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<p>In recent decades, financial market data has become available with increasingly
higher frequency and higher dimension. This rapidly growing amount of financial data
has created many research opportunities and challenges. In this dissertation, I address
several important issues in the areas of asset pricing, financial econometrics, and
computational statistics using large-scale financial data techniques. In terms of
asset pricing (Chapter 2), I investigate the relationship between the cross-section
of expected stock returns and the associated market risks. In terms of financial econometrics
(Chapter 3), I uncover the sources of extreme dependence risks between assets. In
terms of computational statistics (Chapter 4), I design novel algorithms for efficiently
estimating large-scale covariance matrices.</p><p>In Chapter 2, using a large novel
high-frequency dataset, I investigate how individual stock returns respond to two
different market changes: continuous and discontinuous (jump) movements. I also explore
whether the different systematic risks associated with those two distinct movements
are priced in the cross-section of expected stock returns. I show that the cross-section
of expected stock returns reflects a risk premium for the systematic discontinuous
risk but not for the systematic continuous risk. An investment strategy that goes
long stocks in the highest discontinuous beta decile and shorts stocks in the lowest
discontinuous beta decile produces average excess returns of 17% per annum. I estimate
the risk premium for the systematic discontinuous risk is approximately 3% per annum
after controlling for the usual firm characteristic variables including size, book-to-market
ratio, momentum, idiosyncratic volatility, coskewness, cokurtosis, realized-skewness,
realized-kurtosis, maximum daily return, and illiquidity.</p><p>In Chapter 3, co-authored
with Professor Tim Bollerslev and Professor Viktor Todorov, we provide a new framework
for estimating the systematic and idiosyncratic jump tail risks in the financial asset
prices. Our estimates are based on in-fill asymptotics for directly identifying the
jumps, together with Extreme Value Theory (EVT) approximations and methods-of-moments
for assessing the tail decay parameters and the tail dependencies. On implementing
the aforementioned procedures with a panel of intraday prices for a large cross-section
of individual stocks and the S&P 500 market portfolio, we find that the distributions
of the systematic and idiosyncratic jumps are both generally heavy-tailed and close
to symmetric. We also show that the jump tail dependencies deduced from the high-frequency
data together with the day-to-day variation in the diffusive volatility account for
the "extreme" joint dependencies observed at the daily level.</p><p>When it comes
to estimating large covariance matrices, a major challenge is the number of observations
is often only comparable or even smaller than the number of parameters. Therefore,
in Chapter 4, co-authored with Professor Hao Wang, we induce sparsity via graphical
models in order to produce stable and robust covariance matrix estimates. We propose
a new algorithm for Bayesian model determination in Gaussian graphical models under
G-Wishart prior distributions. We first review recent developments in sampling from
G-Wishart distributions for given graphs, with a particular interest in the efficiency
of the block Gibbs samplers and other competing methods. We generalize the maximum
clique block Gibbs samplers to a class of flexible block Gibbs samplers and prove
its convergence. This class of block Gibbs samplers substantially outperforms its
competitors along a variety of dimensions. We next develop the theory and computational
details of a novel Markov chain Monte Carlo sampling scheme for Gaussian graphical
model determination. Our method relies on the partial analytic structure of the G-Wishart
distributions integrated with the exchange algorithm. Unlike existing methods, the
new method requires neither proposal tuning nor evaluation of normalizing constants
of the G-Wishart distributions.</p>
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