Frequently, dynamic hedging strategies minimizing risk exposure are not given in closed form, but need to be approximated numerically. This makes it difficult to estimate residual hedging risk, also called basis risk, when only imperfect hedging instruments are at hand. We propose an easy to...

When managing risk, frequently only imperfect hedging instruments are at hand. We show how to optimally <italic>cross-hedge</italic> risk when the spread between the hedging instrument and the risk is <italic>stationary</italic>. For linear risk positions we derive explicit formulas for the hedge error, and for nonlinear...

We reveal pitfalls in the hedging of insurance contracts with a minimum return guarantee on the underlying investment, e.g. an external mutual fund. We analyze basis risk entailed by hedging the guarantee with a dynamic portfolio of proxy assets for the funds. We also take account of liquidity...

In recent years, with the availability of high-frequency financial market data modeling realized volatility has become a new and innovative research direction. The construction of “observable” or realized volatility series from intra-day transaction data and the use of standard time-series...

Using unobservable conditional variance as measure, latentvariable approaches, such as GARCH and stochasticvolatility models, have traditionally been dominating the empirical finance literature. In recent years, with the availability of highfrequency financial market data modeling realized...

In this note we provide an operational interpretation of the economic index of riskiness of Aumann and Serrano (2008) and discuss its existence in the case of non-finite gambles.

We develop an empirically highly accurate discrete-time daily stochastic volatility model that explicitly distinguishes between the jump and continuous-time components of price movements using nonparametric realized variation and Bipower variation measures constructed from high-frequency...

We develop an empirically highly accurate discrete-time daily stochastic volatility model that explicitly distinguishes between the jump and continuous time components of price movements using nonparametric realized variation and Bipower variation measures constructed from high-frequency...

Using unobservable conditional variance as measure, latent–variable approaches, such as GARCH and stochastic–volatility models, have traditionally been dominating the empirical finance literature. In recent years, with the availability of high–frequency financial market data modeling...

We develop an empirically highly accurate discrete-time daily stochastic volatility model that explicitly distinguishes between the jump and continuoustime components of price movements using nonparametric realized variation and Bipower variation measures constructed from high-frequency intraday...