Investigating the stationarity of insurance premiums: international evidence

This article explores whether there is support for the stationarity hypotheses of life and non-life insurance premiums during the period 1979–2007 for 40 heterogeneous countries. The stationarity of insurance premiums affects insurance companies’ prediction on their future inflow of premium income, which affects the liquidity of insurance companies and their investment plans and thus is relevant to the insurers’ operation. This article employs the advanced nonlinear panel unit-root test with a sequential panel selection method to classify the entire panel into two groups: stationary countries and non-stationary countries. We apply Monte Carlo simulations to derive empirical distributions of the test, which allows us to correct for the finite-sample bias and to consider the cross-country effects. We find relatively stationary life insurance premiums in countries from the following groups: high-income, Europe, and common law origin; relatively stationary non-life insurance premiums exist in the following groups: low-income, Middle East and Africa, and common law origin. Evidence herein shows that different classifications, including income levels, geographic regions, regionally or economically integrated blocs, and legal system, affect the stationarity of life and non-life insurance premiums.     

Lognormal Approximation of Complex Path-Dependent Pension Scheme Payoffs

Abstract

This paper analyzes an explicit return smoothing mechanism which has recently been introduced as part of a new type of pension savings contract that has been offered by Danish life insurers. We establish the payoff function implied by the return smoothing mechanism and show that its probabilistic properties are accurately approximated by a suitably adapted lognormal distribution. The quality of the lognormal approximation is explored via a range of simulation-based numerical experiments, and we point to several other potential practical applications of the paper's theoretical results.     

Least-squares Importance Sampling for Monte Carlo security pricing

We describe a simple Importance Sampling strategy for Monte Carlo simulations based on a least-squares optimization procedure. With several numerical examples, we show that such Least-squares Importance Sampling (LSIS) provides efficiency gains comparable to the state-of-the-art techniques, for problems that can be formulated in terms of the determination of the optimal mean of a multivariate Gaussian distribution. In addition, LSIS can be naturally applied to more general Importance Sampling densities and is particularly effective when the ability to adjust higher moments of the sampling distribution, or to deal with non-Gaussian or multi-modal densities, is critical to achieve variance reductions.     

A new technique for calibrating stochastic volatility models: the Malliavin gradient method

We discuss the application of gradient methods to calibrate mean reverting stochastic volatility models. For this we use formulas based on Girsanov transformations as well as a modification of the Bismut–Elworthy formula to compute the derivatives of certain option prices with respect to the parameters of the model by applying Monte Carlo methods. The article presents an extension of the ideas to apply Malliavin calculus methods in the computation of Greek's.     

The affine Heston model with correlated Gaussian interest rates for pricing hybrid derivatives

In this article we define a multi-factor equity–interest rate hybrid model with non-zero correlation between the stock and interest rate. The equity part is modeled by the Heston model and we use a Gaussian multi-factor short-rate process. By construction, the model fits in the framework of affine diffusion processes, allowing fast calibration to plain vanilla options. We also provide an efficient Monte Carlo simulation scheme.     

A closer look at return predictability of the US stock market: evidence from new panel variance ratio tests

This paper examines the return predictability of the US stock market using portfolios sorted by size, book-to-market ratio and industry. We use novel panel variance ratio tests, based on the wild bootstrap proposed in this paper, which exhibit desirable size and power properties in small samples. We have found evidence that stock returns have been highly predictable from 1964 to 1996, except for a period leading to the 1987 crash and its aftermath. After 1997, stock returns have been unpredictable overall. At a disaggregated level, we find evidence that large-cap portfolios have been priced more efficiently than small- or medium-cap portfolios; and that the stock returns from high-tech industries are far less predictable than those from non-high-tech industries.     

基于Copula函数和蒙特卡洛模拟方法的权证定价

在分析计算单一权证价格的基础上,提出了利用Copula函数和蒙特卡洛模拟的方法来计算多种权证的定价模型。具体实例分析表明,该方法可为投资者提供有益的投资决策参考。     

Testing for negativity in a demand system: A Bayesian approach

The paper introduces Bayesian inference into a demand model. This allows us to test for the negativity condition of the substitution matrix which is difficult to handle directly in the traditional approach. To illustrate the Bayesian inference procedures, we estimate the Rotterdam model and test the demand properties using Japanese data. The empirical results show the importance of specifically considering negativity in demand analysis. First version received: September 1997/final version received: February 1998     

Testing for financial contagion based on a nonparametric measure of the cross‐market correlation

When contagion is defined as a significant increase in market comovement after a shock to one country, we propose a test for financial contagion based on a nonparametric measure of the cross-market correlation. Monte Carlo simulation studies show that our test has reasonable size and good power to detect financial contagion, and that Forbes and Rigobon's test (2002) is relatively conservative, indicating that their test tends not to find evidence of contagion when it does exist. Applying our test to investigate contagion from the 1997 East Asian crisis and the 2007 Subprime crisis, we find that there existed international financial contagion from the two financial crises.     

Spillovers in Exchange Rates and the Effects of Global Shocks on Emerging Market Currencies

This paper provides an investigation into the spillover effects of exchange rate returns and volatility for developed and emerging market currencies, using data from 1997 to 2011. The results suggest that spillovers in exchange rate returns have increased steadily over time, in moderate reaction to economic events. In contrast, spillovers in total observed volatility (measured by squared returns) react more strongly to economic events, and this transmission has remained at a relatively high level since the global financial crisis. Furthermore, over the course of time, global shocks would appear to account for a larger proportion of aggregate exchange rate volatility (and the relative importance of domestic shocks has declined). The paper also considers whether the increase in volatility spillover is due to sudden shocks, or whether it is due to changes in the stochastic trend of the underlying volatility process. The results suggests that in most cases, this increase is due to sudden shocks, however, in certain instances country‐specific events may perpetuate changes to the trend of the underlying volatility spillover.