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Abstract
This thesis studies four related topics in financial economics; realized volatility modelling and forecasting in the presence of model instability, forecasting stock return realized volatility at the quarterly frequency, quarterly realized beta measurement and beta neutrality evaluation under a popular long short strategy.
Recent advances in financial econometrics have allowed for the construction of efficient post measures of daily volatility. The first topic investigates the importance of instability in models of realized volatility and their corresponding forecasts. Testing for model instability is conducted with a subsampling method. We show that removing structurally unstable data of a short duration has a negligible impact on the accuracy of conditional mean forecasts of volatility. In contrast, it does provide a substantial improvement in a model's forecast density of volatility. In addition, the forecasting performance improves, often dramatically, when we evaluate models on structurally stable data.
The second topic is on forecasting stock return volatility at quarterly level. The last decade has seen substantial advances in the measurement, modeling and forecasting of volatility which has entered around the realized volatility literature. To date, most of the focus has been on the daily and monthly frequency, with little attention on longer horizons such as the quarterly frequency. In finance applications, forecasts of volatility at horizons such as quarterly are of fundamental importance to asset pricing and risk management. In this chapter we evaluate models for stock return volatility forecasting at the quarterly frequency. We find that an autoregressive model with one lag of quarterly realized volatility produces the most accurate forecasts, and dominates other approaches, such as the recently proposed mixed-data sampling (MIDAS) approach. Chen and Reeves (2009) introduced a new beta measurement technique via the Hodrick-Prescott filter and found it substantially reduced measurement error and produced much better performance than Fama-MacBeth measurement approach at the monthly frequency.
The third chapter extends this technique to quarterly beta measurement. The finding in Chen and Reeves (2009) is also confirmed at the quarterly frequency. Hodrick-Prescott filtered beta contains the most relevant information and follows closely the true underlying beta. This result is also used in the final chapter to construct the proxy for the true underlying quarterly beta time series.
The final topic is to investigate the economic value of realized beta. Market neutral funds are commonly advertised as alternative investments offering returns which are uncorrelated with the broad market. Utilizing recent advances in financial econometrics we demonstrate that constructing market neutral funds from monthly return data can be widely inaccurate. Given the monthly frequency is the most common for return measurement in the hedge fund industry, our findings highlight the need for higher frequency return data to be more commonly utilized. We demonstrate the use of daily returns to achieve a more market neutral portfolio, relative to the case of only using monthly returns.