Valuing Energy Options in a One Factor Model Fitted to Forward Prices

In this paper we develop a single-factor modeling framework which is consistent with market observable forward prices and volatilities. The model is a special case of the multi-factor model developed in Clewlow and Stickland [1999b] and leads to analytical pricing formula for standard options, caps, floors, collars and swaptions. We also show how American style and exotic energy derivatives can be priced using trinomial trees, which are constructed to be consistent with the forward curve and volatility structure. We demonstrate the application of the trinomial tree to the pricing of a European and American Asian option. The analysis in this paper extends the results in Schwartz [1997] and Amin, et al. [1995].

Pricing Interest Rate Exotics in Multi-Factor Gaussian Interest Rate Models

For many interest rate exotic options, for example options on the slope of the yield curve or American featured options, a one factor assumption for term structure evolution is inappropriate. These options derive their value from changes in the slope or cuvature of the yield curve and hence are more realistically priced with multiple factor models. However, efficient construction of short rate trees becomes computationally intractable as we increase the number of factors and in particular as we move to non-Markovian models. In this paper we describe a general framework for pricing a wide range of interest rate exotic options under a very general family of multi-factor Gaussian interest rate models. Our framework is based on a computationally efficient implementation of Monte Carlo integration utilising analytical approximations as control variates. These techniques extend the analysis of Clewlow, Pang and Strickland [1997] for pricing interest rate caps and swaptions.

Parameterisation and Efficient MCMC Estimation of Non-Gaussian State Space Models

The impact of parameterisation on the simulation efficiency of Bayesian Markov chain Monte Carlo (MCMC) algorithms for two non-Gaussian state space models is examined. Specifically, focus is given to particular forms of the stochastic conditional duration (SCD) model and the stochastic volatility (SV) model, with four alternative parameterisations of each model considered. A controlled experiment using simulated data reveals that relationships exist between the simulation efficiency of the MCMC sampler, the magnitudes of the population parameters and the particular parameterisation of the state space model. Results of an empirical analysis of two separate transaction data sets for the SCD model, as well as equity and exchange rate data sets for the SV model, are also reported. Both the simulation and empirical results reveal that substantial gains in simulation efficiency can be obtained from simple reparameterisations of both types of non-Gaussian state space models.Bayesian methodology, stochastic volatility, durations, non-centred in location, non-centred in scale, inefficiency factors.

Bayesian Analysis of the Stochastic Conditional Duration Model

A Bayesian Markov Chain Monte Carlo methodology is developed for estimating the stochastic conditional duration model. The conditional mean of durations between trades is modelled as a latent stochastic process, with the conditional distribution of durations having positive support. The sampling scheme employed is a hybrid of the Gibbs and Metropolis Hastings algorithms, with the latent vector sampled in blocks. The suggested approach is shown to be preferable to the quasi-maximum likelihood approach, and its mixing speed faster than that of an alternative single-move algorithm. The methodology is illustrated with an application to Australian intraday stock market data.Transaction data, Latent factor model, Non-Gaussian state space model, Kalman filter and simulation smoother.

Environmental Self-Auditing: Setting the Proper Incentives for Discovering and Correcting Environmental Harm

Since the 1970's, the amount of environmental regulation at all levels of government has increased significantly. Major federal statutes include the Clean Air Act, Federal Water Pollution Act, Resource Conservation and Recovery Act (RCRA), Toxic Substances Control Act (ToSCA) and the Comprehensive Environmental Response, Compensation, and Liability Act (CERCLA). In addition, states have their own environmental laws and regulations. The resulting web of often highly technical requirements makes it difficult for even the regulated enterprise itself to know whether it is in compliance with applicable law. In response, many firms have instituted a policy of conducting their own "environmental audits.

Big field, small potatoes: An empirical assessment of EPA's self-audit policy

Environmental self-auditing by private firms is generally thought to both deserve and require encouragement. Firms can audit themselves more cheaply and effectively than can regulators, but too often are deterred for fear that the information they uncover will be used against them. To reduce this disincentive, the EPA's "Audit Policy" lowers punitive fines when firms promptly disclose and correct violations that they themselves discover. While some contend that the Audit Policy is inadequate, EPA touts its success, presenting as evidence the policy's track record to date. Yet our examination of that track record leads us to question EPA's conclusions. While the policy appears to have encouraged firms to self-audit in a number of instances, a comparison of the violations uncovered in these cases with those detected by standard enforcement practices suggests that the typical self-audited violation is relatively minor. For instance, cases arising under the Audit Policy are more likely to concern reporting violations, rather than emissions. The relative insignificance of self-audited violations raises a number of broader policy questions, including whether the Audit Policy could and should be revised to play a larger role in regulatory enforcement

Distribution network reconfiguration validation with uncertain loads - network configuration determination and application

Automatic load transfer (ALT) on the 11 kV network is the process by which circuit breakers on the network are switched to form open points in order to feed load from different primary substations. Some of the potential benefits that may be gained from dynamically using ALT include maximising utilisation of existing assets, voltage regulation and reduced losses. One of the key issues, that has yet to be properly addressed in published research, is how to validate that the modelled benefits really exist. On an 11 kV distribution network where the load is continually changing and the load on each distribution substation is unlikely to be monitored - reduction in losses from moving the normally open point is particularly difficult to prove. This study proposes a method to overcome this problem and uses measured primary feeder data from two parts of the Western Power Distribution 11 kV Network under different configurations. The process of choosing the different configurations is based on a heuristic modelling method of locating minimum voltages to help reduce losses

Radar Detection of High Concentrations of Ice Particles - Methodology and Preliminary Flight Test Results

High Ice Water Content (HIWC) has been identified as a primary causal factor in numerous engine events over the past two decades. Previous attempts to develop a remote detection process utilizing modern commercial radars have failed to produce reliable results. This paper discusses the reasons for previous failures and describes a new technique that has shown very encouraging accuracy and range performance without the need for any hardware modifications to industrys current radar designs. The performance of this new process was evaluated during the joint NASA/FAA HIWC RADAR II Flight Campaign in August of 2018. Results from that evaluation are discussed, along with the potential for commercial application, and development of minimum operational performance standards for a future commercial radar product

Nitrate toxicity in livestock

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