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Category Archives: C5

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.

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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

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.

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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.

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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.

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Advisor: George Tauchen | JEL Codes: C01, C13, C22, C29, C58 | Tagged: Beta, Financial Markets, Heterogeneous Autoregressive, Persistence

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