Title: Volatility surface, term structure and meta-learning-based price forecasting for option strategies design
Authors: Zhou, Shifei (周仕飛)
Abstract: The forecasting of underlying asset price is important for investors to make financial
decisions. A successful prediction can save investors from risk of losing money. This
thesis focuses on the forecasting of underlying asset price and develops an
option-based trading system. A literature review is conducted on volatility and its
related topics. These topics include volatility forecasting, implied volatility smile,
implied volatility term structure, implied volatility surface, local implied volatility and
stochastic volatility. The major forecasting models and methodologies of volatility
prediction are introduced and classified. This classification also gives a direct
blueprint for the composition of this thesis. Based on the investigation, this thesis
proposes three research topics and makes contributions as follows.
First, a model-free term structure-based stochastic model with adaptive
correlation is proposed for price forecasting. Based on observations, the constant
assumption of correlation of stochastic volatility model is found to be unsuitable for
analyzing Hong Kong options market. The least squares method is used to evaluate
this correlation. Besides, the term structure implied volatility is obtained by
integrating option price and strike price from current time to expiry date. This
model-free term structure is used as the long-run mean level of stochastic model to
make use of information contained in term structure. Empirical test shows our model
outperforms CEV model and Regression model in terms of one-day-ahead prediction
performance and 78-day distribution of underlying asset price.
Second, a novel local volatility model with mean-reversion process is proposed.
This mean-reversion term is functioned as long run mean level of local volatility
surface. The larger local volatility departs from its mean level, the greater rate local
volatility will be reverted with. Then, a B-spline with moving average knot control
scheme is applied to interpolate local volatility matrix. The bi-cubic B-spline is used to recover local volatility surface from this local volatility matrix. Finally, Monte
Carlo simulation is adopted to predict underlying asset price. Empirical tests show our
mean-reversion local volatility model has a good prediction performance than
traditional local volatility models.
Third, an improved EMD meta-learning rate-based model for gold price
forecasting is proposed. Firstly, we adopt the EMD method to divide the time series
data into different subsets. Secondly, a back-propagation neural network model
(BPNN) is used to function as the prediction model in our system. We update the
online learning rate of BPNN instantly as well as the weight matrix. Finally,
forecasting results from different BPNNs are summed as a final price forecasting
result. The experiment results show that our system has a good forecasting
performance.
Based on the above three theoretical innovation to current financial models, the
forecasting results of three different models are integrated by an average method as a
final forecasting price value. This value is used to decide the movement trend of
underlying asset price. According to the trend, six different movement patterns are
classified. The corresponding option trading strategies are also designed. Then, the
optimal option trading strategy is selected by three criteria. There are Expected Return,
Value at Risk, and Conditional Value at Risk.
To sum up, this thesis proposes three different models to forecast price and
designs option trading strategies based on three criteria. The future works contain two
aspects. First, the system will be improved for high frequency trading. The
improvement includes calculation optimization and model optimization. Second, the
system will be applied to other options and futures markets.
Notes: CityU Call Number: HG6024.A3 Z4948 2013; x, 156 p. : ill. 30 cm.; Thesis (Ph.D.)--City University of Hong Kong, 2013.; Includes bibliographical references (p. 149-156)
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