Suchergebnisse

A simple asset pricing model with two types of boundedly rational traders, fundamentalists and chartists, is studied. Fractions of trader types change over time according to evolutionary learning, with chartists conditioning their forecasting rule upon deviations from a benchmark fundamental. Volatility clustering arises endogenously and two generic mechanisms are proposed as an explanation: (1) coexistence of a stable steady state and a stable limit cycle, due to a so-called Chenciner bifurcation of the system and (2) intermittency and associated bifurcation routes to strange attractors. Economic intuition as to why these phenomena arise in nonlinear multi-agent evolutionary systems is provided.

The financial crisis that began in the summer of 2007 has led to criticisms that the financial models used by risk managers, portfolio managers, and even regulators simply do not reflect the realities of today's markets. While one tool cannot be blamed for the entire global financial crisis, improving the flexibility and statistical reliability of existing models, in addition to developing better models, is essential for both financial practitioners and academics seeking to explain and prevent extreme events.

AbstractWe study volatility clustering in daily stock returns at both the index and firm levels from 1985 to 2000. We find that the relation between today's index return shock and the next period's volatility decreases when important macroeconomic news is released today and increases with the shock in today's stock market turnover. Collectively, our results suggest that volatility clustering tends to be stronger when there is more uncertainty and disperse beliefs about the market's information signal. Our findings also contribute to a better understanding of the joint dynamics of stock returns and trading volume.

In this study, we apply a Lagrange multiplier (LM) test for the autoregressive conditional heteroscedasticity (ARCH) effects and an exponential generalized autoregressive conditional heteroscedasticity-in-mean (EGARCH-M) model to assess whether regional house prices in Canada exhibit financial characteristics similar to stock indices. Volatility clustering, positive risk-return relationships, and leverage effects are empirically shown to exist in the majority of provincial housing markets of Canada. These volatility behaviors, which reflect regional idiosyncrasies, are further found to differ across provinces. More densely populated provinces exhibit stronger volatility clustering of house prices. The existence of these volatility patterns similar to stock indices has important implications ranging from proper portfolio management to government policy.

In this study, we apply a Lagrange multiplier (LM) test for the autoregressive conditional heteroscedasticity (ARCH) effects and an exponential generalized autoregressive conditional heteroscedasticity-in-mean (EGARCH-M) model to assess whether regional house prices in Canada exhibit financial characteristics similar to stock indices. Volatility clustering, positive risk-return relationships, and leverage effects are empirically shown to exist in the majority of provincial housing markets of Canada. These volatility behaviors, which reflect regional idiosyncrasies, are further found to differ across provinces. More densely populated provinces exhibit stronger volatility clustering of house prices. The existence of these volatility patterns similar to stock indices has important implications ranging from proper portfolio management to government policy.