Research On Real Option Pricing Under Incomplete Information And The Method Of Fluctuation Rate

The theory of real option plays an important role in the field of risk investment. The primarystudy of real option is the applications in the investment field except of the financial market.There has been a good application prospect for the theory of real option in every element ofbusiness, which includes the investment decision, product pricing marketing, after-salesservice enterprises and so on. However, collecting the whole market information is achallenging work for the enterprise in the reality market. Therefore, it is very important tostudy this theory under incomplete information.In this paper, we study the problem of real option pricing and the method of volatilityforecast based on incomplete information. The paper are divided into two parts as follows:In the first section, the incomplete information is measured with the observed cash flowgenerated from the process of investment. Using Ito’s lemma, we derive the partial differentialequations of real options price. Based on the practical background of the numerical solution,the real options value is calculated and analyzed. The results show that the higher the degreeof information identified is, the larger the option value owns.Based on the results obtained above, the prediction of financial volatility is given in thesecond section. Firstly, we test on the correlation and stationary for the data. Secondly, weestimate the corresponding GARCH model and check whether the model is significant. Withthe implied volatility model, the selected data is analyzed. Finally, taking the prediction of thewarrant price volatility as an example, we compare the GARCH model with the impliedvolatility model in different aspects, such as the root mean square error (RMSE), meanabsolute error (MAE) and mean absolute percentage error (MAPE). The results show that,GARCH model may be better in predicting the change of volatility in short term (i.e. oneweek), while the implied volatility model is more accurate in the long term (i.e. one month).The conclusion of this paper verifies the market microstructure theory-market keepsgenerating new information, which combines with market prices leading to the change of pricevolatility.