Power Variations and Testing for Co‐Jumps: The Small Noise Approach

In this paper, we study the effects of noise on bipower variation, realized volatility (RV) and testing for co‐jumps in high‐frequency data under the small noise framework. We first establish asymptotic properties of bipower variation in this framework. In the presence of the small noise, RV is asymptotically biased, and the additional asymptotic conditional variance term appears in its limit distribution. We also propose consistent estimators for the asymptotic variances of RV. Second, we derive the asymptotic distribution of the test statistic proposed in (Ann. Stat. 37, 1792‐1838) under the presence of small noise for testing the presence of co‐jumps in a two‐dimensional Itô semimartingale. In contrast to the setting in (Ann. Stat. 37, 1792‐1838), we show that the additional asymptotic variance terms appear and propose consistent estimators for the asymptotic variances in order to make the test feasible. Simulation experiments show that our asymptotic results give reasonable approximations in the finite sample cases.     

Small sample asymptotic inference for the coefficient of variation: normal and nonnormal models

In applied statistics, the coefficient of variation is widely calculated and interpreted even when the sample size of the data set is very small. However, confidence intervals for the coefficient of variation are rarely reported. One of the reasons is the exact confidence interval for the coefficient of variation, which is given in Lehmann (Testing Statistical Hypotheses, 2nd Edition, Wiley, New York, 1996), is very difficult to calculate. Various asymptotic methods have been proposed in literature. These methods, in general, require the sample size to be large. In this article, we will apply a recently developed small sample asymptotic method to obtain approximate confidence intervals for the coefficient of variation for both normal and nonnormal models. These small sample asymptotic methods are very accurate even for very small sample size. Numerical examples are given to illustrate the accuracy of the proposed method.     

How precise is the finite sample approximation of the asymptotic distribution of realised variation measures in the presence of jumps?

This paper studies the impact of jumps on volatility estimation and inference based on various realised variation measures such as realised variance, realised multipower variation and truncated realised multipower variation. We review the asymptotic theory of those realised variation measures and present a new estimator for the asymptotic ‘variance’ of the centered realised variance in the presence of jumps. Next, we compare the finite sample performance of the various estimators by means of detailed Monte Carlo studies. Here we study the impact of the jump activity, of the jump size of the jumps in the price and of the presence of additional independent or dependent jumps in the volatility. We find that the finite sample performance of realised variance and, in particular, of log-transformed realised variance is generally good, whereas the jump-robust statistics tend to struggle in the presence of a highly active jump process.     

Uniformly asymptotic behavior for the tail probability of discounted aggregate claims in the time-dependent risk model with upper tail asymptotically independent claims

ABSTRACT

In this paper, we consider the tail behavior of discounted aggregate claims in a dependent risk model with constant interest force, in which the claim sizes are of upper tail asymptotic independence structure, and the claim size and its corresponding inter-claim time satisfy a certain dependence structure described by a conditional tail probability of the claim size given the inter-claim time before the claim occurs. For the case that the claim size distribution belongs to the intersection of long-tailed distribution class and dominant variation class, we obtain an asymptotic formula, which holds uniformly for all times in a finite interval. Moreover, we prove that if the claim size distribution belongs to the consistent variation class, the formula holds uniformly for all times in an infinite interval.     

Asymptotic ruin probabilities for proportional investment under interest force with dominatedly-varying-tailed claims

We study the asymptotic behavior of the ruin probabilities in the renewal risk model, in which the insurance company is allowed to invest a constant fraction of its wealth in a stock market which is described by a geometric Brownian motion and the remaining wealth in a bond with nonnegative interest force. We give the expression of the wealth process by the Itô formula, and finally we derive the asymptotic behavior of finite-time and infinite-time ruin probabilities in the presence of pairwise quasi-asymptotically independent claims with dominant varying tails for this model. In the particular case of compound Poisson model, explicit asymptotic expressions for the ruin probabilities are given with tails of regular variation, where the relation of the infinite-time ruin probability is the same as Gaier and Grandits (2004). For this case, we give some numerical results to assess the qualities of the asymptotic relations.     

Variations and estimators for self-similarity parameter of sub-fractional Brownian motion via Malliavin calculus

Using multiple stochastic integrals and the Malliavin calculus, we analyze the asymptotic behavior of the adjusted quadratic variation for a sub-fractional Brownian motion. We apply our results to construct strongly consistent statistical estimators for the self-similarity of sub-fractional Brownian motion.     

A note on a by-claim risk model: Asymptotic results

In this note, we restudy a by-claim risk model with general dependence structures between each main claim and its by-claim. Within the framework of regular variation, we derive some asymptotic expansions for the infinite-time and finite-time ruin probabilities.     

Multipower variation from generalized difference for fractional integral processes with jumps

This article presents limit theorems of the multipower variation based on a generalized difference for the fractional integral process with jumps observed in high frequency. In particular, we obtain the large number laws for threshold multipower variation and multipower variation and the associated central limit theorems. The limit theorems are applied to estimate Hurst parameter, and the consistence and asymptotic distribution of the estimator are established. These results will provide some new statistical tools to analyze long-memory effect in high-frequency situation.     

Estimate for the Finite-time Ruin Probability in the Discrete-time Risk Model with Insurance and Financial Risks

In this article, we consider a discrete-time risk model with insurance and financial risks. We derive some refinements of a general asymptotic formula for the finite-time ruin probability under the assumptions that the net losses follow a common distribution in the intersection between the subexponential class and the Gumbel maximum domain of attraction, and the stochastic discount factors of the risky asset have a common distribution with extended regular variation. The obtained asymptotic upper and lower bounds are transparent and computable.     

An asymptotic representation of a ratio of two statistics and its applications

Some statistics in common use take a form of a ratio of two statistics.In this paper, we will discuss asymptotic properties of the ratio statistic.We obtain an asymptotic representation of the ratio with remainder term o _p(n ^-1) and a Edgeworth expansion with remainder term o(n ^-1/2) And as example, the asymptotic representation and the Edgeworth expansion of the jackknife skewness estimator for U-statistics are established and we discuss the biases of the skewness estimator theoretically.We also apply the result to an estimator of Pearson’s coefficient of variation and the sample correlation coefficient.     

On Estimating the Integrated Co-Volatility Using Noisy High-Frequency Data with Jumps

In this article, we consider the estimation of covariation of two asset prices which contain jumps and microstructure noise, based on high-frequency data. We propose a realized covariance estimator, which combines pre-averaging method to remove the microstructure noise and the threshold method to reduce the jumps effect. The asymptotic properties, such as consistency and asymptotic normality, are investigated. The estimator allows very general structure of jumps, for example, infinity activity or even infinity variation. Simulation is also included to illustrate the performance of the proposed procedure.     

Precise large deviations for sums of WUOD and φ-mixing random variables with dominated variation

In this paper, we investigate the precise large deviations for sums of not identical distributed random variables which are widely upper orthant dependent (WUOD, in short) and φ-mixing. The asymptotic relations for sums of random variables with dominated variation are obtained.     

Inferences on the Coefficients of Variation in a Multivariate Normal Population

In this article, the problem of testing the equality of coefficients of variation in a multivariate normal population is considered, and an asymptotic approach and a generalized p-value approach based on the concepts of generalized test variable are proposed. Monte Carlo simulation studies show that the proposed generalized p-value test has good empirical sizes, and it is better than the asymptotic approach. In addition, the problem of hypothesis testing and confidence interval for the common coefficient variation of a multivariate normal population are considered, and a generalized p-value and a generalized confidence interval are proposed. Using Monte Carlo simulation, we find that the coverage probabilities and expected lengths of this generalized confidence interval are satisfactory, and the empirical sizes of the generalized p-value are close to nominal level. We illustrate our approaches using a real data.     

Comparison of two measures of association in two-way contingency tables

Stuart's (1953) measure of association in contingency tables, t_C, based on Kendall's (1962) t, is compared with Goodman and Kruskal's (1954, 1959, 1963, 1972) measure G. First, it is proved that |G| ≥ |t_C|; and then it is shown that the upper bound for the asymptotic variance of G is not necessarily always smaller than the upper bound for the asymptotic variance of t_C. It is proved, however, that the upper bound for the coefficient of variation of G cannot be larger in absolute value than the upper bound for the coefficient of variation of t_C. The asymptotic variance of t_C is also derived and hence we obtain an upper bound for this asymptotic variance which is sharper than Stuart's (1953) upper bound.     

Asymptotic analysis of tail distortion risk measure under the framework of multivariate regular variation

Abstract

Under the framework of multivariate regular variation, we obtain the asymptotic ratio between the tail distortion risk measure for portfolio loss and the sum of value-at-risk for single loss by a different method from the one in Zhu and Li when the confidence level tends to one. In order to illustrate the derived result, a relevant example is given and the corresponding numerical simulation is also carried out.     

Markov Systems in a General State Space

In this article, we introduce and study Markov systems on general spaces (MSGS) as a first step of an entire theory on the subject. Also, all the concepts and basic results needed for this scope are given and analyzed. This could be thought of as an extension of the theory of a non homogeneous Markov system (NHMS) and that of a non homogeneous semi-Markov system on countable spaces, which has realized an interesting growth in the last thirty years. In addition, we study the asymptotic behaviour or ergodicity of Markov systems on general state spaces. The problem of asymptotic behaviour of Markov chains has been central for finite or countable spaces since the foundation of the subject. It has also been basic in the theory of NHMS and NHSMS. Two basic theorems are provided in answering the important problem of the asymptotic distribution of the population of the memberships of a Markov system that lives in the general space (X, ?(X)). Finally, we study the total variability from the invariant measure of the Markov system given that there exists an asymptotic behaviour. We prove a theorem which states that the total variation is finite. This problem is known also as the coupling problem.     

Influence diagnostics on the coefficient of variation of elliptically contoured distributions

In this article, we study the behavior of the coefficient of variation (CV) of a random variable that follows a symmetric distribution in the real line. Specifically, we estimate this coefficient using the maximum-likelihood (ML) method. In addition, we provide asymptotic inference for this parameter, which allows us to contrast hypothesis and construct confidence intervals. Furthermore, we produce influence diagnostics to evaluate the sensitivity of the ML estimate of this coefficient when atypical data are present. Moreover, we illustrate the obtained results by using financial real data. Finally, we carry out a simulation study to detect the potential influence of atypical observations on the ML estimator of the CV of a symmetric distribution. The illustration and simulation demonstrate the robustness of the ML estimation of this coefficient.     

Nonparametric estimation of a log-variance function in scale-space

In a nonparametric regression setting, we consider the kernel estimation of the logarithm of the error variance function, which might be assumed to be homogeneous or heterogeneous. The objective of the present study is to discover important features in the variation of the data at multiple locations and scales based on a nonparametric kernel smoothing technique. Traditional kernel approaches estimate the function by selecting an optimal bandwidth, but it often turns out to be unsatisfying in practice. In this paper, we develop a SiZer (SIgnificant ZERo crossings of derivatives) tool based on a scale-space approach that provides a more flexible way of finding meaningful features in the variation. The proposed approach utilizes local polynomial estimators of a log-variance function using a wide range of bandwidths. We derive the theoretical quantile of confidence intervals in SiZer inference and also study the asymptotic properties of the proposed approach in scale-space. A numerical study via simulated and real examples demonstrates the usefulness of the proposed SiZer tool.     

Improvements in the Poisson approximation of mixed Poisson distributions

We consider the approximation of mixed Poisson distributions by Poisson laws and also by related finite signed measures of higher order. Upper bounds and asymptotic relations are given for several distances. Even in the case of the Poisson approximation with respect to the total variation distance, our bounds have better order than those given in the literature. In particular, our results hold under weaker moment conditions for the mixing random variable. As an example, we consider the approximation of the negative binomial distribution, which enables us to prove the sharpness of a constant in the upper bound of the total variation distance. The main tool is an integral formula for the difference of the counting densities of a Poisson distribution and a related finite signed measure.     

Semi-parametric tail inference through probability-weighted moments

In this paper, for heavy-tailed models, and working with the sample of the k largest observations, we present probability weighted moments (PWM) estimators for the first order tail parameters. Under regular variation conditions on the right-tail of the underlying distribution function F we prove the consistency and asymptotic normality of these estimators. Their performance, for finite sample sizes, is illustrated through a small-scale Monte Carlo simulation.