Pricing and Hedging Illiquid Energy Derivatives:an Application to the JCC Index

In this paper we discuss a simple econometric strategy for pricing and hedging illiquid financial products, such as the Japanese crude oil cocktail (JCC) index, the most popular OTC energy derivative in Japan. First, we review the existing literature for computing optimal hedge ratios (OHR) and we propose a critical classification of the existing approaches. Second, we compare the empirical performance of different econometric models (namely, regression models in price-levels, price first differences, price returns, as well as error correction and autoregressive distributed lag models) in terms of their computed OHR using monthly data on the JCC over the period January 2000-January 2006. Third, we illustrate and implement a procedure to cross-hedge and price two different swaps on the JCC: a one-month swap and a three-month swap with a variable oil volume. We explain how to compute a bid/ask spread and to construct the hedging position for the JCC swap. Fourth, we evaluate our swap pricing scheme with backtesting and rolling regression techniques. Our empirical findings show that it is not necessary to use sophisticated econometric techniques, since the price level regression model permits to compute a more reliable optimal hedge ratio relative to its competing alternatives.Hedging Models, Cross-Hedging, Energy Derivatives, Illiquid Financial Products, Commodity Markets, JCC Price Index

Industrial Coal Demand in China: A Provincial Analysis

The general concern on the environmental implications of the rising demand for coal registered in China during the last few years has induced considerable research effort to produce accurate forecasts of China’s energy requirements. Nevertheless, no previous study has modelled the coal demand in China at provincial level. The aim of this paper is twofold. First, we estimate and forecast the Chinese demand for coal using panel data disaggregated by provinces and accounting for spatial heterogeneity. Second, given the spatial nature of the data, we explicitly capture the spatial autocorrelation among provinces using spatial econometrics. In particular, we specify the Chinese industrial coal demand at provincial level with a fixed-effect spatial lag model and a fixed-effect spatial error model. The fixedeffect spatial lag model seems to better capture the existing interdependence between provinces. This model forecasts an average annual increase in coal demand to 2010 of 4 percent.Energy demand in China, Coal demand in China, Chinese provinces, Panel data; Spatial econometrics, Forecasting

Oil Price Forecast Evaluation with Flexible Loss Functions

The empirical literature is very far from any consensus about the appropriate model for oil price forecasting that should be implemented. Relative to the previous literature, this paper is novel in several respects. First of all, we test and systematically evaluate the ability of several alternative econometric specifications proposed in the literature to capture the dynamics of oil prices. Second, we analyse the effects of different data frequencies on the coefficient estimates and forecasts obtained using each selected econometric specification. Third, we compare different models at different data frequencies on a common sample and common data. Fourth, we evaluate the forecasting performance of each selected model using static forecasts, as well as different measures of forecast errors. Finally, we propose a new class of models which combine the relevant aspects of the financial and structural specifications proposed in the literature (“mixed” models). Our empirical findings suggest that, irrespective of the shape of the loss function, the class of financial models is to be preferred to time series models. Both financial and time series models are better than mixed and structural models. Results of the Diebold and Mariano test are not conclusive, for the loss differential seems to be statistically insignificant in the large majority of cases. Although the random walk model is not statistically outperformed by any of the alternative models, the empirical findings seem to suggest that theoretically well-grounded financial models are valid instruments for producing accurate forecasts of the WTI spot price.Oil Price, WTI Spot and Futures Prices, Forecasting, Econometric Models

Evaluating the Empirical Performance of Alternative Econometric Models for Oil Price Forecasting

The relevance of oil in the world economy explains why considerable effort has been devoted to the development of different types of econometric models for oil price forecasting. Several specifications have been proposed in the economic literature. Some are based on financial theory and concentrate on the relationship between spot and futures prices (“financial” models). Others assign a key role to variables explaining the characteristics of the physical oil market (“structural” models). The empirical literature is very far from any consensus about the appropriate model for oil price forecasting that should be implemented. Relative to the previous literature, this paper is novel in several respects. First of all, we test and systematically evaluate the ability of several alternative econometric specifications proposed in the literature to capture the dynamics of oil prices. Second, we analyse the effects of different data frequencies on the coefficient estimates and forecasts obtained using each selected econometric specification. Third, we compare different models at different data frequencies on a common sample and common data. Fourth, we evaluate the forecasting performance of each selected model using static and dynamic forecasts, as well as different measures of forecast errors. Finally, we propose a new class of models which combine the relevant aspects of the financial and structural specifications proposed in the literature (“mixed” models). Our empirical findings can be summarized as follows. Financial models in levels do not produce satisfactory forecasts for the WTI spot price. The financial error correction model yields accurate in-sample forecasts. Real and strategic variables alone are insufficient to capture the oil spot price dynamics in the forecasting sample. Our proposed mixed models are statistically adequate and exhibit accurate forecasts. Different data frequencies seem to affect the forecasting ability of the models under analysis.Oil Price, WTI Spot And Futures Prices, Forecasting, Econometric Models

Secretome protein signature of human pancreatic cancer stem-like cells

Emerging research has demonstrated that pancreatic ductal adenocarcinoma (PDAC) contains a sub-population of cancer stem cells (CSCs) characterized by self-renewal, anchorage-independent-growth, long-term proliferation and chemoresistance. The secretome analysis of pancreatic CSCs has not yet been performed, although it may provide insight into tumour/microenvironment interactions and intracellular processes, as well as to identify potential biomarkers. To characterize the secreted proteins of pancreatic CSCs, we performed an iTRAQ-based proteomic analysis to compare the secretomes of Panc1 cancer stem-like cells (Panc1 CSCs) and parental cell line. A total of 72 proteins were found up-/down-regulated in the conditioned medium of Panc1 CSCs. The pathway analysis revealed modulation of vital physiological pathways including glycolysis, gluconeogenesis and pentose phosphate. Through ELISA immunoassays we analysed the presence of the three proteins most highly secreted by Panc1 CSCs (ceruloplasmin, galectin-3, and MARCKS) in sera of PDAC patient. ROC curve analysis suggests ceruloplasmin as promising marker for patients negative for CA19-9.Overall, our study provides a systemic secretome analysis of pancreatic CSCs revealing a number of secreted proteins which participate in pathological conditions including cancer differentiation, invasion and metastasis. They may serve as a valuable pool of proteins from which biomarkers and therapeutic targets can be identified. Biological significance: The secretome of CSCs is a rich reservoir of biomarkers of cancer progression and molecular therapeutic targets, and thus is a topic of great interest for cancer research. The secretome analysis of pancreatic CSCs has not yet been performed. Recently, our group has demonstrated that Panc-CSCs isolated from parental cell line by using the CSC selective medium, represent a model of great importance to deepen the understanding of the biology of pancreatic adenocarcinoma. To our knowledge, this is the first proteomic study of pancreatic CSC secretome. We performed an iTRAQ-based analysis to compare the secretomes of Panc1 CSCs and Panc1 parental cell line and identified a total of 43 proteins secreted at higher level by pancreatic cancer stem cells. We found modulation of different vital physiological pathways (such as glycolysis and gluconeogenesis, pentose phosphate pathway) and the involvement of CSC secreted proteins (for example 72 kDa type IV collagenase, galectin-3, alpha-actinin-4, and MARCKS) in pathological conditions including cancer differentiation, invasion and metastasis. By ELISA verification we found that MARCKS and ceruloplasmin discriminate between controls and PDAC patients; in addition ROC curve analyses indicate that MARCKS does not have diagnostic accuracy, while ceruloplasmin could be a promising marker only for patients negative for CA19-9.We think that the findings reported in our manuscript advance the understanding of the pathways implicated in tumourigenesis, metastasis and chemoresistance of pancreatic cancer, and also identify a pool of proteins from which novel candidate diagnostic and therapeutic biomarkers could be discovered

Pancreatic ductal adenocarcinoma cell lines display a plastic ability to bi‑directionally convert into cancer stem cells

Pancreatic ductal adenocarcinoma (PDAC) is often diagnosed when metastatic events have occurred. Cancer stem cells (CSCs) play an important role in tumor initiation, metastasis, chemoresistance and relapse. A growing number of studies have suggested that CSCs exist in a dynamic equilibrium with more differentiated cancer cells via a bi‑directional regeneration that is dependent on the environmental stimuli. In this investigation, we obtain, by using a selective medium, PDAC CSCs from five out of nine PDAC cell lines, endowed with different tumorsphere‑forming ability. PDAC CSCs were generally more resistant to the action of five anticancer drugs than parental cell lines and were characterized by an increased expression of EpCAM and CD44v6, typical stem cell surface markers, and a decreased expression of E‑cadherin, the main marker of the epithelial state. PDAC CSCs were able to re‑differentiate into parental cells once cultured in parental growth condition, as demonstrated by re‑acquisition of the epithelial morphology, the decreased expression levels of EpCAM and CD44v6 and the increased sensitivity to anticancer drugs. Finally, PDAC CSCs injected into nude mice developed a larger subcutaneous tumor mass and showed a higher metastatic activity compared to parental cells. The present study demonstrates the ability to obtain CSCs from several PDAC cell lines and that these cells are differentially resistant to various anticancer agents. This variability renders them a model of great importance to deeply understand pancreatic adenocarcinoma biology, to discover new biomarkers and to screen new therapeutic compounds

Wide spetcrum mutational analysis of metastatic renal cell cancer : a retrospective next generation sequencing approach

Renal cell cancer (RCC) is characterized by histological and molecular heterogeneity that may account for variable response to targeted therapies. We evaluated retrospectively with a next generation sequencing (NGS) approach using a pre-designed cancer panel the mutation burden of 32 lesions from 22 metastatic RCC patients treated with at least one tyrosine kinase or mTOR inhibitor. We identified mutations in the VHL, PTEN, JAK3, MET, ERBB4, APC, CDKN2A, FGFR3, EGFR, RB1, TP53 genes. Somatic alterations were correlated with response to therapy. Most mutations hit VHL1 (31,8%) followed by PTEN (13,6%), JAK3, FGFR and TP53 (9% each). Eight (36%) patients were wild-type at least for the genes included in the panel. A genotype concordance between primary RCC and its secondary lesion was found in 3/6 cases. Patients were treated with Sorafenib, Sunitinib and Temsirolimus with partial responses in 4 (18,2%) and disease stabilization in 7 (31,8%). Among the 4 partial responders, 1 (25%) was wild-type and 3 (75%) harbored different VHL1 variants. Among the 7 patients with disease stabilization 2 (29%) were wild-type, 2 (29%) PTEN mutated, and single patients (14% each) displayed mutations in VHL1, JAK3 and APC/CDKN2A. Among the 11 non-responders 7 (64%) were wild-type, 2 (18%) were p53 mutated and 2 (18%) VHL1 mutated. No significant associations were found among RCC histotype, mutation variants and response to therapies. In the absence of predictive biomarkers for metastatic RCC treatment, a NGS approach may address single patients to basket clinical trials according to actionable molecular specific alterations.Peer reviewe

Metformin Enhances Cisplatin-Induced Apoptosis and Prevents Resistance to Cisplatin in Co-mutated KRAS/LKB1 NSCLC

Abstract Introduction We hypothesized that activating KRAS mutations and inactivation of the liver kinase B1 (LKB1) oncosuppressor can cooperate to sustain NSCLC aggressiveness. We also hypothesized that the growth advantage of KRAS/LKB1 co-mutated tumors could be balanced by higher sensitivity to metabolic stress conditions, such as metformin treatment, thus revealing new strategies to target this aggressive NSCLC subtype. Methods We retrospectively determined the frequency and prognostic value of KRAS/LKB1 co-mutations in tissue specimens from NSCLC patients enrolled in the TAILOR trial. We generated stable LKB1 knockdown and LKB1-overexpressing isogenic H1299 and A549 cell variants, respectively, to test the in vitro efficacy of metformin. We also investigated the effect of metformin on cisplatin-resistant CD133+ cells in NSCLC patient-derived xenografts. Results We found a trend towards worse overall survival in patients with KRAS/LKB1 co-mutated tumors as compared to KRAS-mutated ones (hazard ratio: 2.02, 95% confidence interval: 0.94–4.35, p = 0.072). In preclinical experiments, metformin produced pro-apoptotic effects and enhanced cisplatin anticancer activity specifically in KRAS/LKB1 co-mutated patient-derived xenografts. Moreover, metformin prevented the development of acquired tumor resistance to 5 consecutive cycles of cisplatin treatment (75% response rate with metformin-cisplatin as compared to 0% response rate with cisplatin), while reducing CD133+ cells. Conclusions LKB1 mutations, especially when combined with KRAS mutations, may define a specific and more aggressive NSCLC subtype. Metformin synergizes with cisplatin against KRAS/LKB1 co-mutated tumors, and may prevent or delay the onset of resistance to cisplatin by targeting CD133+ cancer stem cells. This study lays the foundations for combining metformin with standard platinum-based chemotherapy in the treatment of KRAS/LKB1 co-mutated NSCLC