论文检索

结合统计区间与市场价格：最坏情形状态价格分布AI 解读

The paper shows how to combine (historical) statistical data and (current) market prices to form conservative trading strategies for options. This gives rise to a “worst case” state price distribution, which provides sharp price bounds for all convex European options. The paper provides for existence and computational algorithms under conditions which can be understood as “no arbitrage”. The worst case distribution converges to a regular state price distribution if the…

扩散过程与Itô过程的方差分析AI 解读

Itô processes are the most common form of continuous semimartingales, and include diffusion processes. This paper is concerned with the nonparametric regression relationship between two such Itô processes. We are interested in the quadratic variation (integrated volatility) of the residual in this regression, over a unit of time (such as a day). A main conceptual finding is that this quadratic variation can be estimated almost as if the residual process were observed, the…

金融期权与统计预测区间AI 解读

The paper shows how to convert statistical prediction sets into worst case hedging strategies for derivative securities. The prediction sets can, in particular, be ones for volatilities and correlations of the underlying securities, and for interest rates. This permits a transfer of statistical conclusions into prices for options and similar financial instruments. A prime feature of our results is that one can construct the trading strategy as if the prediction set had a 100…

似然理论中的 Bartlett 恒等式与大偏差AI 解读

The connection between large and small deviation results for the signed square root statistic $R$ is studied, both for likelihoods and for likelihood-like criterion functions. We show that if $p - 1$ Barlett identities are satisfied to first order, but the $p$th identity is violated to this order, then $\operator{cum}_q(R) = O(n^{-q/2})$ for $3 \leq q < p$, whereas $\operator{cum}_p(R) = \kappa_p n^{-(p-2)/2} + O(n^{-p/2})$. We also show that the large deviation behavior of $R$…

鞅展开与二阶推断AI 解读

The paper develops a one-step triangular array Edgeworth expansion for multivariate martingales that are, essentially, asymptotically ergodic. Both discrete and continuous time are covered. The expansion is in a test function topology. We investigate when the expansion has the usual Edgeworth form, looking in particular at likelihood inference, including Cox regression, and at inference for stationary time series. The triangular array nature of the results make them useful…

对偶似然AI 解读

This paper introduces the concept of dual likelihood as a method of improving accuracy in inference situations depending on martingale estimating equations. Asymptotic results are given for the dual likelihood ratio statistic, and the structure of the family of alternatives is explored. Applications to survival analysis and also to time series, likelihood inference and independent observations are given. Connections to nonparametric likelihood (including empirical…

依赖变量分布的渐近展开与自助法：鞅方法AI 解读

The paper develops a one-step triangular array asymptotic expansion for continuous martingales which are asymptotically normal. Mixing conditions are not required, but the quadratic variations of the martingales must satisfy a law of large numbers and a central limit type condition. From this result we derive expansions for the distributions of estimators in asymptotically ergodic differential equation models, and also for the bootstrapping estimators of these distributions.