M. Humayun Kabir

Russian financial crisis, US financial stock returns and the IMF

We find a statistically significant increase in adjusted correlation between portfolio returns during the Russian financial crisis period, especially during the peak of the crisis. We also find that commercial bank and Savings & Loan Institutions (S&L) portfolios lost market value significantly with events, starting with the debt moratorium and ruble devaluation on 17 August 1998. Much of the significant losses were driven by smaller size portfolios of financial institutions. The greater losses were incurred by commercial banks, and most importantly, by smaller commercial banks, S&Ls and investment banks in the third sub-period following the debt moratorium. We also found a form of contagion effect on the portfolio of smaller banks. Moreover, International Monetary Fund help or bailout has been perceived ineffective contributing to any recovery from crisis in Russia.We find a statistically significant increase in adjusted correlation between portfolio returns during the Russian financial crisis period, especially during the peak of the crisis. We also find that commercial bank and Savings & Loan Institutions (S&L) portfolios lost market value significantly with events, starting with the debt moratorium and ruble devaluation on 17 August 1998. Much of the significant losses were driven by smaller size portfolios of financial institutions. The greater losses were incurred by commercial banks, and most importantly, by smaller commercial banks, S&Ls and investment banks in the third sub-period following the debt moratorium. We also found a form of contagion effect on the portfolio of smaller banks. Moreover, International Monetary Fund help or bailout has been perceived ineffective contributing to any recovery from crisis in Russia.

Catch Animal Spirits in Auction: Evidence from New Zealand Property Market

Following the animal spirits theory proposed by Akerlof and Shiller, this article contributes to behavior economics by investigating the possibility of using auction sales data to capture evidence of irrational exuberance in the housing market. Using the monthly percentages of residential property auction sales for Auckland, Wellington and Christchurch regions in New Zealand from 2006 to 2015, and the exuberance testing method proposed by Phillips, Shi and Yu, we find that animal spirits have been developing in the Auckland housing market since 2013, but not in other regions. When compared to the results based on price†to†rent ratios, auction sales provide more meaningful results for identifying market†wide irrational exuberance at an early stage. The causality test on price†to†rent ratios and auction sales volume shows that asset prices and animal spirits influence each other in the short run. In the long run, prices have significant effect on animal spirits, but not vice versa.

Nonlinear decomposition analysis of risk aversion and stock-holding behaviour of US households

Using the Survey of Consumer Finances of 2001 and 2004, this article provides a nonlinear decomposition analysis to find the relative importance of household risk preference characteristics after allowing adjustment for distribution of other household characteristics. We find significant contributions of net worth, college education, inherited wealth, managerial and low unemployment risk occupation in explaining the differences in probability of stockholding among the least and higher risk-averse households. The results show the impact of internet bubble and recession in post-9/11 environment on risk preference groups in terms of their stockholding behaviour.

Pricing and hedging options with GARCH-stable proxy volatilities

ABSTRACT This article considers modelling nonnormality in return with stable Paretian (SP) innovations in generalized autoregressive conditional heteroskedasticity (GARCH), exponential generalized autoregressive conditional heteroskedasticity (EGARCH) and Glosten-Jagannathan-Runkle generalized autoregressive conditional heteroskedasticity (GJR-GARCH) volatility dynamics. The forecasted volatilities from these dynamics have been used as a proxy to the volatility parameter of the Black–Scholes (BS) model. The performance of these proxy-BS models has been compared with the performance of the BS model of constant volatility. Using a cross section of S&P500 options data, we find that EGARCH volatility forecast with SP innovations is an excellent proxy to BS constant volatility in terms of pricing. We find improved performance of hedging for an illustrative option portfolio. We also find better performance of spectral risk measure (SRM) than value-at-risk (VaR) and expected shortfall (ES) in estimating option portfolio risk in case of the proxy-BS models under SP innovations. Abbreviation: generalized autoregressive conditional heteroskedasticity (GARCH), exponential generalized autoregressive conditional heteroskedasticity (EGARCH) and Glosten-Jagannathan-Runkle generalized autoregressive conditional heteroskedasticity (GJR-GARCH)

Back-testing extreme value and Lévy value-at-risk models: Evidence from international futures markets

Purpose - The purpose of the paper is to back-test value-at-risk (VaR) models for conditional distributions belonging to a Generalized Hyperbolic (GH) family of Levy processes – Variance Gamma, Normal Inverse Gaussian, Hyperbolic distribution and GH – and compare their risk-management features with a traditional unconditional extreme value (EV) approach using data from future contracts return data of S&P500, FTSE100, DAX, HangSeng and Nikkei 225 indices. Design/methodology/approach - The authors apply tail-based and Levy-based calibration to estimate the parameters of the models as part of the initial data analysis. While the authors utilize the peaks-over-threshold approach for generalized Pareto distribution, the conditional maximum likelihood method is followed in case of Levy models. As the Levy models do not have closed form expressions for VaR, the authors follow a bootstrap method to determine the VaR and the confidence intervals. Finally, for back-testing, they use both static calibration (on the entire data) and dynamic calibration (on a four-year rolling window) to test the unconditional, independence and conditional coverage hypotheses implemented with 95 and 99 per cent VaRs. Findings - Both EV and Levy models provide the authors with a conservative proportion of violation for VaR forecasts. A model targeting tail or fitting the entire distribution has little effect on either VaR calculation or a VaR model’s back-testing performance. Originality/value - To the best of the authors’ knowledge, this is the first study to explore the back-testing performance of Levy-based VaR models. The authors conduct various calibration and bootstrap techniques to test the unconditional, independence and conditional coverage hypotheses for the VaRs.