Name
Title | ||
|---|---|---|
Prediction of pricing and hedging errors for equity linked warrants with Gaussian process models |
Abstract | ||
|---|---|---|
Gaussian process (GP) model is a Bayesian kernel-based learning machine. In this paper, we propose a GP model with a various mixed kernel for pricing and hedging ELWs (equity linked warrants) traded at KRX with predictive distribution. We experiment with daily market data relevant to KOSPI200 call ELWs from March 2006 to July 2006, comparing the performance of the GP model with those of various neural network (NN) models to show its effectiveness. The applied NN models contain early stopping, regularized NN, and bagging. The proposed GP model shows that its forecast capability outperforms those of the three NN models in terms of both pricing and hedging errors, thereby generating consistent results. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1016/j.eswa.2007.07.041 | Expert Syst. Appl. |
Keywords | Field | DocType |
nn model,neural networks,regularized nn,consistent result,kospi200 call elws,gaussian process model,proposed gp model,daily market data,hedging,gp model,derivatives,various mixed kernel,gaussian process,equity linked warrants,gaussian processes,various neural network,predictive distribution,neural network | Econometrics,Kernel (linear algebra),Early stopping,Computer science,Hedge (finance),Artificial intelligence,Equity (finance),Gaussian process,Market data,Artificial neural network,Machine learning,Bayesian probability | Journal |
Volume | Issue | ISSN |
35 | 1-2 | Expert Systems With Applications |
Citations | PageRank | References |
8 | 0.57 | 9 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gyu-Sik Han | 1 | 18 | 2.55 |
| Jaewook Lee | 2 | 735 | 50.24 |