The Underpricing and Excess Returns of Initial Public Offerings in Taiwan Based on Noisy Trading: A Stochastic Frontier Model

Initial public offerings (IPOs) are typically offered at prices lower than the transaction price in the early aftermarket. With a stochastic frontier model, we measured the fair offer price of an IPO and then the deliberate IPO underpricing and the market misvaluation based on the estimated fair offer price. Our results show that IPOs are deliberately underpriced. The extent of noisy trading leading to significantly higher market transaction prices explains the excess IPO returns. We conclude that initial IPO returns result primarily from the noisy trading activities instead of the deliberate IPO underpricing.     

Valuation of commodity derivatives in a new multi-factor model

This paper extends existing commodity valuation models to allow for stochastic volatility and simultaneous jumps in the spot price and spot volatility. Closed-form valuation formulas for forwards, futures, futures options, geometric Asian options and commodity-linked bonds are obtained using the Heston (1993) and Bakshi and Madan (2000) methodology. Stochastic volatility and jumps do not affect the futures price at a given point in time. However, numerical examples indicate that they play important roles in pricing options on futures. This revised version was published online in June 2006 with corrections to the Cover Date.     

Planning Your Own Debt

We model the Danish market for mortgage backed securities with a two-factor interest rate model and use a stochastic programming approach to analyse how an individual home-owner should initially compose and subsequently readjust his mortgage in an optimal way. Results show that the 'rules of thumb' used by financial institutions are reasonable, although best suited for more aggressive mortgagors, for whom the delivery option is of some value. More risk-averse investors should also re-adjust frequently, but use more diversified portfolios. Results are insensitive to whether a one- or two-factor model is used, provided the former is suitably calibrated.     

A Closer Look at the Relation between GARCH and Stochastic Autoregressive Volatility

We show that, for three common SARV models, fitting a minimummean square linear filter is equivalent to fitting a GARCH model.This suggests that GARCH models may be useful for filtering,forecasting, and parameter estimation in stochastic volatilitysettings. To investigate, we use simulations to evaluate howthe three SARV models and their associated GARCH filters performunder controlled conditions and then we use daily currency andequity index returns to evaluate how the models perform in arisk management application. Although the GARCH models produceless precise forecasts than the SARV models in the simulations,it is not clear that the performance differences are large enoughto be economically meaningful. Consistent with this view, wefind that the GARCH and SARV models perform comparably in testsof conditional value-at-risk estimates using the actual data.     

Time diversification and security preferences: A stochastic dominance analysis

We use stochastic dominance to test whether investor should prefer riskier securities as the investment horizon lengthens. Return distributions for stocks, bonds, and U.S. Treasury bills are generated for holding periods of one to 25 years by simulation. For each holding period, stochastic dominance tests are run to establish preferences between the alternative security classes. Contrary to previous mean-variance based studies, we find no evidence that high-risk securities (stocks) dominate low-risk securities (bonds, Treasury bills) as the investment horizon lengthens. However, we do find that corporate bonds systematically dominate government bonds.     

Optimal portfolio choice in the bond market

We consider the Merton problem of optimal portfolio choice when the traded instruments are the set of zero-coupon bonds. Working within a Markovian Heath–Jarrow–Morton model of the interest rate term structure driven by an infinite-dimensional Wiener process, we give sufficient conditions for the existence and uniqueness of an optimal trading strategy. When there is uniqueness, we provide a characterization of the optimal portfolio as a sum of mutual funds. Furthermore, we show that a Gauss–Markov random field model proposed by Kennedy [Math. Financ. 4, 247–258(1994)] can be treated in this framework, and explicitly calculate the optimal portfolio. We show that the optimal portfolio in this case can be identified with the discontinuities of a certain function of the market parameters.     

Measuring Inflation Expectations Uncertainty Using High‐Frequency Data

Inflation expectations play a key role in determining future economic outcomes. The associated uncertainty provides a direct gauge of how well‐anchored the inflation expectations are. We construct a model‐based measure of inflation expectations uncertainty by augmenting a standard unobserved components model of inflation with information from noisy and possibly biased measures of inflation expectations obtained from financial markets. This new model‐based measure of inflation expectations uncertainty is more accurately estimated and can provide valuable information for policymakers. Using U.S. data, we find significant changes in inflation expectations uncertainty during the Great Recession.     

Machine learning in individual claims reserving

Machine learning techniques make it feasible to calculate claims reserves on individual claims data. This paper illustrates how these techniques can be used by providing an explicit example in individual claims reserving.     

Automatic balancing mechanisms for notional defined contribution accounts in the presence of uncertainty

The notional defined contribution model combines pay-as-you-go financing and a defined contribution pension formula. This paper aims to demonstrate the extent to which liquidity and solvency indicators are affected by fluctuations in economic and demographic conditions and to explore the introduction of an automatic balancing mechanism (ABM) into the pension scheme. We demonstrate that the introduction of an ABM reduces the volatility of the buffer fund and that, in most cases, the automatic mechanism that re-establishes solvency produces the highest value of the risk-adjusted notional factor.     

Expected exponential utility maximization of insurers with a Linear Gaussian stochastic factor model

In this paper, we consider the problem of optimal investment by an insurer. The wealth of the insurer is described by a Cramér–Lundberg process. The insurer invests in a market consisting of a bank account and m risky assets. The mean returns and volatilities of the risky assets depend linearly on economic factors that are formulated as the solutions of linear stochastic differential equations. Moreover, the insurer preferences are exponential. With this setting, a Hamilton–Jacobi–Bellman equation that is derived via a dynamic programming approach has an explicit solution found by solving the matrix Riccati equation. Hence, the optimal strategy can be constructed explicitly. Finally, we present some numerical results related to the value function and the ruin probability using the optimal strategy.