RT Journal Article
SR Electronic
T1 Toward Better Risk Forecasts
JF The Journal of Portfolio Management
FD Institutional Investor Journals
SP 82
OP 91
DO 10.3905/jpm.2005.500362
VO 31
IS 3
A1 Kenneth Gosier
A1 Ananth N Madhavan
A1 Vitaly Serbin
A1 Jian Yang
YR 2005
UL https://pm-research.com/content/31/3/82.abstract
AB Volatility forecasts crucial to many financial applications usually assume implicitly that the frequency of the data should match the forecast horizon; portfolio managers typically rely on risk models estimated using monthly data to produce monthly volatility forecasts, for example. For longer–term forecasts, this practice has two drawbacks: Volatility estimates can be based on stale data; and return events occurring within long sampling intervals are obscured, confounding estimation. Monthly volatility risk measures constructed using higher–frequency data seem to be more robust than those using low–frequency data. Microstructure effects can explain the differences in estimates.