My
dissertation consists of three essays which contribute new theoretical and
em- pirical results to Volatility Estimation and Market Microstructure theory
as well as Risk Management. Chapter 2 extends the ACD-ICV method proposed by
Tse and Yang (2012) for the estimation of intraday volatility of stocks to
estimate monthly volatility. We compare the ACD-ICV estimates against the
realized volatility (RV) and the generalized autoregressive conditional
heteroskedasticity (GARCH) estimates. Our Monte Carlo experiments and
empirical results on stock data of the New York Stock Exchange show that the
ACD-ICV method performs very well against the other two methods. As a 30-day
volatility predictor, the Chicago Board Options Exchange volatility index (VIX)
predicts the ACD-ICV volatility estimates better than the RV estimates. While
the RV method appears to dominate the literature, the GARCH method based on
aggregating daily conditional variance over a month performs well against the
RV method. Chapter 3 propose to model the aggregate trade volume of stocks in
a quote- driven (specialist) market using a compound Poisson distribution.
Trades are as- sumed to be initiated by either informed or uninformed
traders. Our model treats trade volume endogenously and calibrates two
measures of informed trading: rela- tive frequency of informed trading and
relative volume of informed trading. Empiri- cal analysis of daily volatility
estimates of 50 NYSE stocks shows that trade volume initiated by informed
traders increase volatility, while trade volume initiated by un- informed
traders reduce volatility. However, for both informed and uninformed traders,
the disaggregated effect of trade frequency is to increase volatility. Our
results also confirm that trade frequency dominates trade volume and trade
size in affecting volatility. Yet trade volume and trade size have
incremental information for volatility beyond that exhibited in trade
frequency. Chapter 4 propose to estimate the intraday Value at Risk (IVaR)
for stocks using real-time transaction data. Transaction data filtered by
price durations are modeled employing a two-state asymmetric autoregressive
conditional duration (AACD) model, and the IVaR is computed using Monte Carlo
simulation. Empirical analysis of New York Stock Exchange (NYSE) stocks show
that IVaR estimated using the AACD ap- proach track closely to those using
the Dionne, Duchesne and Pacurar (2009) and Giot (2005) methods. Backtesting
results show that our method performs the best among other methods.
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