Forward Prices Research Articles

The connectedness features of German electricity futures over short and long maturities

This research provides an extensive characterization of the contagion between electricity, energy commodities, financial assets and economic indicators across several maturities. Despite the widespread importance of electricity futures, this has been an under-researched topic. The evolution of connectedness is investigated between 2006 and 2023. With a special focus on electricity forward base and peak contracts, results show that the contagion effects are moderate but evolve through time, with greater impacts observed during the crisis years. We confirm that electricity forward prices are more sensitive to operators’ future expectations on fundamental market conditions than to financial and economic shocks.

A Barndorff-Nielsen and Shephard model with leverage in Hilbert space for commodity forward markets

We propose an extension of the model introduced by Barndorff-Nielsen and Shephard, based on stochastic processes of Ornstein–Uhlenbeck type taking values in Hilbert spaces and including the leverage effect. We compute explicitly the characteristic function of the log-return and the volatility processes. By introducing a measure change of Esscher type, we provide a relation between the dynamics described with respect to the historical and the risk-neutral measures. We discuss in detail the application of the proposed model to describe the commodity forward curve dynamics in a Heath–Jarrow–Morton framework, including the modelling of forwards with delivery period occurring in energy markets and the pricing of options. For the latter, we show that a Fourier approach can be applied in this infinite-dimensional setting, relying on the attractive property of conditional Gaussianity of our stochastic volatility model. In our analysis, we study both arithmetic and geometric models of forward prices and provide appropriate martingale conditions in order to ensure arbitrage-free dynamics.

Does Vertical Integration Always Reduce an Electricity Retailer’s Fixed Price Offer?

We use a profit-maximizing model of forward pricing, which revealed that an electricity retailer’s fixed price offer (FPO) contains a large forward premium that encourages vertical integration of a generation company and a retailer. However, vertical integration does not always reduce FPOs, particularly when residential customers are segmented by consumption size and price sensitivity. Hence, a proposed merger of a big generation company and a big retailer requires regulatory scrutiny.

Renewable Energy and Equilibrium Hedging in Electricity Forward Markets

We study the impact of renewable energy on forward markets for electricity. Previous literature shows that forward prices are determined by time-varying demand and volatile spot prices. We introduce supply risk from renewable generation and find that stochastic renewable output mitigates income risk for generating firms, in particular when negative shocks to renewable output have large positive price impact. This risk off-setting effect leads to reduced hedging needs for generating companies and increases the forward premium. Using five years of high-frequency spot and futures market data, we confirm our model empirically. In sum, our findings suggest that intermittent renewable generation changes firm’s hedging incentives and has significant impact on forward prices for electricity. JEL Classification: G11, G13, Q42

How to Value Proved but Undeveloped Petroleum Reserves

Proved undeveloped reserves (PUDs) are typically assigned a value of zero when the cost to produce them is greater than the prevailing forward curve. The zero valuation occurs despite the option value contained in even the most expensive PUDs. While PUDs are worthless if spot and forward prices forever remain below the cost to produce them, they have positive value if either the spot price or a contracted futures price exceeds the cost at any time. Since the probability that future prices exceed cost is positive, PUDs have positive option value despite the industry practice. Zero valuation occurs primarily because financing is unavailable when hedging is contingent on uncertain future outcomes where probabilities cannot be modeled. The failure of models to recognize contingent hedging is a limitation that leads to chronically undervalued PUDs in marginal and sub-marginal price environments. The literature is silent on contingent hedging where financing is dependent on forward curves that will not exist until some future date. This paper introduces a model that addresses these limitations.

Pengembangan Portal Penjualan Lobster berbasis Web menggunakan Metode Scrum dengan Fitur Prediksi Harga menggunakan Regresi Linier Berganda

This research aims to reveal effective design and content strategies to enhance the attractiveness of a website-based lobster export sales portal. These strategies are developed to capture attention and maintain buyer interest while integrating a one-month forward price prediction feature using linear regression. The research methodology involves implementing the Scrum method for website project management and multiple linear regression to forecast lobster prices. The focus lies on Scrum implementation in designing the sales portal and incorporating lobster price prediction features on the website. The research results demonstrate the successful development of the website using the Scrum framework within three sprints. Feasibility testing with Blackbox Testing aligns with expectations. Multiple linear regression is utilized to predict lobster prices, yielding a forecasted price of 587.3 for the 57th month. With a changing formula each month, the model aims for greater accuracy in anticipating lobster price fluctuations related to seasonal dynamics and specific events. This research provides insights into the implementation of Scrum in website design and practical applications of lobster price prediction in the context of export sales.

Investigating the Impact of Agricultural, Financial, Economic, and Political Factors on Oil Forward Prices and Volatility: A SHAP Analysis

Accurately forecasting crude oil prices is crucial due to its vital role in the industrial economy. In this study, we explored the multifaceted impact of various financial, economic, and political factors on the forecasting of crude oil forward prices and volatility. We used various machine learning models to forecast oil forward prices and volatility based on their superior predictive power. Furthermore, we employed the SHAP framework to analyze individual features to identify their contributions in terms of the prediction. According to our findings, factors contributing to oil forward prices and volatility can be summarized into four key focal outcomes. First, it was confirmed that soybean forward pricing overwhelmingly contributes to oil forward pricing predictions. Second, the SSEC is the second-largest contributor to oil forward pricing predictions, surpassing the contributions of the S&P 500 or oil volatility. Third, the contribution of oil forward prices is the highest in predicting oil volatility. Lastly, the contribution of the DXY significantly influences both oil forward price and volatility predictions, with a particularly notable impact on oil volatility predictions. In summary, through the SHAP framework, we identified that soybean forward prices, the SSEC, oil volatility, and the DXY are the primary contributors to oil forward price predictions, while oil forward prices, the S&P 500, and the DXY are the main contributors to oil volatility predictions. These research findings provide valuable insights into the most-influential factors for predicting oil forward prices and oil volatility, laying the foundation for informed investment decisions and robust risk-management strategies.

The Levelised Cost of Frequency Control Ancillary Services in Australia’s National Electricity Market

Over the period 2016–2021 Australia’s National Electricity Market (NEM) experienced an investment supercycle with 16,000MW of new utility-scale renewable plant commitments in a power system with a peak demand of 35,000MW, and the disorderly loss of 5,000MW of synchronous coal-fired plant. This placed strains on system security, most visibly in the distribution of the power systems’ frequency, requiring material changes to the NEM’s suite of Frequency Control Ancillary Service (FCAS) markets. Utility-scale batteries are ideally suited for FCAS duties, but there is no forward price curve for FCAS markets, nor is there any systematic framework for determining equilibrium prices that might otherwise be used for investment decision-making. In this article, we develop an approach for quantifying long run equilibrium costs and stochastic spot prices in the markets for Frequency Control Ancillary Services, with the intended application being to guide the suitability of utility-scale battery investments under conditions of uncertainty and missing forward FCAS markets.

On the impact of targeted and universal electricity concessions policy on fuel poverty in the NEM's Queensland region

In Australia's National Electricity Market (NEM), households have faced markedly higher electricity prices following the 2022 war in Ukraine and subsequent energy crisis. Forward prices for coal and gas increased sharply, trading at multiples of historic averages. While initially sheltered to wholesale price shocks through regulatory lag, retail electricity tariffs rose by more than 30 % for the 2024 financial year. Impacts of this magnitude warrant adjustment to hardship policy given electricity is an essential service. In the NEM's Queensland region, hardship policy payments for vulnerable households were more than doubled, and a one-off universal payment was made to all households. In this article, we model the number of households that meet a definition of fuel poverty before, and after, the tariff increases and changes to policy. We find the underlying levels of fuel poverty rose from 6.2 % to 11.6 % over the period 2022 to 2023. From a horizontal and vertical policy efficiency perspective, the suite of policy initiatives improve outcomes with the headline result being a material reduction in fuel poverty, falling from 11.6 % to 5.4 % of households.

Analyzing Risk Premiums in the Brazilian Power Market: A Quantitative Study

This paper conducts an empirical analysis of risk premiums in the Brazilian electricity market, a critical but understudied field. Employing two distinct methodologies—Average Forward Prices and Last Observed Forward Prices—the study calculates risk premiums between spot and forward electricity prices. Our analysis consistently identifies negative risk premiums, which serve as indicators that the market may be underestimating certain types of risk. These underestimations are potentially influenced by inherent market uncertainties, including volatile demand, unpredictable supply, and frequent regulatory shifts. Additionally, we observe a high volatility in risk premiums, signifying a dynamic and ever-changing market where expectations are continuously recalibrated. Such conditions present possible arbitrage opportunities for market actors and underline the need for policymakers to introduce measures mitigating market unpredictability. By focusing on these nuances, this paper enriches the broader discourse on risk premiums in electricity markets and underscores the necessity for further research aimed at devising effective risk management strategies.

It Is Easy To Find Lithium; Turning a Profit Is Hard

Finding lithium in the water from oil and gas wells is easy. Finding enough to make money is hard. In the US and Canada there has been growing interest in directly extracting lithium from the water coming out of the oil/water separator, which is competing with more established techniques such as mining and evaporating lithium-rich fluids. The race to find lithium is driven by expectations that fast-rising electric car sales will make the lithium required for batteries in those vehicles a valuable commodity. Those chasing direct extraction are also betting that their innovations can do what they say. Direct extraction from water started looking like a real possibility earlier this year when ExxonMobil paid $100 million to buy a company holding 120,000 gross acres of leases in south Arkansas. The price reflected the location in the heart of the direct lithium extraction industry of the future. The area offers a unique combination of lithium-rich water plus the infrastructure and expertise needed to transport, process, and dispose of the billions of gallons of water needed for industrial-scale mineral extraction. Commercial production of battery-quality lithium carbonate in Arkansas is years off. But the sprawling network of water-producing wells, pipelines, and processing that have made the state one of the world’s leading bromine producers, lowers the risk and cost of commercial lithium production. The source of the lithium-rich water is the Smackover—an oil formation discovered near El Dorado, Arkansas, back in 1921 when a gusher blew in. A year after that discovery, there were 608 producing wells nearby, according to the El Dorado News-Times. Now with lithium looking like battery gold, investors are rushing in. In this emotional market, lithium carbonate prices have swung like cryptocurrency. As of mid-October, a ton of it was selling for about $25,000. Over the past quarter it averaged $32,000/ton. Late last year it stood at $85,000. And in 2021 it was going for $10,000, said Graham Bain, vice president for subsurface opportunities at Enverus. He offered $25,000/ton as a “go forward price.” As with oil and gas, hopes of high prices have a way of rapidly increasing supplies, resulting in price-crashing gluts. So far, most oil companies sound curious but far from committed to lithium. “There’s a big rush. I have received several requests from companies in the Permian Basin to discuss lithium in the water,” said Jean-Philippe Nicot, senior research scientist at the Bureau of Economic Geology (BEG) at The University of Texas at Austin. He is the lead author on recent paper that offered a primer on what’s known about lithium brines from oil wells in states from Texas to Mississippi. It is based on nearly 1,802 water analyses from the US Geological Survey (USGS)—some dating back to the 1960s—plus a recent 576-well survey by the BEG. The geologists, whose work ranged from hard-rock mining to water research, said this is an early effort to begin figuring out how to find the lithium in subsurface water.

Co-movements between forward prices and resource availability in hydro-dominated electricity markets

AbstractHydropower producers estimate the opportunity value of their water, known as a water value, by comparing current prices to future opportunities. When hydropower dominates the energy mix, the system’s hydrological state predominantly governs supply and thus prices. Despite this intuitive relationship, industry practice is to assume that inflow to reservoirs and prices are independent when they establish operational policies 1–2 years ahead. To investigate the impact of this assumption, we formulate the hydropower scheduling problem as a Markov decision process and develop a novel price model that considers the joint dynamics of forward prices and inflows. We find that producers underestimate their water value when they ignore co-movements between price and inflow. The dependency makes producers more willing to postpone generation and tolerate slightly higher spillage risk. This is because high inflow periods tend to observe low prices and the reservoir capacity is limited. Nevertheless, a case study of a hydropower plant with industry data suggests modest economic losses in practice. Our numerical results suggest a potential gain of 0.17% in expected revenue and approximately unchanged revenue variance if producers consider the co-movements when establishing an operational policy.

A Novel Semiparametric Structural Model for Electricity Forward Curves

We propose a novel semi-parametric structural model to estimate the electricity forward curves based on elementary forward prices. The proposed model (i) explores the nonarbitrage relations between contracts with overlapping delivery periods, (ii) considers a parametric structure for price seasonality and exogenous variables, and (iii) uses non-parametric techniques to extract the remaining inter-temporal and cross-maturity information from data. Thus, our model allows users to estimate and complete the historical prices of any swap contract. We address the multi-objective estimation problem by hierarchical optimization. First, arbitrage levels are minimized. Then, the parametric part of the model is estimated. Finally, smoothness in the maturity and trading date dimensions are jointly considered in the estimation of the non-parametric part of the model. Based on a controlled study with real data from the Nordic power market, we show that our model outperforms benchmarks in terms of estimation error for missing data. We also isolate the effect of accounting for overlaps and smoothing in the trading dates dimension. Results show that these two key features of our model are crucial for improving the model accuracy. Finally, we apply our method to estimate the Brazilian forward curve and reconstruct the historical data.

Strategic logistics service procurement in shipping supply chains

This paper develops a game-theoretic model to explore the strategic procurement of logistics services in a shipping supply chain, where a freight forwarder canvasses orders and provides one-stop service to shippers. The forwarder faces a decision between in-house and outsourcing modes for inland logistics services, as well as the choice to purchase ocean shipping services through a forward agreement or in a spot market. The spot prices of inland logistics and ocean shipping services are positively correlated with shipper demand, meaning that changes in shipper demand can affect the spot prices. Our analysis indicates that the responsiveness of spot prices could be beneficial to the carrier but detrimental to the forwarder. Hence, the forwarder would adopt a forward agreement to purchase ocean shipping service even when the forward price is slightly higher than the expected spot price. Moreover, a forward agreement could be reached if the market size is relatively small. Finally, we show that the forwarder should adopt the in-house mode for inland logistics services, even when the in-house mode cost is higher than the expected outsourcing cost. This study contributes the literature by revealing how the correlation between shipper demand and spot prices for two types of logistics services affects the procurement decisions of a freight forwarder.

Intelligent option portfolio model with perspective of shadow price and risk-free profit

Since Markowitz proposed modern portfolio theory, portfolio optimization has been being a classic topic in financial engineering. Although it is generally accepted that options help to improve the market, there is still an improvement for the portrayal of their unique properties in portfolio problems. In this paper, an intelligent option portfolio model is developed that allows selling options contracts to earn option fees and considers the high leverage of options in the market. Deep learning methods are used to predict the forward price of the underlying asset, making the model smarter. It can find an optimal option portfolio that maximizes the final wealth among the call and put options with multiple strike prices. We use the duality theory to analyze the marginal contribution of initial assets, risk tolerance limit, and portfolio leverage limit for the final wealth. The leverage limit of the option portfolio has a significant impact on the return. To satisfy the investors with different risk preferences, we also give the conditions for the option portfolio to gain a risk-free return and replace the Conditional Value-at-Risk. Numerical experiments demonstrate that the intelligent option portfolio model obtains a satisfactory out-of-sample return, which is significantly positively correlated with the volatility of the underlying asset and negatively correlated with the forecast error of the forward price. The risk- free option model is effective in achieving the goal of no drawdown and gaining satisfactory returns. Investors can adjust the balance point between returns and risks according to their risk preference.

Implementation of currency interventions of the Central Bank of Armenia with financial derivatives

Purpose: The main goal of the paper is to develop financial derivatives of the Republic of Armenia (RA) as hedging tools for foreign exchange risk management for financial organizations and other entities. The secondary goal is to substantiate the efficiency and expediency of the use of financial derivatives as alternative tools for the implementation of foreign exchange interventions of the Central Bank of Armenia (CBA). Methodology: In order to evaluate the currency interventions carried out by the Central Bank of Armenia (CBA), the interventions and their impact were studied by applying causal-comparative analysis and synthesis methods based on the CBA and International Monetary Fund (IMF) data. The most efficient intervention tools were revealed as a result of such analysis. The forward prices were calculated based on the swap intervention data and using the principle of impossibility of arbitration. Results: The present study substantiated that the application of a forward contract for difference can ensure the same goals like those pursued by foreign exchange interventions with no impact on foreign reserves and no need for sterilization. Conclusion: It was proposed to divide the swap agreements used by the CBA into spot and forward, which will ensure the realization of the same goals along with engaging a large number of non-financial companies. At the same time, the application of a forward contract for difference during the implementation of foreign exchange interventions will lead to the development of the financial derivatives market and ensure the matching of foreign exchange cash flows received by businesses in different periods.

A possible interpretation of financial markets affected by dark volatility

<abstract><p>The aim of this paper is to use a special type of Einstein warped product manifolds recently introduced, the so-called PNDP-manifolds, for the differential geometric study, by focusing on some aspects related to dark field in financial market such as the concept of dark volatility. This volatility is not fixed in any relevant economic parameter, a sort of negative dimension, a ghost field, that greatly influences the behavior of real market. Since the PNDP-manifold has a "virtual" dimension, we want to use it in order to show how the Global Market is influenced by dark volatility, and in this regard we also provide an example, by considering the classical exponential models as possible solutions to our approach. We show how dark volatility, combined with specific conditions, leads to the collapse of a forward price.</p></abstract>

Option Prices Expansions and Applications

Daniel Bloch presents a general pricing approximation technique for European call option prices in a jump-diffusion model with stochastic interest rates. The author considers the dynamics of the logarithm of the forward price, under the forward measure, in the class of multifactor Affine and Quadratic models without jumps, and devise the dynamics of its associated variance swap. Bloch expresses the expected future average volatility as a function of that variance swap, and uses classical Ito's calculus to expand prices around the Black formula. The author then adds jumps to the dynamics of the log-forward price and condition the expectation of the call price with respect to the number of jumps. Applying a change of measure, option prices decompose into a weighted sum of approximated multifactor Affine and Quadratic models. Bloch uses these prices decompositions to define an analytical formula that approximate the implied volatility surface in the class of Affine and Quadratic models with jumps. At last, the author compares a few prices approximations against Monte Carlo simulations for a range of maturities covering one year, and provide accuracy measures. Bloch then uses these prices as benchmark to measure the accuracy of the implied volatility surface expansion. The author obtains very low MAE and RMSE, both on the prices and IVS, demonstrating that the series expansions are very precise for short and long maturities. Applications include: fast prices and Greeks estimation for European options, initial values for numerical computation of IVS, and control variate for complex pricing models in Monte Carlo and Machine Learning.

Higher moments in the fundamental specification of electricity forward prices

An extended specification for estimating the risk premia necessary for the forward pricing of wholesale electricity is developed in order to respond to the increasing need for more precise risk management of hedging positions in practice. Using Taylor expansions, we provide new specifications for the electricity forward premium including its dependency on all four moments of the expected wholesale price density as well as the higher moments of the demand density including skewness and kurtosis. Overall we argue that previous models have been underspecified and that the extended formulation proposed in this analysis is robust and worthwhile.

A Fourier Interpolation Method for Numerical Solution of FBSDEs: Global Convergence, Stability, and Higher Order Discretizations

The convolution method for the numerical solution of forward-backward stochastic differential equations (FBSDEs) was originally formulated using Euler time discretizations and a uniform space grid. In this paper, we utilize a tree-like spatial discretization that approximates the BSDE on the tree, so that no spatial interpolation procedure is necessary. In addition to suppressing extrapolation error, leading to a globally convergent numerical solution for the FBSDE, we provide explicit convergence rates. On this alternative grid the conditional expectations involved in the time discretization of the BSDE are computed using Fourier analysis and the fast Fourier transform (FFT) algorithm. The method is then extended to higher-order time discretizations of FBSDEs. Numerical results demonstrating convergence are presented using a commodity price model, incorporating seasonality, and forward prices.