We study nonparametric estimation for the partially conditional average treatment effect, defined as effect function over an interested subset of confounders. propose a double kernel weighting estimator where weights aim to control balancing error any confounders from reproducing Hilbert space after smoothing variables. In addition, we present augmented version our which can incorporate outcome...

We assess the predictive accuracy of a large number of multivariate volatility models in terms of pricing options on the Dow Jones Industrial Average. We measure the value of model sophistication in terms of dollar losses by considering a set 248 multivariate models that differ in their specification of the conditional variance, conditional correlation, and innovation distribution. All models b...

For observational studies, we study the sensitivity of causal inference when treatment assignments may depend on unobserved confounders. We develop a loss minimization approach for estimating bounds conditional average effect (CATE) confounders have bounded odds ratio selection. Our is scalable and allows flexible use model classes in estimation, including nonparametric black-box machine learni...

We study the correlation of monthly excess returns for seven major countries over the period 1960-90. We find that the international covariance and correlation matrices are unstable over time. A multivariate GARCH(1,1) model with constant conditional correlation helps to capture some of the evolution in the conditional covariance structure. However tests of specific deviations lead to a rejecti...

Motivated by the security of the nonlinear filter generator, the concept of correlation was previously extended to the conditional correlation, that studied the linear correlation of the inputs conditioned on a given (short) output pattern of some specific nonlinear function. Based on the conditional correlations, conditional correlation attacks were shown to be successful and efficient against...

[1] In this paper we present a data-derived model of relativistic electron flux at geosynchronous orbit. The model is driven by multiple solar wind and magnetospheric inputs and combines the deterministic approach of nonlinear dynamics with conditional probability consideration. The model is used for one-day predictions of daily flux maxima for the years 1995–2000. The deterministic part of the...

This paper employs a multivariate dynamic conditional correlation GARCH model, which is developed by Engle (2001, 2002), to detect the timing and nature of changes in the comovement between Iranian output and prices for the periods after Iran–Iraq war , known as imposed war . The results showed that there is a weak correlation between output and prices after imposed war and  varies periodically...

Recent studies in international finance have shown that correlation of international equity returns increases during volatile periods. However, correlation should be used with great care. For example, assuming a multivariate normal distribution with constant correlation, conditional correlation during volatile periods (large absolute returns) is higher than conditional correlation during tranqu...