrid neural networks that combine multiple GARCH models (MAE, MSE, HMAE, and HMSE). They are applied to anticipate the KOSPI 200 index data's actual volatility. Among these, hybrid neural networks that integrate more than one GARCH-type model have much better forecasting performance than neural network models that mix two or more or more GARCH-type models. GW-LSTM makes the least accurate forecast. We note that the hybrid model combining the three GARCH models shows a minor increase in predicting ability based on merging two and three models.

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Section: Results Of Out-of-sample Forecastsmentioning

confidence: 98%

Section: Literature Reviewmentioning

confidence: 99%

Section: Error Comparison Of Different Modelsmentioning

confidence: 99%

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