Guaranteed Annuity Options (GAOs) are options available to holders of certain pension policies. Under these contracts, policyholders contribute premiums into a fund managed by the insurer. At retirement, the policyholders buy life annuities at a guaranteed rate provided by the original insurer, or annuitize with another insurer. If the guaranteed annuity rates are better than the prevailing rat...

Credit derivatives are potentially one of the most important new types of financial products introduced during the past decade. Many investors have portfolios with values that ar higWy sensitive to shifts in the spread between risky and riskless yields, and credit derivatives offer these investors an important new tool for managing and hedging their exposure to this type of risk. In this articl...

A survey on derivative usage and financial risk management in New Zealand shows that the currency forward is the most frequently used derivatives in hedging transaction exposure. This paper examines whether forwards performs better than over-the-counter option for a New Zealand exporter in hedging NZD/USD transaction exposure. This research adopts H sin, Kuo and Lee’s (1994) model of hedging ef...

A new analytical approximation tool, derived from the classical PDEs’ theory, is introduced in order to build approximate transition densities of diffusions. The tool is useful for approximate pricing and hedging of financial derivatives, and for maximum likelihood and method of moments estimates of diffusion parameters. The approximation is uniform with respect to time and space variables. Mor...

This study presents a deep reinforcement learning approach for global hedging of long-term financial derivatives. A similar setup as in Coleman et al. (2007) is considered with the risk management lookback options embedded guarantees variable annuities ratchet features. The algorithm Buehler (2019a) applied to optimize neural networks representing policies both quadratic and non-quadratic penal...

Financial models are largely used in option pricing. These physical models capture several salient features of asset price dynamics. The pricing performance can be significantly enhanced when they are combined with nonparametric learning approaches, that empirically learn and correct pricing errors through estimating state price distributions. In this paper, we propose a new semiparametric meth...