Working Paper: NBER ID: w15808
Authors: Yacine Atsahalia; Jean Jacod
Abstract: This paper describes a simple yet powerful methodology to decompose asset returns sampled at high frequency into their base components (continuous, small jumps, large jumps), determine the relative magnitude of the components, and analyze the finer characteristics of these components such as the degree of activity of the jumps. We extend the existing theory to incorporate to effect of market microstructure noise on the test statistics, apply the methodology to high frequency individual stock returns, transactions and quotes, stock index returns and compare the qualitative features of the estimated process for these different data and discuss the economic implications of the results.
Keywords: Asset Returns; Market Microstructure; High Frequency Data
JEL Codes: C12; C14; C22; G12
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| market microstructure noise (G14) | test statistics (C52) |
| jumps in asset pricing models (G19) | relative risk measures (C52) |
| jumps in asset pricing models (G19) | optimal portfolio allocations (G11) |
| jumps included in model (C24) | market completeness consequences for contingent claims valuation (G10) |
| market microstructure noise (G14) | power variations (L94) |
| different types of jumps (C34) | derivative pricing (G13) |
| dynamics of asset returns (G12) | systematic and idiosyncratic risks (D80) |
| dynamics of asset returns (G12) | option pricing (G13) |
| dynamics of asset returns (G12) | risk management strategies (H12) |