
Summary: We propose a nonparametric estimation procedure for continuous-time stochastic models. Because prices of derivative securities depend crucially on the form of the instantaneous volatility of the underlying process, we leave the volatility function unrestricted and estimate it nonparametrically. Only discrete data are used but the estimation procedure still does not rely on replacing the continuous-time model by some discrete approximation. Instead the drift and volatility functions are forced to match the densities of the process. We estimate the stochastic differential equation followed by the short-term interest rate and compute nonparametric prices for bonds and bond options.
Applications of statistics to actuarial sciences and financial mathematics, derivative securities, discrete-time sampling, drift, Markov processes: estimation; hidden Markov models, continuous-time stochastic models, short-term interest rate, Density estimation, Derivative securities (option pricing, hedging, etc.), estimation of stochastic differential equations, kernel estimation, term structure of interest rates, bond options, option pricing, volatility function
Applications of statistics to actuarial sciences and financial mathematics, derivative securities, discrete-time sampling, drift, Markov processes: estimation; hidden Markov models, continuous-time stochastic models, short-term interest rate, Density estimation, Derivative securities (option pricing, hedging, etc.), estimation of stochastic differential equations, kernel estimation, term structure of interest rates, bond options, option pricing, volatility function
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