A mixed C-vine copula model for hedging price and volumetric risk in wind power trading

When energy trading companies enter into long-term agreements with wind power producers, where a fixed price is paid for the fluctuating production, they are facing a joint price and volumetric risk. Since the pay-off of such agreements is non-linear, a hedging portfolio would ideally consist of not only forwards, but also a basket of e.g. call and put options. Illiquidity and an almost non-existent market for options challenge however the optimal hedging of joint price and volumetric risk in many market places. Here, we consider the case of the Danish power market, and exploit its strong positive correlation with the much more liquid German market to construct a proxy hedge. We propose a three-dimensional mixed vine copula to model the evolution of the Danish and German spot electricity prices and the Danish wind power production. We construct a realistic hedging portfolio by identifying various instruments available in the market, such as real options in the form of the right to transfer electricity across the border and the right to convert electricity to heat. Using the proposed vine copula to determine optimal hedging decisions, we show that significant benefits are to be drawn by extending the hedging portfolio with the proposed instruments.     

Detection of arbitrage in a market with multi-asset derivatives and known risk-neutral marginals

In this paper we study the existence of arbitrage opportunities in a multi-asset market when risk-neutral marginal distributions of asset prices are known. We first propose an intuitive characterization of the absence of arbitrage opportunities in terms of copula functions. We then address the problem of detecting the presence of arbitrage by formalizing its resolution in two distinct ways that are both suitable for the use of optimization algorithms. The first method is valid in the general multivariate case and is based on Bernstein copulas that are dense in the set of all copula functions. The second one is easier to work with but is only valid in the bivariate case. It relies on results about improved Fréchet–Hoeffding bounds in presence of additional information. For both methods, details of implementation steps and empirical applications are provided.     

Monte Carlo valuation of natural gas investments

In this evaluation of energy assets related to natural gas, our particular focus is on a base load natural gas combined cycle power plant and a liquefied natural gas facility in a realistic setting. We also value several American-type investment options following the least squares Monte Carlo approach. We calibrate mean-reverting stochastic processes for gas and electricity prices by using data from NYMEX NG futures contracts and the Spanish wholesale electricity market, respectively. Additional sources of uncertainty concern the initial investment outlay, or the option's time to maturity, or the cost of CO₂ emission permits.     

Modelling electricity prices: a time change approach

To capture mean reversion and sharp seasonal spikes observed in electricity prices, this paper develops a new stochastic model for electricity spot prices by time changing the Jump Cox-Ingersoll-Ross (JCIR) process with a random clock that is a composite of a Gamma subordinator and a deterministic clock with seasonal activity rate. The time-changed JCIR process is a time-inhomogeneous Markov semimartingale which can be either a jump-diffusion or a pure-jump process, and it has a mean-reverting jump component that leads to mean reversion in the prices in addition to the smooth mean-reversion force. Furthermore, the characteristics of the time-changed JCIR process are seasonal, allowing spikes to occur in a seasonal pattern. The Laplace transform of the time-changed JCIR process can be efficiently computed by Gauss–Laguerre quadrature. This allows us to recover its transition density through efficient Laplace inversion and to calibrate our model using maximum likelihood estimation. To price electricity derivatives, we introduce a class of measure changes that transforms one time-changed JCIR process into another time-changed JCIR process. We derive a closed-form formula for the futures price and obtain the Laplace transform of futures option price in terms of the Laplace transform of the time-changed JCIR process, which can then be efficiently inverted to yield the option price. By fitting our model to two major electricity markets in the US, we show that it is able to capture both the trajectorial and the statistical properties of electricity prices. Comparison with a popular jump-diffusion model is also provided.     

Cheaper,Greener and More Efficient: Rationalising UK Carbon Prices

Current UK energy use policies, which primarily aim to reduce carbon emissions, provide abatement incentives that vary by user and fuel, creating inefficiency. Distributional concerns are often given as a justification for the lower carbon price faced by households, but there is little rationale for carbon prices associated with the use of gas to be lower than those for electricity. We consider reforms that raise carbon prices faced by households and reduce the variation in carbon prices across gas and electricity use, improving the efficiency of emissions reduction. We show that the revenue raised from these reforms can be recycled in a way that ameliorates some of the distributional concerns. Whilst such recycling is not able to protect all poorer households, existing policy also makes distributional trade‐offs, but does so in an opaque and inefficient way.     

The Relationship Between Energy Commodity Prices and Electricity and Market Index Performances: Evidence from an Emerging Market

We investigate the effect of energy commodity price movements on market and electricity index returns in Turkey for the periods before, during, and after the year 2008. Although the Turkish economy is highly reliant on oil, we find that oil price does not lead either electricity or market indexes. This might be attributable to sluggish integration of financial markets in Turkey compared to developed markets. Natural gas price leads electricity index in the pre-2008 period. Its significance is reduced following the decline in natural gas usage in electricity production. This suggests that commodity dependence may be driving the link between commodity and asset prices in related sectors.     

Electricity Forward Prices: A High-Frequency Empirical Analysis

We conduct an empirical analysis of forward prices in the PJM electricity market using a high‐frequency data set of hourly spot and day‐ahead forward prices. We find that there are significant risk premia in electricity forward prices. These premia vary systematically throughout the day and are directly related to economic risk factors, such as the volatility of unexpected changes in demand, spot prices, and total revenues. These results support the hypothesis that electricity forward prices in the Pennsylvania, New Jersey, and Maryland market are determined rationally by risk‐averse economic agents.     

Score-driven copula models for portfolios of two risky assets

ABSTRACT

The precise measurement of the association between asset returns is important for financial investors and risk managers. In this paper, we focus on a recent class of association models: Dynamic Conditional Score (DCS) copula models. Our contributions are the following: (i) We compare the statistical performance of several DCS copulas for several portfolios. We study the Clayton, rotated Clayton, Frank, Gaussian, Gumbel, rotated Gumbel, Plackett and Student's t copulas. We find that the DCS model with the Student's t copula is the most parsimonious model. (ii) We demonstrate that the copula score function discounts extreme observations. (iii) We jointly estimate the marginal distributions and the copula, by using the Maximum Likelihood method. We use DCS models for mean, volatility and association of asset returns. (iv) We estimate robust DCS copula models, for which the probability of a zero return observation is not necessarily zero. (v) We compare different patterns of association in different regions of the distribution for different DCS copulas, by using density contour plots and Monte Carlo (MC) experiments. (vi) We undertake a portfolio performance study with the estimation and backtesting of MC Value-at-Risk for the DCS model with the Student's t copula.     

The forward premium in electricity futures

Understanding the nature of the forward premium is particularly crucial, but rather elusive, for a non-storable commodity such as wholesale electricity. Whilst forward prices emerge as the expectation of spot plus, or minus, an ex ante premium for risk, the manifestation and empirical analysis must focus upon realised ex post premiums. This presents modelling requirements to control for shocks to the spot expectation as well as the endogeneity of ex post premia with spot price outcomes. In addition, because electricity is a derived commodity in the sense that market prices are often set by technologies that convert gas or coal into power, it is an open question whether much of the premia in power may actually be a pass-through of the premia in gas (or coal). Using a four dimensional VAR model we are able to distinguish fundamental and behavioural aspects of price formation in both the daily and monthly forward premia from the British market. We present new evidence on daily and seasonal sign reversals, associated with demand cycles, the greater importance of behavioural adaptations in the risk premia than fundamental or spot market risk measures, and the substantial fuel risk pass-through. We also show the value of a nonlinear specification in this context.     

Modeling exchange rate dependence dynamics at different time horizons

Despite an extensive body of research, the best way to model the dependence of exchange rates remains an open question. In this paper we present a new approach which employs a flexible time-varying copula model. It allows the conditional correlation between exchange rates to be both time-varying and modeled independently from the marginal distributions. We introduce a dynamic specification for the correlation using the Fisher transformation. Applied to Euro/US dollar and Japanese Yen/US dollar, our results reveal a significantly time-varying correlation, dependent on the past return realizations. We find that a time-varying copula with the proposed correlation specification gives better results than alternative dynamic benchmark models. The dynamic copula model outperforms at six different time horizons, ranging from hourly to daily, confirming the model specification.     

Assessing High-Risk Scenarios by Full-Range Tail Dependence Copulas

Copulas with a full-range tail dependence property can cover the widest range of positive dependence in the tail, so that a regression model can be built accounting for dynamic tail dependence patterns between variables. We propose a model that incorporates both regression on each marginal of bivariate response variables and regression on the dependence parameter for the response variables. An ACIG copula that possesses the full-range tail dependence property is implemented in the regression analysis. Comparisons between regression analysis based on ACIG and Gumbel copulas are conducted, showing that the ACIG copula is generally better than the Gumbel copula when there is intermediate upper tail dependence. A simulation study is conducted to illustrate that dynamic tail dependence structures between loss and ALAE can be captured by using the one-parameter ACIG copula. Finally, we apply the ACIG and Gumbel regression models for a dataset from the U.S. Medical Expenditure Panel Survey. The empirical analysis suggests that the regression model with the ACIG copula improves the assessment of high-risk scenarios, especially for aggregated dependent risks.     

Electricity Prices and Power Derivatives: Evidence from the Nordic Power Exchange

This paper examines the importance of the regular pattern in the behavior of electricity prices, and its implications for the purposes of derivative pricing. We analyze the Nordic Power Exchange's spot, futures, and forward prices. We conclude that the seasonal systematic pattern throughout the year, in particular, is of crucial importance in explaining the shape of the futures/forward curve. Moreover, in the context of the oneand two factor models analyzed in this paper, a simple sinusoidal functionis adequate in order capture the seasonal pattern of the features and forwardcurve directly implied by the seasonal behavior of spot electricity prices.     

Modelling the joint behaviour of electricity prices in interconnected markets

The liberalization of energy markets worldwide during recent decades has introduced severe implications for the price formation in these markets. Especially within the European day-ahead electricity markets, increased physical connections between different market areas and a joint effort on optimizing the aggregate social welfare have led to highly connected markets. Consequently, observing the exact same hourly day-ahead prices for two or more interconnected electricity markets in Europe happens frequently. This affects the modelling of such prices and in turn the valuation of derivatives written on prices from these market areas. In this paper, we propose a joint model for day-ahead electricity prices in interconnected markets composed of a combination of transformed Ornstein–Uhlenbeck processes. We discuss the properties of the model and propose an estimation procedure based on filtering techniques. Furthermore, the properties of the model reveal that analytical prices are attainable for, e.g., forwards and spread options.     

Emission Allowance as a Derivative on Commodity-Spread

We provide a valuation formula for emission allowance. Assuming that the value of emission allowance on the last day of a trading phase is equal to a spread of commodity prices (e.g. electricity and natural gas) when the spread is positive and less than the penalty, we show that the emission allowance price is equal to the value of a portfolio of European call options on the spread of the commodities. Using the formula, we obtain a hedging strategy for emission allowance trading. We also empirically analyze option value embedded in emission allowance, and find by numerical analysis that the option value is relatively large.     

Tracking a changing copula

A copula models the relationships between variables independently of their marginal distributions. When the variables are time series, the copula may change over time. Recursive procedures based on indicator variables are proposed for tracking these changes over time. Estimation of the unknown parameters is by maximum likelihood. When the marginal distributions change, pre-filtering is necessary before constructing the indicator variables on which the recursions are based. This entails estimating time-varying quantiles and a simple method based on time-varying histograms is proposed. The techniques are applied to the Hong Kong and Korean stock market indices. Some interesting and unexpected movements are detected, particularly after the attack on the Hong Kong dollar in 1997.     

Bounds and prices of currency cross-rate options

This paper derives the pricing bounds of a currency cross-rate option using the option prices of two related dollar rates via a copula theory and presents the analytical properties of the bounds under the Gaussian framework. Our option pricing bounds are useful, because (1) they are general in the sense that they do not rely on the distribution assumptions of the state variables or on the selection of the copula function; (2) they are portfolios of the dollar-rate options and hence are potential hedging instruments for cross-rate options; and (3) they can be applied to generate bounds on deltas. The empirical tests suggest that there are persistent and stable relationships between the market prices and the estimated bounds of the cross-rate options and that our option pricing bounds (obtained from the market prices of options on two dollar rates) and the historical correlation of two dollar rates are highly informative for explaining the prices of the cross-rate options. Moreover, the empirical results are consistent with the predictions of the analytical properties under the Gaussian framework and are robust in various aspects.     

Dynamic pricing: when to entice brand switching and when to reward consumer loyalty

This article develops a theory of dynamic pricing in which firms may offer separate prices to different consumers based on their past purchases. Brand preferences over two periods are described by a copula admitting various degrees of positive dependence. When commitment to future prices is infeasible, each firm offers lower prices to its rival's customers. When firms can commit to future prices, consumer loyalty is rewarded if preference dependence is low, but enticing brand switching occurs if preference dependence is high. Our theory provides a unified treatment of the two pricing policies and sheds light on observed practices across industries.     

Do Americans consume too little natural gas? An empirical test of marginal cost pricing

This article measures the extent to which prices exceed marginal costs in the U.S. natural gas distribution market during the period 1991–2007. We find large departures from marginal cost pricing in all 50 states, with residential and commercial customers facing average markups of over 40%. Based on conservative estimates of the price elasticity of demand, these distortions impose hundreds of millions of dollars of annual welfare loss. Moreover, current price schedules are an important preexisting distortion which should be taken into account when evaluating carbon taxes and other policies aimed at addressing external costs.     

Shorting in Speculative Markets

In models of trading with heterogeneous beliefs following Harrison-Kreps, short selling is prohibited and agents face constant marginal costs-of-carry. The resale option guarantees that prices exceed buy-and-hold prices and the difference is identified as a bubble. We propose a model where risk-neutral agents face asymmetric increasing marginal costs on long and short positions. Here, agents also value an option to delay, and a Hamilton-Jacobi-Bellman equation quantifies the influence of costs on prices. An unexpected decrease in shorting costs may deflate a bubble, linking financial innovations that facilitated shorting of mortgage-backed securities to the collapse of prices.     

Pricing CDS spreads with Credit Valuation Adjustment using a mixture copula

Credit derivatives pricing models before Basel III ignored losses in market value stemming from higher probability of counterparty default. We propose a general credit derivatives pricing model to evaluate a Credit Default Swap (CDS) with counterparty risk, including the Credit Valuation Adjustment (CVA) in order to optimize the economic capital allocation. We work from the model proposed by Luciano (2003, Working Paper, International Center of Economic Research) and the general pricing representation established by Sorensen and Bollier (Financial Analysts Journal 1994;50(3):23–33) to provide a model close to the market practice, easy to implement and fitting with Basel III framework. We approach the dependence between counterparty risk and that of the reference entity with a technical tool: the copula, in particular, the mixture one that combines common “extreme” copulas. We study the CDS's vulnerability in extreme dependence cases. By varying Spearman's rho, the mixture copula covers a broad spectrum of dependence and ensures closed form prices. We end up with an application on real market data.