Electricity Price Modelling with a Regime Switching Volatility

We present a methodology to model electricity price dynamics by applying the interest rate theory toolkit. We construct the electricity market following [16] and applying the Heath, Jarrow and Morton ([7]) model. The electricity returns forward curve evolution using the Regime Switching Volatility is the instrument chosen to reflect into a simulating model the natural seasonality of electricity prices. The model calibration and the volatility parameters estimation allow to simulate in a realistic way the future electricity prices.

Long run analysis of crude oil portfolios

This paper deals with the analysis of the long-run behavior of a set of mispricing portfolios generated by three crude oils, where one of the oils is the reference commodity and it is compared to a combination of the other two ones. To this aim, the long-term parameter related to the mispricing portfolio are estimated on empirical data. We pay particular attention to the cases of mispricing portfolios either of stationary type or following a Brownian motion: the former situation is associated to replication portfolios of a reference commodity; the latter one allows to implement forecasts. The theoretical setting is validated through empirical data on WTI, Brent and Dubai oils

Modelling the Evolution of Credit Spreads Using the Cox Process Within the HJM Framework A CDS Option Pricing Model

In this paper a simulation approach for defaultable yield curves is developed within the Heath et al. (1992) framework. The default event is modelled using the Cox process where the stochastic intensity represents the credit spread. The forward credit spread volatility function is affected by the entire credit spread term structure. The paper provides the defaultable bond and credit default swap option price in a probability setting equipped with a sub filtration structure. The Euler-Maruyama stochastic integral approximation and the Monte Carlo method are applied to develop a numerical algorithm for pricing. Finally, the Antithetic Variable technique is used to reduce the variance of credit default swap option prices.pricing; HJM model; Cox process; Monte Carlo method; CDS option

Modelling the Evolution of Credit Spreads using the Cox Process within the HUM Framework: A CDS Option Pricing Model

In this paper a simulation approach for defaultable yield curves is developed within the Heath et al. (1992) framework. The default event is modelled using the Cox process where the stochastic intensity represents the credit spread. The forward credit spread volatility function is affected by the entire credit spread term structure. The paper provides the defaultable bond and CDS option price in a probability setting equipped with a subfiltration structure. The Euler-Maruyama stochastic integral approximation and the Monte Carlo method are applied to develop a numerical algorithm for pricing. Finally, the Antithetic Variables technique is used to reduce the variance of CDSO estimations.HJM model; Cox process; Monte Carlo method; bond price; CDS option

Why did CPDOs fail? An analysis focused on credit spread modeling

In this paper we propose a model to evaluate the performance of a Constant Proportion Debt Obligation (CPDO) and assess its rating. We model credit spread evolution in a HJM framework and default events for CPDO are generated by using a reduced form approach. Implementing a numerical algorithm that simulates the strategy of a CPDO, we obtain a rating for CPDO by using Monte Carlo simulations. We find a rating inferior to the one assigned by rating agencies. Using our model for credit spread dynamics, the revealed default probability for CPDO could have been predicted.

A hidden Markov model for statistical arbitrage in international crude oil futures markets

In this work, we study statistical arbitrage strategies in international crude oil futures markets. We analyse strategies that extend classical pairs trading strategies, considering the two benchmark crude oil futures (Brent and WTI) together with the newly introduced Shanghai crude oil futures. We document that the time series of these three futures prices are cointegrated and we model the resulting cointegration spread by a mean-reverting regime-switching process modulated by a hidden Markov chain. By relying on our stochastic model and applying online filter-based parameter estimators, we implement and test a number of statistical arbitrage strategies. Our analysis reveals that statistical arbitrage strategies involving the Shanghai crude oil futures are profitable even under conservative levels of transaction costs and over different time periods. On the contrary, statistical arbitrage strategies involving the three traditional crude oil futures (Brent, WTI, Dubai) do not yield profitable investment opportunities. Our findings suggest that the Shanghai futures, which has already become the benchmark for the Chinese domestic crude oil market, can be a valuable asset for international investors

Liquid Chromatography-Tandem Mass Spectrometry Analysis of Perfluorooctane Sulfonate and Perfluorooctanoic Acid in Fish Fillet Samples

Perfluorooctane sulfonate (PFOS) and perfluorooctanoic (PFOA) acid are persistent contaminants which can be found in environmental and biological samples. A new and fast analytical method is described here for the analysis of these compounds in the edible part of fish samples. The method uses a simple liquid extraction by sonication, followed by a direct determination using liquid chromatography-tandem mass spectrometry (LC-MS/MS). The linearity of the instrumental response was good, with average regression coefficients of 0.9971 and 0.9979 for PFOS and PFOA, respectively, and the coefficients of variation (CV) of the method ranged from 8% to 20%. Limits of detection (LOD) were 0.04 ng/g for both the analytes and recoveries were 90% for PFOS and 76% for PFOA. The method was applied to samples of homogenized fillets of wild and farmed fish from the Mediterranean Sea. Most of the samples showed little or no contamination by perfluorooctane sulfonate and perfluorooctanoic acid, and the highest concentrations detected among the fish species analyzed were, respectively, 5.96 ng/g and 1.89 ng/g. The developed analytical methodology can be used as a tool to monitor and to assess human exposure to perfluorinated compounds through sea food consumption

Targeted and untargeted quantification of quorum sensing signalling molecules in bacterial cultures and biological samples via HPLC-TQ MS techniques

Quorum sensing (QS) is the ability of some bacteria to detect and to respond to population density through signalling molecules. QS molecules are involved in motility and cell aggregation mechanisms in diseases such as sepsis. Few biomarkers are currently available to diagnose sepsis, especially in high-risk conditions. The aim of this study was the development of new analytical methods based on liquid chromatography-mass spectrometry for the detection and quantification of QS signalling molecules, including N-acyl homoserine lactones (AHL) and hydroxyquinolones (HQ), in biofluids. Biological samples used in the study were Pseudomonas aeruginosa bacterial cultures and plasma from patients with sepsis. We developed two MS analytical methods, based on neutral loss (NL) and product ion (PI) experiments, to identify and characterize unknown AHL and HQ molecules. We then established a multiple-reaction-monitoring (MRM) method to quantify specific QS compounds. We validated the HPLC-MS-based approaches (MRM-NL-PI), and data were in accord with the validation guidelines. With the NL and PI MS-based methods, we identified and characterized 3 and 13 unknown AHL and HQ compounds, respectively, in biological samples. One of the newly found AHL molecules was C12-AHL, first quantified in Pseudomonas aeruginosa bacterial cultures. The MRM quantitation of analytes in plasma from patients with sepsis confirmed the analytical ability of MRM for the quantification of virulence factors during sepsis

Mean-Reverting Statistical Arbitrage Strategies in Crude Oil Markets

In this paper, we introduce the concept of statistical arbitrage through the definition of a mean-reverting trading strategy that captures persistent anomalies in long-run relationships among assets. We model the statistical arbitrage proceeding in three steps: (1) to identify mispricings in the chosen market, (2) to test mean-reverting statistical arbitrage, and (3) to develop statistical arbitrage trading strategies. We empirically investigate the existence of statistical arbitrage opportunities in crude oil markets. In particular, we focus on long-term pricing relationships between the West Texas Intermediate crude oil futures and a so-called statistical portfolio, composed by other two crude oils, Brent and Dubai. Firstly, the cointegration regression is used to track the persistent pricing equilibrium between the West Texas Intermediate crude oil price and the statistical portfolio value, and to identify mispricings between the two. Secondly, we verify that mispricing dynamics revert back to equilibrium with a predictable behaviour, and we exploit this stylized fact by applying the trading rules commonly used in equity markets to the crude oil market. The trading performance is then measured by three specific profit indicators on out-of-sample data