Iqbal Mansur

Sensitivity of the Bank Stock Returns Distribution to Changes in the Level and Volatility of Interest Rate: A GARCH-M Model

The objective of this paper is to employ the generalized autoregressive conditionally heteroskedastic in the mean (GARCH-M) methodology to investigate the effect of interest rate and its volatility on the bank stock return generation process. This framework discards the restrictive assumptions of linearity, independence, and constant conditional variance in modeling bank stock returns. The model presented here allows for shifts in the volatility equation in response to the changes in monetary policy regime in 1979 and 1982 to be estimated. ARCH, GARCH, and volatility feed back effects are found to be significant. Interest rate and interest rate volatility are found to directly impact the first and the second moments of the bank stock returns distribution, respectively. The latter also affects the risk premia indirectly. The degree of persistence in shocks is substantial for all the three bank portfolios and sensitive to the nature of the bank portfolio and the prevailing monetary policy regime.

Interest Rate Risk and Equity Values of Life Insurance Companies: A GARCH-M Model

The importance of managerial decisions related to interest-sensitive cash flows has received considerable attention in the insurance literature. Consistent with the interest-sensitive nature of insurer assets and liabilities, empirical research has shown that insurer insolvency is significantly related to interest rate volatility. We investigate the interest rate sensitivity of monthly stock returns of life insurers based on a generalized autoregressive conditionally heteroskedastic in the mean (GARCH-M) model. We examine three different portfolios (equally weighted, risk-based, and size-based) with binary variables to explicitly account for varying interest rate strategies adopted by the Federal Reserve System. Results based on data for the period 1975 through 2000 indicate that life insurer equity values are sensitive to long-term interest rates and that interest sensitivity varies across subperiods and across risk-based and size-based portfolios. The results complement insolvency research that links insurer financial performance to changes in interest rates.

International Spillover of Risk and Return Among Major Banking Institutions: A Bivariate GARCH Model

Using the bivariate GARCH methodology, this study examines bank stock sensitivities to market, interest rate, and exchange rate, and investigates the spillover effects of interest rate volatility and unsystematic risk among the banking sectors of the United States and Japan, and the United States and Germany. Empirical results show that return-generating processes of the banking sectors considered can be properly described by GARCH models. Within this framework, banks are found to be highly sensitive to macroeconomic shocks such as the exchange rate and interest rate, with the latter exerting its impact at the volatility level. Moreover, stock volatilities in the banking sectors of the three countries are found to be highly interdependent. The direction and magnitude of the effects from interest rate volatility and unsystematic shocks in one country on other countries are sensitive to the origin of the shock, with the United States playing a leadership role. The findings have serious implications on international financial stability, international portfolio diversification, and policy formulation by central banks and fiscal authorities.

Bank stock return sensitivities to the long‐term and short‐term interest rates: a multivariate GARCH spproach

This study employs a multivariate GARCH model to investigate the relative sensitivities of the first and the second moment of bank stock return distribution to the short‐term and long‐term interest rates and their respective volatilities. Three portfolios are formed representing the money center banks, large banks, and small banks, respectively. Estimation and testing of hypotheses are carried out for each of the three portfolios separately. The sample includes daily data over the 1988‐2000 period. Several hypotheses are tested within the multivariate GARCH specification. These include the hypotheses of: (i) insensitivity of bank stock return to the changes in the short‐term and long‐term interest rates, (ii) insensitivity of bank stock returns to the changes in the volatilities of short‐term and long‐term interest rates, and (iii) insensitivity of bank stock return volatility to the changes in the short‐term and long‐term interest rate volatilities. The findings indicate that short‐term and long‐term interest rates and their volatilities do exert significant and differential impacts on the return generation process of the three bank portfolios. The magnitudes and the direction of the effect are model‐specific namely that they depend on whether the short‐term or the long‐term interest rate level is included in the mean return equation. These findings have implications on bank hedging strategies against the interest rate risk, regulatory decisions concerning risk‐based capital requirement, and investor’s choice of a portfolio mix.

Market Risk, Interest Rate Risk, and Interdependencies in Insurer Stock Returns: A System-GARCH Model

We examine market risk, interest rate risk, and interdependencies in returns and return volatilities across three insurer segments within a System-GARCH framework. Three main results are obtained: market risk is greatest for accident and health (AH interest rate sensitivity is negative and greatest for Life insurers; and interdependencies in returns are significant with the magnitude being strongest between P&C and A&H insurers. The implication is that greatest diversification benefits arise between Life and the other segments of the insurance industry. Market risk and interest rate risk for diversified firms are smaller than those for nondiversified firms for both product and geographic diversification.

Foreign Exchange Volatility Shifts and Futures Hedging: An ICSS-GARCH Approach

In this study, the impact of volatility regime shifts on volatility persistence and hedge ratio estimation is determined for four major currencies using an iterated cumulative sums of squares (ICSS)-GARCH model. Employing a standard GARCH (1,1) model as the benchmark, within-sample results demonstrate that the inclusion of volatility shifts substantially reduces volatility persistence and the significance of the ARCH and GARCH coefficients. In terms of hedging effectiveness, the ICSS-GARCH model outperforms the standard GARCH model for all four currencies. In comparison to two constant volatility models, the standard GARCH model yields the lowest performance, whereas the ICSS-GARCH model performs at least as well as these models. In out-of-sample analysis, the GARCH model provides substantial variance reductions relative to the constant volatility models. Moreover, the ICSS-GARCH model yields positive variance reductions relative to all competing models, including the standard GARCH model. The results suggest that in cases where dynamic hedging is important, sudden shifts in volatility should not be ignored.

Sectoral stock return sensitivity to oil price changes: a double-threshold FIGARCH model

We investigate the association between the stock return distributions of 10 major U.S. sectors and oil returns within a double-threshold FIGARCH model. This model nests GARCH, IGARCH and Fama–French specifications as its special cases and allows a test of their validity. This model also has the advantage of capturing not only the short-run dynamics (as in the standard GARCH model), but also the long-run persistence pattern of oil shock effects that may decay at a slower hyperbolic pace. We find that: (i) data reject the more restricted GARCH, IGARCH and Fama–French models in favor of FIGARCH, (ii) oil return is a significant determinant of every sectors return and/or return volatility, (iii) oil effects are asymmetric for oil returns above and below the thresholds, (iv) asymmetry is stronger when oil return volatility is greater, (v) volatilities of sectoral returns exhibit threshold-based regime shifts, and (vi) oil shock effects follow a hyperbolic, rather than an exponential decay pattern, establishing long-term persistence of shocks. Policy implications are drawn.

Expected returns and economic factors: a GARCH approach

This paper provides evidence that economic variables which proxy for discount rate changes and cash flow expectations are important determinants of expected excess market returns. The results also show that the GARCH process is an appropriate methodology in the modelling of monthly excess returns and that the assumptions of statistical independence and linearity, common to models of asset pricing, are inappropriate. Furthermore, the findings of this study also suggest that the persistence in shocks to volatilities is, in part, a manifestation of the time dependence in the rate of information arrival to the market

Conditional higher order moments in metal asset returns

This study examines the role of higher order moments in the returns of four important metals, aluminium, copper, gold and silver, using the asymmetric GARCH (AGARCH) model with a conditional skewed generalized-t (SGT) distribution. Implications of higher order moments in metal returns are evaluated by comparing the performances of conditional value-at-risk measures obtained from the AGARCH models with SGT distributions to those obtained from the AGARCH models with normal and student-t distributions. With the exception of gold, the AGARCH model with the SGT distribution appears to have the best fit for all metals examined.

A nonparametric investigation of seasonality in US stock market cycle durations

This study examines the durations of US stock market cycle expansions and contractions for the presence of seasonality. Specifically, it is determined whether the distributional characteristics (i.e., location and dispersion) of the durations of market expansions and contractions are dependent on the time of the year the market phase begins or ends. The duration data are obtained from a stock market chronology of monthly peak and trough dates for the period May 1835 through July 1998 and nonparametric rank‐based tests are used to test for the presence of seasonality. In order to provide some evidence on robustness with respect to the sample data, results are obtained for the entire sample period as well as for various sub‐periods. When the data are aggregated on a quarterly basis, the evidence suggests that seasonal structures are present in stock market cycle durations. These seasonals are related primarily to shifts in location over the course of the year and to when a market expansion or contraction begins. However, when the duration data are aggregated on a bi‐annual basis, support for seasonality is much more limited.