Abstract:
Observers of financial markets have long noted that asset prices are very volatile
and commonly exhibit jumps (price spikes). Thus, the assumption of a continuous
process for asset price behavior is often violated in practice. Although empirical
studies have found that the impact of such jumps is transitory, the shortterm effect
in the volatility may nonetheless be considerable with important financial implications
for the valuation of derivatives, asset allocation and risk management.
This dissertation contributes to the literature in two areas. First, I evaluate the
small sample properties of a nonparametric method for identifying jumps. I focus on
the implication of adding noise to the prices and recent methods developed to contend
with such market frictions. Initially, I examine the properties and convergence results
of the power variations that constitute the jump statistics. Then I document the
asymptotic results of these jump statistics. Finally, I estimate their size and power. I
examine these properties using a stochastic volatility model incorporating alternative
noise and jump processes. I find that the properties of the statistics remain close to the
asymptotics when methods for managing the effects of noise are applied judiciously.
Improper use leads to invalid tests or tests with low power. Empirical evidence
demonstrates that the nonparametric method performs well for alternative models,
noise processes, and jump distributions.
In the second essay, I present a study on market data from U.S. energy futures
markets. I apply a nonparametric method to identify jumps in futures prices of
crude oil, heating oil and natural gas contracts traded on the New York Mercantile
Exchange. The sample period of the intraday data covers January 1990 to January
2008. Alternative methods such as staggered returns and optimal sampling frequency
methods are used to remove the effects of microstructure noise which biases the tests
against detecting jumps.
I obtain several important empirical results: (i) The realized volatility of natural
gas futures exceeds that of heating oil and crude oil. (ii) In these commodities,
large volatility days are often associated with large jump components and large jump
components are often associated with weekly announcements of inventory levels. (iii)
The realized volatility and smooth volatility components in natural gas and heating
oil futures are higher in winter months than in summer months. Moreover, cold
weather and inventory surprises cause the volatility in natural gas and heating oil to
increase during the winter season. (iv) The jump component produces a transitory
surge in total volatility, and there is a strong reversal in volatility on days following
a significant jump day. (v) I find that including jump and seasonal components
as explanatory variables significantly improves the modeling and forecasting of the
realized volatility.