TIME‐VARYING VOLATILITY IN CANADIAN AND U.S. STOCK INDEX AND INDEX FUTURES MARKETS: A MULTIVARIATE ANALYSIS

We use a multivariate generalized autoregressive heteroskedasticity model (M‐GARCH) to examine three stock indexes and their associated futures prices: the New York Stock Exchange Composite, S&P 500, and Toronto 35. The North American context is significant because markets in Canada and the United States share similar structures and regulatory environments. Our model allows examination of dependence in volatility as it captures time variation in volatility and cross‐market influences. Estimated time variation in volatility is significant, and the volatilities are highly positively correlated. Yet, we find that the correlation in North American index and futures markets has declined over time.     

It’s About Time: An Examination of Loss Reserve Development Time Horizons

A rich body of academic research has addressed the question of earnings management in the property-casualty insurance industry via manipulation of loss reserve estimates. This study analyzes the variability of reserve estimates at different development horizons to determine whether the predominant practice of relying on five years of development is appropriate. We examine two common measures of reserve estimation error, calendar year development and accident year development, and compare and contrast the two approaches. We also consider the appropriateness of the common practice of aggregating lines of business. After examining reserve development patterns for each of the major lines of business, we conclude that the appropriate development horizon to adequately establish ultimate liability may be longer than the current maximum reported horizon of 10 years found in Schedule P for most lines of business, including the aggregate reserves. Although longer-term development horizons are necessary to establish insurers’ ultimate liability, relatively short-term development horizons may be more appropriate when attempting to identify deliberate manipulations or to assess solvency risk, where the short-term variations are the primary object of interest. Ultimately, this article investigates the degree to which methodology originally developed for estimating loss reserve errors is appropriate today, in particular, relative to current data availability.     

Would a Flat‐Rate Tax Stimulate Entrepreneurship in Germany? A Behavioural Microsimulation Analysis Allowing for Risk*

When potential income tax reforms are debated, the suspected impact on entrepreneurship is often used as an argument in favour of or against a certain policy. Quantitative ex‐ante evaluations of the effect of certain tax reform options on entrepreneurship are very rare, however. This paper estimates the ex‐ante effects of the German tax reform 2000 and of two hypothetical flat‐rate tax scenarios on entries into and exits out of self‐employment based on a structural microsimulation model with econometrically estimated transition rates under risk. The simulation results indicate that flatter tax systems do not encourage people to choose self‐employment, but rather discourage them from doing so. This is explained by the reduction of entrepreneurs' income risk through progressive taxation.     

The Local Whittle Estimator of Long-Memory Stochastic Volatility

We propose a new semiparametric estimator of the degree of persistencein volatility for long memory stochastic volatility (LMSV) models.The estimator uses the periodogram of the log squared returnsin a local Whittle criterion which explicitly accounts for thenoise term in the LMSV model. Finite-sample and asymptotic standarderrors for the estimator are provided. An extensive simulationstudy reveals that the local Whittle estimator is much lessbiased and that the finite-sample standard errors yield moreaccurate confidence intervals than the widely-used GPH estimator.The estimator is also found to be robust against possible leverageeffects. In an empirical analysis of the daily Deutsche Mark/USDollar exchange rate, the new estimator indicates stronger persistencein volatility than the GPH estimator, provided that a largenumber of frequencies is used.