Skip to main content
Article
Using Neural Networks to Forecast Volatility for an Asset Allocation Strategy Based on the Target Volatility
Procedia Computer Science
  • Youngmin Kim
  • David Lee Enke, Missouri University of Science and Technology
Abstract

The objective of this study is to use artificial neural networks for volatility forecasting to enhance the ability of an asset allocation strategy based on the target volatility. The target volatility level is achieved by dynamically allocating between a risky asset and a risk-free cash position. However, a challenge to data-driven approaches is the limited availability of data since periods of high volatility, such as during financial crises, are relatively rare. To resolve this issue, we apply a stability-oriented approach to compare data for the current period to a past set of data for a period of low volatility, providing a much more abundant source of data for comparison. In order to explore the impact of the proposed model, the results of this approach will be compared to different volatility forecast methodologies, such as the volatility index, the historical volatility, the exponentially weighted moving average (EWMA), and the generalized autoregressive conditional heteroskedasticity (GARCH) model. Trading measures are used to evaluate the performance of the models for forecasting volatility. An empirical study of the proposed model is conducted using the Korea Composite Stock Price Index 200 (KOSPI 200) and certificate of deposit interest rates from January, 2006 to February, 2016.

Meeting Name
Complex Adaptive Systems (2016: Nov. 2-4, Los Angeles, CA)
Department(s)
Engineering Management and Systems Engineering
Research Center/Lab(s)
Intelligent Systems Center
Keywords and Phrases
  • Adaptive systems,
  • Complex networks,
  • Forecasting,
  • Investments,
  • Neural networks,
  • Asset allocation,
  • Data-driven approach,
  • Exponentially weighted moving average,
  • Forecast volatility,
  • Forecasting volatility,
  • Generalized autoregressive conditional heteroskedasticity,
  • Volatility forecasting,
  • Volatility forecasts,
  • Electronic trading,
  • Artificial neural networks,
  • Asset allocation strategy,
  • Target volatility
Document Type
Article - Conference proceedings
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2016 The Authors, All rights reserved.
Creative Commons Licensing
Creative Commons Attribution-Noncommercial-No Derivative Works 4.0
Publication Date
11-1-2016
Citation Information
Youngmin Kim and David Lee Enke. "Using Neural Networks to Forecast Volatility for an Asset Allocation Strategy Based on the Target Volatility" Procedia Computer Science Vol. 95 (2016) p. 281 - 286 ISSN: 1877-0509
Available at: http://works.bepress.com/david-enke/51/