Ambiguous volatility and asset pricing in continuous time

This paper formulates a model of utility for a continuous time framework that captures the decision-maker's concern with ambiguity about both volatility and drift. Corresponding extensions of some basic results in asset pricing theory are presented. First, we derive arbitrage-free pricing rules based on hedging arguments. Ambiguous volatility implies market incompleteness that rules out perfect hedging. Consequently, hedging arguments determine prices only up to intervals. However, sharper predictions can be obtained by assuming preference maximization and equilibrium. Thus we apply the model of utility to a representative agent endowment economy to study equilibrium asset returns. A version of the C-CAPM is derived and the effects of ambiguous volatility are described

Dynamic Hedging Using Generated Genetic Programming Implied Volatility Models

The purpose of this paper is to improve the accuracy of dynamic hedging using implied volatilities generated by genetic programming. Using real data from S&P500 index options, the genetic programming's ability to forecast Black and Scholes implied volatility is compared between static and dynamic training-subset selection methods. The performance of the best generated GP implied volatilities is tested in dynamic hedging and compared with Black-Scholes model. Based on MSE total, the dynamic training of GP yields better results than those obtained from static training with fixed samples. According to hedging errors, the GP model is more accurate almost in all hedging strategies than the BS model, particularly for in-the-money call options and at-the-money put options.Comment: 32 pages,13 figures, Intech Open Scienc

Risk Management for Swedish Farmers - An empirical study on hedge ratios for Swedish wheat

The paper investigates data on purchasing price of wheat from Swedish grain buyer Lantmännen and MATIF future contracts on milling wheat in an attempt to replicate the conditions for a Swedish farmer trying to manage his risk on wheat by trading future contracts on the MATIF exchange. Two static linear regressions and four dynamic GARCH models are employed on a sample of 1679 daily returns and 339 weekly returns ranging from 2009-07-01 to 2016-01-11. All regressions are ran on both daily returns and weekly returns to investigate how the rebalancing frequency changes the outcome of the hedges. The correlation of spot and future price changes from 0.19 for daily returns to 0.49 for weekly returns and all weekly return hedges outperforms the daily hedges in variance reduction. It is however hard to find a general best model over both daily and weekly returns and for all samples. The simple OLS performs best in the daily sample with -3.19% in variance over the full sample compared to a no-hedge and in the weekly return the VECM-VECH reduces variance by -29% over the full sample compared to a no-hedge

Electricity derivative markets : investment valuation, production planning and hedging

This thesis studies electricity derivative markets from a view point of an electricity producer. The traditionally used asset pricing methods, based on the no arbitrage principle, are extended to take into account electricity specific features: the non storability of electricity and the variability in the load process. The sources of uncertainty include electricity forward curve, prices of resources used to generate electricity, and the size of the future production. Also the effects of competitors' actions are considered. The thesis illustrates how the information in the derivative prices can be used in investment and production planning. In addition, the use of derivatives as a tool to stabilize electricity dependent cash flows is considered. The results indicate that the information about future electricity prices and their uncertainty, obtained from derivative markets, is important in investment analysis and production planning.reviewe