Jin-Huei Yeh

Random Aggregation with Applications in High-Frequency Finance

In this paper we consider properties of random aggregation in time series analysis. For application, we focus on the problem of estimating the high-frequency beta of an asset return when the returns are subject to the effects of market microstructure. Specifically, we study the correlation between intraday log returns of two assets. Our investigation starts with the effect of non‐synchronous trading on intraday log returns when the underlying return series follows a stationary time series model. This is a random aggregation problem in time series analysis. We also study the effect of non‐synchronous trading on the covariance of two asset returns. To overcome the impact of non‐synchronous trading, we use Markov chain Monte Carlo methods to recover the underlying log return series based on the observed intraday data. We then define a high‐frequency beta based on the recovered log return series and propose an efficient method to estimate the measure. We apply the proposed analysis to many mid‐ or small‐cap stocks using the Trade and Quote Data of the New York Stock Exchange, and discuss implications of the results obtained. Copyright (C) 2010 John Wiley & Sons, Ltd.

Bias-corrected realized variance

ABSTRACT We propose a novel “bias-corrected realized variance” (BCRV) estimator based upon the appropriate re-weighting of two realized variances calculated at different sampling frequencies. Our bias-correction methodology is found to be extremely accurate, with the finite sample variance being significantly minimized. In our Monte Carlo experiments and a finite sample MSE comparison of alternative estimators, the performance of our straightforward BCRV estimator is shown to be comparable to other widely-used integrated variance estimators. Given its simplicity, our BCRV estimator is likely to appeal to researchers and practitioners alike for the estimation of integrated variance.

A noise-robust estimator of volatility based on interquantile ranges

This paper proposes a new class of estimators based on the interquantile range of intraday returns, referred to as interquantile range based volatility (IQRBV), to estimate the integrated daily volatility. More importantly and intuitively, it is shown that a properly chosen IQRBV is jump-free for its trimming of the intraday extreme two tails that utilize the range between symmetric quantiles. We exploit its approximation optimality by examining a general class of distributions from the Pearson type IV family and recommend using IQRBV.04 as the integrated variance estimate. Both our simulation and the empirical results highlight interesting features of the easy-to-implement and model-free IQRBV over the other competing estimators that are seen in the literature.

Loss Aversion in Real Estate Transactions

This research examines loss aversion within the real estate market by evaluating evidence and a priori arguments on the effects of investor sentiment on willingness-to-accept among homeowners. Based upon a unique data set providing the complete histories of transactions in the real estate market, our findings reveal that the loss aversion phenomenon prevails regardless of homeowners’ gender. Interestingly, young investors, and non-owner occupied investors faced with lower-priced cases exhibit higher levels of loss aversion. By sketching the whole conditional distribution via quantile regression, this study provides new evidence suggesting difference on cognitive biases across investors’ residential region, gender, and age. Key words: Loss aversion, investor experience, quantile regression, real estate markets

A New Approach for Identification and Characterization of Price Jumps

This paper proposes a newly-developed jump test based on the order statistics from intraday returns to identify the direction and magnitude of jumps. Our identification strategy, as suggested in the recent high-frequency finance, hinges on the local Gaussianity of the intraday return distribution in absence of jumps. Our test allows for an operational threshold in examining and characterizing a potential spectrum of jump sizes with signs. Besides its better statistical size and power properties, our numerical results demonstrate its robustness to the threshold, market microstructure noises, and the underlying stochastic volatility; our empirical evidence delivers several interesting and meaningful points. First, the numbers of identified positive and negative jumps are significantly fdfi erent in their intensities and sizes, respectively. We find that jumps come in clusters and such clustering patterns can be linked to forward looking variables such as VIX. Moreover, by varying the operational threshold, our jump test allows us to sketch a rough picture understanding various features of finite-activity jumps. Finally, we observe asymmetries in the intensities as well as the jump-contributed price variations between the positive and negative ones. Given these observations, we expect this jump test to be potentially useful in investments or risk management.