Econometrics Commons^™

Data Driven Value-At-Risk Forecasting Using A Svr-Garch-Kde Hybrid, Marius Lux, Wolfgang Karl Hardle, Stefan Lessmann

Sim Kee Boon Institute for Financial Economics

Appropriate risk management is crucial to ensure the competitiveness of financial institutions and the stability of the economy. One widely used financial risk measure is value-at-risk (VaR). VaR estimates based on linear and parametric models can lead to biased results or even underestimation of risk due to time varying volatility, skewness and leptokurtosis of financial return series. The paper proposes a nonlinear and nonparametric framework to forecast VaR that is motivated by overcoming the disadvantages of parametric models with a purely data driven approach. Mean and volatility are modeled via support vector regression (SVR) where the volatility model is motivated …

On Emerging Asia-Pacific Equity Markets From The Perspective Of The Dynamics Of Mean And Volatility Spillovers, Li Xu

FIU Electronic Theses and Dissertations

This dissertation investigates the dynamics of mean and volatility spillovers from the U.S. and three large (regional) Asia-Pacific stock markets to ten small (local) ones from June 2008 to May 2013.

After a brief introduction to the main purposes and contributions of my research in Chapter 1, I examine the impact of lagged American and regional returns on the local markets in Chapter 2. By building up a univariate autoregressive model and treating lagged U.S. and regional returns as exogenous variables, I find that the local markets have statistically significant exposure to lagged returns of their own and the U.S. …