title: Volatility and return forecasting
title: time series and options-based methods
titleType: Subtitle
Creators: name: Yao, Xingzhi
Publisher: name: Lancaster University
Year: 2017

description: This thesis attempts to model and forecast returns and realized volatility
      using two different methods: time series models that exploit the
      historical information set and options-based approach that provides a
      natural forecast of return variation from listed option prices. Both
      univariate and multivariate estimation of the time series models are
      considered in our analysis.
      
      Chapter 1: This chapter introduces a modified fractionally co-integrated
      vector autoregressive model, M-FCVAR, that caters for systems with I(0)
      and I(d) variables under the presence of long memory in the co-integrating
      residuals. Model inference of the FCVAR and M-FCVAR are compared using
      Monte Carlo simulations and an empirical application. The M-FCVAR is found
      to yield better in-sample fit and more precise model estimates. Higher
      return predictability is observed over long horizons using the M-FCVAR in
      the empirical example. In addition, the shocks associated with the I(0)
      variables could be permanent or transitory. We show that particular
      equation specifications are required to restrict these shocks when they
      produce only transitory effects on the I(d) variables. The simulation
      results show that the inappropriate treatment of the shock to the I(0)
      variable may negatively affect the precision in the estimation of the
      model parameters as well as the in-sample fit.
      
      Chapter 2: This chapter evaluates the performance of various measures of
      model-free option-implied volatility in predicting returns and realized
      volatility. The critical role of the out-of-the money call options is
      highlighted through an investigation of the relevance of different
      components of the model-free implied volatility. The Monte Carlo
      simulations show that: first, volatility forecasting performance of
      measures of implied volatility can be enhanced by employing an
      interpolation-extrapolation technique; second, for most measures
      considered, gains in their predictive power for future returns can be
      obtained by implementing an interpolation procedure. An empirical
      application using SPX options recorded from 2003 to 2013 further
      illustrates these claims.
      
      Chapter 3: This chapter compares the performance of various least absolute
      shrinkage and selection operator (Lasso) based models in forecasting
      future log realized variance (RV) constructed from high-frequency returns.
      We conduct a comprehensive empirical study using the SPY and 10 individual
      stocks selected from 10 different sectors. In an in-sample analysis, we
      provide evidence for the invalidity of the lag structure implied by the
      heterogeneous autoregressive (HAR) model which has been heavily adopted in
      volatility forecast. In our out-of-sample study considering the full time
      period, the best forecasting performance is usually provided by the
      Lasso-based model and the idea of forecast combination tends to improve
      the forecasting accuracy of the Lasso-based model. Among all models of
      interest, the ordered Lasso AR using the forecast combination serves as
      the top performer most frequently in forecasting RV and its improvements
      over the HAR model are, in most cases, significant over monthly horizons.
      Moreover, we observe a strong impact of the financial crisis on the
      performance of the Lasso-based models. Nevertheless, the ordered Lasso AR
      with the forecast combination still retains its advantages in the
      post-crisis period, especially over long horizons. In line with the
      existing study, the superiority of the Lasso-based models is more evident
      in a larger forecasting window size. The conclusions outlined above are
      not affected by the variation in the sampling frequency upon which the RV
      series are based. However, as the sampling frequency increases, there
      tends to be more situations where the Lasso-based model achieves the top
      performance in the full sample analysis.