Multi-Horizon Forecast Optimality Based on Related Forecast Errors
By Christopher G. MacGibbon
This thesis develops a new Multi-Horizon Moment Conditions test for evaluating multi-horizon forecast optimality. The test is based on the variances, covariances and autocovariances of optimal forecast errors that should have a non-zero relationship for multi-horizon forecasts. A simulation study is conducted to determine the test’s size and power properties. Also, the effects of combining the Multi-Horizon Moment Conditions test and the well-known Mincer-Zarnowitz and zero autocorrelation tests into one forecast optimality test are examined. Lastly, an empirical study evaluating forecast optimality for four multi-horizon forecasts made by the Survey of Professional Forecasters is included.
Advisors: Andrew Patton, Grace Kim and Kent Kimbrough | JEL Codes: G1, G17, G00
Extreme Value Theory with High-Frequency Financial Data
By Abhinay Sawant
Extreme Value Theory (EVT) is one of the most commonly applied models in financial risk management for estimating the Value at Risk of a portfolio. However, the EVT model is practical for estimation only when data is independent and identically distributed, which usually does not characterize financial returns data. This paper aims to modify this model by using high-frequency data to standardize financial returns by their realized volatility and then tests the modified model with recent equity data. The results from the paper show an improvement in the EVT model when forward volatility can be properly forecast.
Advisor: George Tauchen | JEL Codes: G00
