Cross-Stock Comparisons of the Relative Contribution of Jumps to Total Price Variance
By Vivek Bhattacharya
This paper uses high-frequency price data to study the relative contribution of jumps to the total volatility of an equity. In particular, it systematically compares the relative contribution of jumps across a panel of stocks from three different industries by computing the cross-correlation of this statistic for pairs of stocks. We identify a number of empirical regularities in this cross-correlation and compare these observations to predictions from a standard jump-diffusion model for the joint price process of two stocks. A main finding of this paper is that this jump-diffusion model, when calibrated to particular pairs of stocks in the data, cannot replicate some of the empirical patterns observed. The model predictions differ from the empirical observations systematically: predictions for pairs of stocks from the same industry are on the whole much less accurate than predictions for pairs of stocks from different industries. Some possible explanations for this discrepancy are discussed.
Advisor: George Tauchen | JEL Codes: C5, C52, C58 | Tagged: Econometric Modeling, Financial Econometrics, High-Fequency Data, Jumps
Relative Contribution of Common Jumps in Realized Correlation
By Kyu Won Choi
This paper studies common intraday jumps and relative contribution of these common jumps
in realized correlation between individual stocks and market index, using high-frequency price
data. We find that the common jumps significantly contribute in realized correlation at different
threshold cut-offs and both common jumps and realized correlation are relatively consistent across
time period including financial crisis. We also find a weak, positive relationship between relative
contribution of common jumps and realized correlation, when we further sample high-frequency
data into a year. We also observe that the volatility index and market index reveal the strongest
relationship.
Advisor: Geourge Tauchen, Tim Bollerslev | JEL Codes: C40, C58, G10 | Tagged: Diffusive Covariation, Realized Correlation, Relative Contribution of Common Jumps
Volatility and Correlation Modeling for Sector Allocation in International Equity Markets
By Melanie Fan and Kate Yuan
Reliable estimates of volatility and correlation are crucial in asset allocation and risk management. This paper investigates Static, RiskMetrics, and Dynamic Conditional Correlation (DCC) models for estimating volatility and correlation by testing them in an asset allocation context. Optimal allocation weights for one year found using estimates from each model are carried to the subsequent year and the realized Sharpe ratio is computed to assess portfolio performance. We also study cumulative risk-adjusted returns over the entire sample period. Our ndings indicate that DCC does not consistently have an advantage over the other two models, although it is optimal in certain scenarios.
Advisor: Aino Levonmaa, Emma Rasiel | JEL Codes: C32, C51, G11, G15 | Tagged: Asset Allocation, Dynamic Correlation, Emerging Markets, Volatilita
Beta Estimation Using High Frequency Data
By Angela Ryu
Using high frequency stock price data in estimating nancial measures often causes serious distortion. It is due to the existence of the market microstructure noise, the lag of the observed price to the underlying value due to market friction. The adverse eect of the noise can be avoided by choosing an appropriate sampling frequency. In this study, using mean square error as the measure of accuracy in beta estimation, the optimal pair of sampling frequency and the trailing window was empirically found to be as short as 1 minute and 1 week, respectively. This surprising result may be due to the low market noise resulting from its high liquidity and the econometric properties of the errors-in-variables model. Moreover, the realized beta obtained from the optimal pair outperformed the constant beta from the CAPM when overnight returns were excluded. The comparison further strengthens the argument that the underlying beta is time-varying.
Advisor: George Tauchen | JEL Codes: C51, C58, G17 | Tagged: Beta estimation, Beta Trailing Window, High-Frequency Data, Market Microstructure Noise, Optimal Sampling Interval, Realized Beta
Time-Varying Beta: The Heterogeneous Autoregressive Beta Model
By Kunal Jain
Conventional models of volatility estimation do not capture the persistence in high-frequency market data and are not able to limit the impact of market micro-structure noise present at very finely sampled intervals. In an attempt to incorporate these two elements, we use the beta-metric as a proxy for equity-specific volatility and use finely sampled time-varying conditional forecasts estimated using the Heterogeneous Auto-regressive framework to form a predictive beta model. The findings suggest that this predictive beta is better able to capture persistence in financial data and limit the effect of micro-structure noise in high frequency data when compared to the existing benchmarks.
Advisor: George Tauchen | JEL Codes: C01, C13, C22, C29, C58 | Tagged: Beta, Financial Markets, Heterogeneous Autoregressive, Persistence
Collusion with Three Bidders at First-Price Auctions
By Andrew Born
Lopomo, Marx, & Sun (2009) show that, given a speci fied environment, pro table collusion is not possible for a two-person bidding ring operating at a fi rst-price sealed-bid auction. This research investigates the rigor of their result by expanding the theoretical framework to the case of a three-bidder cartel. The output generated from the linear programming model con firms the authorsearlier result. This is a key finding as it is the first to establish a basis for comparison of equilibrium surplus scenarios among multiple-bidder auction formats. The analytic and numerical results pave the way for future research examining the effect of cartel size on profi tability and have many real-world implications for both private and public policy alike.
Advisor: Leslie Marx | JEL Codes: C57
Strategic Behavior in Online Auctions: An Analysis of Sniping
by Claudia Lai
Abstract
Sniping is a prevalent phenomenon in eBay auctions, which have a fixed end time. Such
practice seems apparently inconsistent with standard auction theory – last minute bids are
received with reduced probability, and should rationally be submitted earlier – yet
previous literature has shown that bidders typically do not engage in early bidding, or if
they do, submit low valued bids that they then raise at the last minute. Modeling of the
sniping strategy in a game theoretical framework has been limited to games under
abstract assumptions not applicable to the conditions under which the eBay bidding game
is typically played. This paper intends to carry out analysis within a more general, more
applicable framework, and finds that sniping is a collusive strategy that players engage in
to prevent price inflation of an auctioned item, and weakly dominates engaging in
bidding one’s valuation early, so long as late bids are received with sufficiently high
probability.
Professor Daniel Graham, Faculty Advisor
JEL Codes: C57
Analysis of Auction Price Risk: An Empirical Study of the Australian Aboriginal Art Market
by Ilya Voytov
Abstract
Auction theory economists have shown that auctions can be structured to maximize
the expected revenue to the seller. In this thesis, I show that they can also be
optimized to minimize the sellers’ risk through an understanding of the driving factors
behind seller’s auction price risk. I derive a general form equation for auction price
variance, and discuss how changes in the number of bidders and the type of bidders
affect the sellers’ auction risk. An empirical component of this paper takes data from
auction sales of Australian Aboriginal art and uses observed price variance to make
deductions about the underlying types of participating bidders.
Professor Neil De Marchi, Faculty Advisor
JEL Codes: C57, D44, D53, N27,
