Nopporn Thamrongrat

• Walailak University

This paper derives a closed-form expansion for the conditional expectation of a continuous-time stochastic process, given by V t , T : = e - ∫ t T g ( v s ) d s f ( v T ) for 0 ≤ t ≤ T , where v t evolves according to the extended Cox-Ingersoll-Ross process, for any C ∞ functions f and g. We apply the Feynman-Kac theorem to state a Cauchy problem a...

Moment swaps are essentially forward contracts on realized higher moments of log-returns of a specified underlying asset, which play an important role in protection against different kinds of market shocks, and variance, skewness, and kurtosis swaps are examples of moment swaps currently traded in markets. To facilitate market practitioners, this w...

In this paper, we develop an iterative approach for obtaining a closed-form expansion for the conditional expectation of the valuation process, defined by (formula presented) for 0 ≤ t ≤ T, where vt is assumed to follow the extended Cox-Ingersoll-Ross process, for any smooth real-valued functions f; g, and h. The novel analytical approach presented...

Increased visceral fat is associated with an increased mortality rate. Bioelectrical impedance analysis (BIA) is a noninvasive method to assess visceral fat that is easily accessible and avoids exposure to radiation. It is unknown how a visceral fat rating from a noninvasive machine correlates with the serum lipid profile and fasting blood sugar (F...

In this paper, we show how to estimate the asymptotic (conditional) covariance matrix, which appears in central limit theorems in high-frequency estimation of asset return volatility. We provide a recipe for the estimation of this matrix by subsampling; an approach that computes rescaled copies of the original statistic based on local stretches of...

In this paper we present a weak limit theorem for a numerical approximation of Brownian semi-stationary processes studied in [14]. In the original work of [14] the authors propose to use Fourier transformation to embed a given one dimensional (Lévy) Brownian semi-stationary process into a two-parameter stochastic field. For the latter they use a si...

Stable convergence is a type of convergence of random variables, which is stronger than weak convergence but weaker than convergence in probability. It has been used in asymptotic theory of statistics and probability since Renyi originated his work (cf. [91]) in 1963. In this thesis, we study applications of stable convergence in two aspects. First...

In this paper, we show how to estimate the asymptotic (conditional) covariance matrix, which appears in many central limit theorems in high-frequency estimation of asset return volatility. We provide a recipe for the estimation of this matrix by subsampling, an approach that computes rescaled copies of the original statistic based on local stretche...