Bayesian parametric and semi-parametric financial tail-risk forecasting incorporating range and realized measures

Now we are in a world saturated with data and information, and numerous quantitative methods for financial risk management are proposed and used by many financial research institutions and organizations within recent years. Quantitative financial risk measurement is now a fundamental tool for investment decisions, capital allocation and external regulation. The Global Financial Crisis (GFC) has once again emphasized the importance of accurate risk measurement and prediction for financial organizations, which require accurate volatility estimation and forecasting. The intra-day range has been frequently used in the literature and proven its superiority compared to return in volatility estimation and forecasting. Furthermore, high frequency econometrics has been gaining more popularity in the last decade and has developed into a major area in econometrics, driven by the increasing availability of high frequency data and algorithm-based high frequency trading in seconds or even milliseconds. The data recorded on a high frequency level contain much more information than the conventional daily financial data, and thus the volatility measures calculated based on high frequency data are much more efficient than the daily return and range. In this thesis, we aim to develop a series of volatility and tail risk models employing intra-day and high frequency volatility measures. Firstly, the Realized GARCH framework is extended to incorporate the realized range, as potentially more efficient series of information than realized variance. Furthermore, we propose an innovative sub-sampled realized range and also adopt an existing scaling scheme, in order to deal with the micro-structure noise of the high frequency volatility measures. In addition, a Bayesian estimator is developed for the Realized GARCH type models, and presents favourable results compared to the frequentist estimator. Through empirical studies on various market indices that consider predictive likelihoods as well as 1% VaR and ES forecasting, results clearly indicate that the realized range and sub-sampled realized range in a Realized GARCH framework, with Student-t errors, lead to more accurate volatility and predictive density forecasts. Further, a new framework called Realized Conditional Autoregressive Expectile (Realized CARE) is proposed, through incorporating a measurement equation into the conventional CARE model, in a manner analogous to the Realized GARCH model. The intra-day range and realized measures (e.g. realized variance and realized range, etc.) are employed as the dependent variable in the measurement equation. The measurement equation here models the contemporaneous dependence between the realized measure and the latent conditional expectile. In addition, a targeted search based on a quadratic approximation is proposed, which improves the computational speed of estimation of the expectile level parameter. Bayesian adaptive Markov Chain Monte Carlo methods and likelihood-based frequentist methods are proposed for estimation, whilst their properties are compared via a simulation study. Furthermore, the methods of sub-sampling and scaling are applied to the realized variance and realized range, to help deal with the inherent micro-structure noise of the realized volatility measures. In a real forecasting study applied to 6 market indices and 3 individual assets, compared to the original CARE, the parametric GARCH and Realized GARCH models, one-day-ahead Value-at-Risk and Expected Shortfall forecasting results favor the proposed Realized CARE model, especially the Realized CARE model incorporating the realized range and the sub-sampled realized range. Finally, we propose a new intra-day volatility estimator named signed range, which incorporates open, high and low prices for its calculation. A high frequency simulation study is conducted to analyze the relationship between signed range volatility and return volatility. An adaptive MCMC is developed for the parameter estimation and is compared with the maximum likelihood approach through simulation study. Then we propose the symmetric and asymmetric Conditional Autoregressive Signed Range (CARSR) type models, and the proposed models demonstrate their superiority compared to GARCH and Conditional Autoregressive Range (CARR) models in the 1% VaR and ES forecasting study

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Contains fulltext : 76546.pdf (author's version ) (Open Access)Inaugural address RU, 25 maart 201023 p

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