Sampling Techniques for Big Data Analysis

In analysing big data for finite population inference, it is critical to adjust for the selection bias in the big data. In this paper, we propose two methods of reducing the selection bias associated with the big data sample. The first method uses a version of inverse sampling by incorporating auxiliary information from external sources, and the second one borrows the idea of data integration by combining the big data sample with an independent probability sample. Two simulation studies show that the proposed methods are unbiased and have better coverage rates than their alternatives. In addition, the proposed methods are easy to implement in practice.This is a manuscript of an article published as J.K. Kim and Z. Wang (2019). "Sampling Techniques for Big Data Analysis," International Statistical Review, 87, S177-S191. doi: 10.1111/insr.12290. Posted with permission.</p

Time-Varying Volatility in the Foreign Exchange Market: New Evidence on its Persistence and on Currency Spillovers

We examine empirically the volatility of four major US dollar spot exchange rates using intraday data over 40 trading days. Using multivariate stochastic volatility models, we investigate the degree of persistence of exchange rate volatility for data sampled at different frequencies and the role of volatility spillovers across exchange rates. We find that the noise component of volatility 'aggregates out' very quickly, being dominated by the more persistent component of volatility for data sampled at 15-minute or lower frequencies. Our results also suggest that exchange rate volatility is very persistent and that cross-currency spillovers are small. Copyright Blackwell Publishers Ltd, 2004.

Pricing volatility of stock returns with volatile and persistent components

Risk return, In-mean effect, Volatility, Long memory, Innovations, C14, G12, G15,