Time series analysis for financial market meltdowns

Abstract

There appears to be a consensus that the recent instability in global financial markets may be attributable in part to the failure of financial modeling. More specifically, current risk models have failed to properly assess the risks associated with large adverse stock price behavior. In this paper, we first discuss the limitations of classical time series models for forecasting financial market meltdowns. Then we set forth a framework capable of forecasting both extreme events and highly volatile markets. Based on the empirical evidence presented in this paper, our framework offers an improvement over prevailing models for evaluating stock market risk exposure during distressed market periods.

Tail risk analysis of the S&P/OIC COMCEC 50 index

“…The empirical evidence of the analysed Islamic stocks exhibits asymmetry, heavy-tail and volatility clustering . Similar findings are pointed out by Kim et al (2011) for the conventional index Standard and Poor 500 (SPX) and by for two important Islamic indices: (1) the Dow Jones Islamic Market Index (DJIMI) and (2) the Standard and Poor Sharia index (SHX). The normality violation is true for both Islamic and non-Islamic indices.…”

“…Recent works support also the employment of the stable and tempered stable distributions instead of the normal assumption to model the financial data (Kim, Rachev, Bianchi, Mitov, & Fabozzi, 2011;Rachev & Mittnik, 2000).…”

Risk Estimation on High Frequency Financial Data

“…The (quasi-) maximum-likelihood method is used to estimate the model parameters. The NTS-ARMA-(FI)GARCH approach is carried out in a way as described in Kim et al (2011): (1) …”

Stable-GARCH Models for Financial Returns: Fast Estimation and Tests for Stability

“…McCulloch [7], Rachev and Mittnik [8], Borak et al [9], and Nolan [10] offer extensive accounts of the stable distribution and its wide applicability in finance, while Samorodnitsky and Taqqu [11] provides a more technical development including the multivariate setting. Extensions and compliments to the use of the stable Paretian include the tempered stable (see e.g., [12,13], and the references therein) and the geometric stable (see, e.g., [14][15][16], and the numerous references therein).…”