Perhaps it’s not too astounding a finding…
But a Federal Reserve staff working paper by Dobrislav P. Dobrev and Pawel J. Szerszen has found that using historical high frequency data to forecast equity returns is far more effective than using general daily or monthly data.
The paper is quite technical, but here’s its main observation:
As a practical rule of thumb, we find that two years of high frequency data often suffice to obtain the same level of precision as twenty years of daily data, thereby making our approach particularly useful in finance applications where only short data samples are available or economically meaningful to use.
Moreover, we find that compared to model inference without high-frequency data, our approach largely eliminates underestimation of risk during bad times or overestimation of risk during good times. We assess the attainable improvements in VaR forecast accuracy on simulated data and provide an empirical illustration on stock returns during the financial crisis of 2007-2008.
In other words, it’s much easier to accurately forecast risk if you consider high frequency data when developing your models.
As the paper also concludes (our emphasis):
Last, but perhaps most important in risk management applications, we find that risk forecasts based on our approach to exploiting high-frequency data are considerably closer to the truth in both bad and good times relative to those stemming from traditional model inference on daily data, which we find can overestimate risk by as much as 30% in good times or underestimate it by as much as 10% in bad times.
We support our findings both with extensive simulations and an empirical illustration on VaR forecasts for S&P500 and Google returns during the financial crisis of 2007-2008. Thanks to incorporating the strong information content of high-frequency volatility measures, we are able to better curb risk taking exactly when needed the most, i.e. early on in times of crisis (rather than with a delay), while avoiding unnecessary overstatement of risk in normal times.
Qualitatively, our findings are robust both across different models and jump-robust volatility measures on high frequency data that we analyze. In view of the documented substantial precision gains in forecasting risk of equity returns, the estimation approach we propose can directly add value in different areas of risk management and asset pricing. Beyond equity returns, the method can be applied also to other financial data such as foreign exchange rates, bonds and interest rates. It can be easily geared also towards model specification testing. More generally, we establish a promising and tractable way to incorporate additional sources of information, such as alternative high frequency volatility measures, into models in state space form.
Which means that when crisis stikes, it’s high frequency firms that will have most likely managed their risk exposures in time to curb losses.
Related links:
An impatient market is not a happy market - FT Alphaville
High-frequency trading: Up against a bandsaw – FT
Data-feed arbitrage - FT Alphaville