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13:00-14:30 Session 2A: Financialization of Commodity Markets
Location: Room 1
Do financially constrained firms engage in opportunistic and risky behavior? Evidence from the oil and gas production sector.
DISCUSSANT: Gabriel Power

ABSTRACT. We document opportunistic behavior of financially constrained firms in the oil and gas production sector. Our results show that firms exposed to credit shocks engage in infill drilling, a controversial drilling practice that allows firms to enhance reported reserves and project larger production volumes, but carries a risk of a substantial damage to ultimate recovery. In contrast, our results do not indicate risk shifting showing that firms are not willing to take immediate risks. We also show that financing constraints prevent firms from timely adjusting drilling efforts in response to changing macroeconomic conditions. Our paper documents a previously unrecognized long term effect of credit supply shocks.

Has financialization changed the impact of macro announcements on commodity markets?
PRESENTER: Gabriel Power
DISCUSSANT: Alessandro Melone

ABSTRACT. Since the 2000s, there is a debate in industry and academia concerning the financialization of commodity markets and its impacts. Prior research suggests important market changes due to the growth in  investment and participation by non-traditional actors. Whether financialization is beneficial, harmful or neutral to traditional market participants is still debated. This paper assesses the influence of financialization by investigating the high-frequency impact of macroeconomic announcements on commodity futures returns and volatility. We find that overall, financialization appears to lessen the impact of macro news on commodity markets, as measured by price drift and volatility changes, consistent with prior literature suggesting that financial participants improve liquidity and price discovery and reduce volatility. Assuming traditional market participants prefer stability, our results suggest a beneficial impact of financialization. The effect is robust for three different definitions of financialization. The evidence is stronger for pro-cyclical commodities than for gold.

Stock-Oil Comovement: Fundamentals or Financialization?
DISCUSSANT: Veronika Selezneva

ABSTRACT. We investigate the sources of time-variation in the stock-oil correlation over the period 1983-2019. We first derive a novel oil futures return news decomposition following Campbell and Shiller (1988) and Campbell (1991). Then, for both stock and oil, we split unexpected returns into cash flow news (which can be related to asset-specific fundamentals) and discount rate news (which can be driven by shocks to investors holding both assets) using a vector autoregressive (VAR) model. We find that about 79% of the time-varying correlation is related to the comovement of cash flow news between the two assets. This result is robust to different specifications of the VAR model used to decompose returns. We provide supportive evidence that underlying changes in the structure of the real economy, such as the increased oil production in the U.S., are key drivers for the changing stock-oil comovement beyond the financialization of commodities.

13:00-14:30 Session 2B: Power Generation 1
Location: Room 2
The Reliability Pricing Mechanism and Coal-Fired Generators in PJM

ABSTRACT. We study the effects of the Reliability Pricing Model (RPM) on the operational decisions of coal plant managers in the PJM electricity market. We study switching between the operational state and retirement. We focus on the period from 2001 through 2018, a period with significant changes in power market dynamics. The empirical data show that coal generators are more likely to retire after the advent of RPM. The advent of a capacity market hastened the demise of coal-fired generators in PJM.

Joint optimization of sales-mix and generation plan for a large electricity producer
PRESENTER: Paolo Falbo
DISCUSSANT: Alexander Kronies

ABSTRACT. The paper develops a typical management problem of a large power producer (i.e. he can partly influence the market price). In particular, he routinely needs to decide how much of his generation it is preferable to commit to fixed price bilateral contracts (e.g. futures) or to the spot market. However, he also needs to plan how to distribute the production across the different plants under his control. The two decisions, namely the sales-mix and the generation plan, naturally interact, since the opportunity to influence the spot price depends, among other things, by the amount of the energy that the producer directs on the spot market. We develop a risk management problem, since we consider an optimization problem combining a trade-off between expectation and conditional value at risk of the profit function of the producer. The sources of uncertainty are relatively large and encompass demand, renewables generation and the fuel costs of conventional plants. We also model endogenously the price of futures in a way reflecting an information advantage of a large power producer. In particular, it is assumed that the market forecast the price of futures in a naive way, namely not anticipating the impact of the large producer on the spot market. The paper provides a MILP formulation of the problem, and it analyzes the solution through a simulation based on Spanish power market data.

The bigger, the better?
DISCUSSANT: Stein-Erik Fleten

ABSTRACT. I empirically investigate wind energy production dynamics and correlations to electricity prices on a turbine-individual level. I find that large turbine production outputs are less negatively correlated to electricity prices than those of small turbines. Apart from the fact that large turbines produce more and are less volatile in their production outputs over time, they sell electricity at a higher average price, outperforming their smaller peers. Additional tests on high-frequency data confirm these results and indicate that the financial impacts are large when considering short-term dynamics. These findings are important for investors to consider when allocating capital to alternative venues as renewable energy.

13:00-14:30 Session 2C: Commodity Pricing 1
Location: Room 3
A Model of Price Correlations between Clean Energy Indices and Energy Commodities
DISCUSSANT: Valerio Potì

ABSTRACT. This paper theoretically and empirically examines the relationship between environmental value embedded in clean energy indices and energy value obtained from energy prices by focusing on the influence of energy risk on clean energy business including renewables. We propose a supply and demand-based correlation (CR) model of clean energy indices and energy prices that takes into account the influence of energy on clean energy business including renewables. We also propose a market risk model based on CR model to conduct the risk management for stocks of clean energy firms appropriately. Empirical studies estimate the model parameters using the stock indices and energy prices including S&P Global Clean Energy Index (GCE), Wilderhill Clean Energy Index (ECO), S&P/TSX Renewable Energy and Clean Technology Index (TXCT), S&P 500, WTI crude oil prices, and Henry Hub (HH) natural gas prices. It is shown by using the model that the correlations between GCE or ECO and WTI crude oil or HH natural gas prices be positive and be an increasing function of the corresponding energy prices. Results seem reasonable because the values of renewable energy businesses, which sell electricity in the spot market, are enhanced by the increase in energy prices, considering that electricity spot prices tend to increase in line with energy prices. In contrast, it is also shown that the correlations between S&P 500 and WTI or HH prices be still positive but be a decreasing function of the energy prices. This sharp contrast may come from the fact that the S&P 500 listed companies’ businesses can be damaged by high energy prices while not applicable to GCE and ECO companies. Regarding TXCT, the correlations with WTI are positive and are a decreasing function of WTI while those with HH tend to be positive and are an increasing function of HH. It may suggest that TXCT is not fully functioning but still developing as a clean energy index, taking into account the results of GCE and ECO. Regarding market risk, CR model demonstrates different VaR from ordinary normal distribution (OND) model because CR model includes more upward or downward sloping demand curve shape reflecting the reality of the markets than the exponential in OND model, resulting in positive or negative impacts of prices on the volatilities in high clean energy index regions, respectively. We compare CR model with existing dynamic conditional correlation (DCC) model. Since CR model demonstrates the same level of the correlations from DCC model, CR model can work well as the correlation model.

Commodity Pricing: Evidence from Rational and Behavioral Models
PRESENTER: Valerio Potì
DISCUSSANT: Alain Kabundi

ABSTRACT. In this paper, we study commodity pricing. To explain commodity prices and return volatility, we consider both a classical fundamental-based model with a rational representative agent and a behavioral extension with heterogeneous agents. We formally examine the role of speculators, in particular in relation to the super cycle in commodities and the time period most associated with the so-called financialization of commodities. We examine a total of 15 commodities covering agriculture, softs, energy and metals and a sample where possible covers the period from 1959 to 2017. In all cases, especially in the final part of the sample period, we reject the restrictions associated with the rational representative agent model. Our behavioral model, which augments the fundamentals based investor with both heterogeneous investors and heterogeneous horizons, performs much better and in particular during the commodity super cycle and the recent period of financialization of commodities.

Commodity Price Shocks: Order within Chaos?
PRESENTER: Alain Kabundi
DISCUSSANT: Takashi Kanamura

ABSTRACT. The prices of 27 internationally-traded commodities are decomposed into transitory and permanent components by applying an ideal band-pass filter to monthly data during 1970-2019. We find that transitory and permanent shocks accounted for roughly equal contributions to price variations, although with wide heterogeneity. Permanent shocks accounted for two-thirds of the variability in annual agricultural prices but less than half of the variability in base metals prices. For energy, the permanent shock component has trended upward, for agriculture, downwards, and for metals, flat. We also find that the transitory shock component reflects three medium-term cycles. The first cycle (from the early 1970s to mid-1980s) and third cycle (from the early 2000s to 2020 onwards) exhibit similar duration and involve all commodities; the second cycle (spanning the 1990s) is mostly applicable to agriculture and metals (with the nota-ble absence of energy) and exhibited smaller amplitude and duration.

13:00-14:30 Session 2D: Agricultural Commodities 1
Location: Room 4
The Euronext Wheat Market: Participants and their Importance
PRESENTER: Alexis Poullain

ABSTRACT. We utilize confidential regulatory data to identify a typology of stakeholders on the Euronext milling wheat (“EBM”) market, and to document key patterns of market participation. Specifically, we identify the relative importance of different types of end-customers in the wheat market through 2019—in terms of both intraday transactions and overnight positions.

For end-of-day positions, we differentiate between financial operators (with three separate categories for investment firms, funds, and other financial institutions ) and commercial actors (a category that groups producers, storers, transformers, and physical-market dealers and merchants). Euronext wheat is not part of any of the major investable commodity indices, and indeed we find virtually no long-only commodity index traders (CITs). Commercial traders are (in the aggregate) virtually net zero across all maturities. This pattern is in sharp contrast with US grain and oilseed futures markets, where regulatory data show that CITs account for over 30 percent of the long open interest while non-CIT commercial traders (as a group) are sharply net short.

Intuitively, insofar as CITs are price-insensitive, one would expect that the risk premia would be different in both markets. We propose a simple theoretical model tying the CITs’ aggregate long position to the difference between futures and expected future spot prices, and provide supporting empirical evidence on the difference between the risk premia in the US soft red winter vs. Euronext wheat markets.

For intraday transactions, we provide novel evidence that (in the Euronext wheat market, at this specific point in time) the three largest trader categories are (in order of decreasing contribution to overall volume) “point-and-click” arbitragers, ultra-fast traders, and large commercial traders. We document activity patterns of these types of traders during the delivery period, and show that “point-and-click” traders contribute substantially to the trading volume both before and during the maturity period.

Market Sentiment around USDA Announcements
DISCUSSANT: Michael Adjemian

ABSTRACT. In the past few decades, a sizable portion of total annual realized equity returns have been realized around scheduled meetings of the Federal Open Market Committee (Lucca and Moench 2015). Likewise, a large literature in agricultural economics shows that periodic USDA reports move grains and oilseed prices significantly and often very substantially. The fact that agricultural markets react strongly to USDA announcements supports the notion that they bring valuable information to the market and help resolve uncertainty about fundamental demand and supply.

Most of the agricultural economics literature to date focuses on what happens to prices following USDA announcements (e.g., Adjemian 2012; Karali et al. 2019; Ying et al. 2019) or on how fast the new information is impounded into commodity prices (e.g., Adjemian and Irwin 2018; Lehecka et al. 2014). In order to capture the full impact of the USDA reports, though, it is necessary to also investigate how market sentiment and uncertainty change around the announcements. This is our purpose in the present paper.

It is customary in financial economics to rely on option-implied volatilities as a proxy for forward-looking market uncertainty. This same approach has also been taken for USDA reports—see McNew and Espinosa (1994) and, most recently, Isengildina-Massa et al. (2008). In this context, Bekaert et al. (2013) show that the VIX index (i.e., the Standard and Poor 500 equity-index option-implied volatility) captures both heightened uncertainty about global macroeconomic conditions and risk aversion among investors. The same should be true in agricultural markets. In particular, insofar as risk aversion permeates all asset markets, risk aversion levels in commodity markets should move at least partly in sync with equity-market risk aversion. Likewise, insofar as the demand for physical commodities reflects the strength of the global economy, uncertainty about the latter should percolate to agricultural markets. Indeed, Adjemian et al. (2016) show that forward-looking volatility expectations in grain and oilseed markets are driven significantly by the VIX index. In the present paper, we therefore investigate the behavior of commodity option-implied volatilities around USDA announcements after controlling for changes in the VIX.

A second innovation of this paper is that it accounts explicitly for expert opinions and uncertainty about the USDA information. Specifically, we account for the fact that, prior to all major USDA announcements, companies like Bloomberg and Reuters conduct surveys about market analysts’ expectations regarding the upcoming reports. Details about those surveys are typically released in the week before the announcement. We make use of this information as a proxy for commodity-specific uncertainty and sentiment, which might affect market reactions following the report release. We combine a set of statistical tests and regressions in an event-study framework – the standard methodology for studying announcement effect (Binder 1998). The results of our study shed light on how USDA announcement help resolve uncertainty. Focusing on implied volatility, our study goes beyond the extant literature in at least three ways: (i) our analysis provides evidence on uncertainty and sentiment prior to the announcement, which offers a closer look into how these variables evolve; (ii) it teases out the respective impacts of global vs. commodity-specific market uncertainty and sentiment; and (iii) our sample extends the set of USDA reports over time and covers all four groups of USDA reports.

Our findings offer both practical and policy implications for market participants and policy makers. Short-run hedging or investment decisions around USDA announcement can be improved by considering the forecast-to-announcement structures of forward-looking volatility, leading to more efficient pricing in the long run. Public programs involving price volatility, such as crop insurance (Sherrick 2015) or USDA season-average price forecasts that incorporate forward-looking volatility (as advocated by Adjemian et al. 2019) should also benefit from our conclusions.

What Drives Volatility in Agricultural Futures Markets
PRESENTER: Michael Adjemian
DISCUSSANT: Alexis Poullain

ABSTRACT. We exploit unique features of agricultural markets to establish empirically that both global uncertainty and commodity-specific shocks drive forward-looking volatility in commodity markets. The relative importance of global vs. idiosyncratic factors varies across commodities and time. Specifically, in period of elevated financial-markets stress, macroeconomic uncertainty amplifies commodity-market volatility; the opposite is true during economic expansions. The impact of inventory shocks, in contrast, is asymmetrical: (near-)stockouts boost commodity market volatility to a greater extent than plentiful stocks moderate it. Financial speculation has an immediate, but short-lived and negative, impact on price volatility.

15:00-16:30 Session 3A: Market Efficiency
Location: Room 1
Market Inefficiencies Surrounding Energy Announcements
PRESENTER: Sultan Alturki
DISCUSSANT: Amine Loutia

ABSTRACT. We use sequential energy inventory announcements to shed new light on the informational efficiency of financial markets. Our findings provide clear evidence of inefficiency in oil futures and stock markets. This inefficiency can be exploited by sophisticated traders. We examine the effect of market conditions, such as liquidity and oil attention, on the efficient incorporation of information in this setting. We also construct a predictor that can predict inventory surprises and pre-announcement returns in-sample and out-of-sample. Finally, we develop a combination forecast that can be used as a proxy for market expectations of oil inventory announcements.

OPEC production announcements: Effects on stock prices
PRESENTER: Amine Loutia
DISCUSSANT: Robert Ready

ABSTRACT. We examine evidence on the impact of OPEC quota production announcements on stock markets by employing a 3-factor Fama-French Model coupled with an EGARCH model to capture some important features of stock markets. We adopt the event study methodology and use data of stock markets between Q1 1991 and Q3 2015 and three indices as benchmarks (S&P Global 1200, MSCI ACWI AND MSCI Global). We find that stock prices responses to OPEC’s announcements depend on the index used, react more significantly to production cut and increase and affected by industries’ idiosyncratic features.

The Day that WTI Died: Asset Prices and Firm Production Decisions
DISCUSSANT: Sultan Alturki

ABSTRACT. On April 20, 2020 the flagship North American benchmark price for crude oil, West Texas Intermediate (WTI) for delivery in Cushing, OK, went negative for the first time in history, settling at -$37/bbl. We document that this event had a widespread effect on physical purchase contracts throughout the United States, despite ample storage capacity at many locations. We show that firms with crude purchase contracts indexed to WTI shut in production after this event at greater rates than those that did not. The difference in behavior is strongest among high productivity wells and occurs despite an overall improvement in pricing and crude market fundamentals after the April 20th event. Our evidence suggests that asset prices have important implications for the real actions of firms due to their importance as price setting mechanisms in contracting.

15:00-16:30 Session 3B: Commodity Investment 1
Location: Room 2
The valuation effects of index investment in commodity futures
PRESENTER: Loïc Maréchal
DISCUSSANT: Fabio Moneta

ABSTRACT. We identify and date a significant surge in the amount of investment tracking commodity futures indices. Using a difference-in-differences setting on cumulative abnormal log price changes, computed with several benchmarks during the roll window of the SPGSCI, we first find that the uncovered break in the speculative investment structure had an alleviating effect. Second, we explain the abnormal nearby and first deferred contracts price changes by measures of risk (liquidity) premium required at long (short)term horizon by speculative (hedging) activity. Finally, we find that transaction costs incurred by an arbitrager (price taker) explain most of the abnormal term-structure change. In addition, this abnormal change -which is of 26 basis points at most- is never significant once we adjust the standard errors for event-induced variance and cross-correlation.

The Challenges of Oil Investing: Contango and Crowding
PRESENTER: Fabio Moneta
DISCUSSANT: Andrew Vivian

ABSTRACT. The ability of oil investment vehicles to perfectly track spot oil has always been challenging, however recently many vehicles have underperformed spot oil. We study the behavior of oil futures and exchange-traded products that invest in oil futures to document and understand the source of this tracking error. The primary reason that oil investment vehicles have underperformed spot oil is due to an increase in contango in oil futures markets that we find might be related to investment crowding and the financialization of commodity markets. We show that from 2006 to 2017 oil futures investing underperformed spot oil and the market was in contango most of the time.Proxies for crowding, such as the concentration of major oil investors and changes in assets under management and fund flows of major oil exchange-traded products are associated with contango in the futures markets and the divergence between futures and spot returns. We also provide evidence of an impact of the financialization on oil futures prices.

Roll Reversal in Commodity Futures
PRESENTER: Andrew Vivian
DISCUSSANT: Loïc Maréchal

ABSTRACT. The relationship between commodity futures differing only in terms of time to maturity has long been of interest to academics, practitioners and policymakers. “Roll yield” signals based on the futures term structure can generate substantial profits in the cross-section of commodity returns (see for example Fuertes et al., 2010). However, to the best of our knowledge, neither the time-series aspects of these “roll yield” signals nor the time-series returns of the first maturity contract relative to the second contract have received detailed prior investigation. In this paper we propose that there is time-series reversal in the relative price of the first and second maturity contracts, which can be predicted by the roll yield. These reversals towards normal values is consistent with the correction of shocks and / or a pull towards fundamental values. We demonstrate that spread trading strategies based on these time-series reversals are profitable and generate substantial Sharpe ratios.

15:00-16:30 Session 3C: Renewable Energies 1
Location: Room 3
Optimal Electricity Distribution Pricing under Risk and High Photovoltaics Penetration
PRESENTER: Maxim Bichuch
DISCUSSANT: Almendra Awerkin

ABSTRACT. A hierarchical Stackelberg game is studied in a competitive power market under high Photovoltaics (PV) penetration and demand-side uncertainty. The Stackelberg leader, who is the government regulator, attempts to define a set of network tariffs that results in maximal overall system net benefits with consideration of consumer utility, cost recovery, and renewable energy promotion. The Stackelberg followers, who are rational consumers of electricity, optimize individual PV investment and maximize personal utility. With the consumers' demand evolution described by a discretized Ornstein--Uhlenbeck process, we show mathematical results on closed form approximations and existence of game equilibrium. Numerical results are calibrated to PJM power market data, and illustrate the market participants' coupled decisions. Results suggest that consumers tend to rely more on PV when the market demand is more volatile, with potential risks of the death spiral where the high electricity retail price resulting from increased distributed generation further incentivizes PV investment.

Optimal installation of renewable electricity sources: the case of Italy
PRESENTER: Almendra Awerkin
DISCUSSANT: Giorgio Rizzini

ABSTRACT. Starting from the model in \cite{KV}, we test the real impact of current renewable installed power in the electricity price in Italy, and assess how much the renewable installation strategy which was put in place in Italy deviated from the optimal one obtained from the model in the period 2012-2018. To do so, we consider the Ornstein-Uhlenbeck (O-U) process, including an exogenous increasing process influencing the mean reverting term, which is interpreted as the current renewable installed power. Using real data of electricity price, photovoltaic and wind energy production from the six main Italian price zones, we estimate the parameters of the model and obtain quantitative results, such as the production of photovoltaic energy impacts the North zone, while wind is significant for Sardinia and the Central North zone does not present electricity price impact. Then we implement the solution of the singular optimal control problem of installing renewable power production devices, in order to maximize the profit of selling the produced energy in the market net of installation costs. We extend the results of \cite{KV} to the case when no impact on power price is presented, and to the case when $N$ players can produce electricity by installing renewable power plants. We are thus able to describe the optimal strategy and compare it with the real installation strategy that was put in place in Italy.

ETS, Emissions and the Energy-Mix Problem
PRESENTER: Giorgio Rizzini
DISCUSSANT: Maxim Bichuch

ABSTRACT. In this paper, we investigate the impact of ETS on the emissions and the energy-mix through a bilevel model where a policymaker interacts with an oligopolistic electricity market over a finite time horizon. At the upper level, the policymaker aims at maximizing a welfare function deciding the optimal number of allowances to be distributed to the electricity market. At the lower level, the electricity market, represented by two large companies, decide the optimal long-term capacity expansion between conventional and non-conventional technologies. The uncertainty is modelled through scenarios, obtained using Markov chain bootstrapping, made of coal and gas prices and electricity demand. We solve the problem considering a large set of efficient equilibrium solution between the two electricity producers. We provide a model calibration through real data and a detailed comparative statics.

15:00-16:30 Session 3D: Risk Analysis and Hedging 1
Location: Room 4
A New Integrated Risk-Management Policy for the Newsvendor Position
PRESENTER: Andrea Roncoroni

ABSTRACT. We show that integrating the optimal combined custom hedge developed by Guiotto and Roncoroni (2019) with the optimal procurement policy in a newsvendor model allows a firm to obtain a signifi- cant improvement in both risk and return and the resulting gain may be traded off for a substantial enhancement in operational flexibility.

Robust static hedging of price and volumetric risk
DISCUSSANT: Nicola Secomandi

ABSTRACT. We consider the problem of maximizing a quadratic function plus a piece-wise linear term where the parameters are subject to uncertainty. Because of the uncertain parameters, we cannot solve our problem by mathematical programming methods. Instead, we use robust optimization techniques--and the Fenchel duality theorem--to find the robust counterpart of our problem. Our solution considers polyhedral and ellipsoidal uncertainty sets.

As an application, we consider the case of a load-serving entity that seeks to hedge against price and volumetric risk. Using a bond, future and put, we build two portfolios with the electricity price and a weather index as underlying assets. We find that hedging, as expected, reduces the potential loss in revenue. We also show that the hedging performance is affected when the volume is not considered as uncertain--a factor regularly overview in the literature.

Quadratic Hedging of Futures Term Structure Risk in Merchant Energy Trading Operations
PRESENTER: Nicola Secomandi
DISCUSSANT: Andrea Roncoroni

ABSTRACT. Merchant energy trading companies manage conversion assets to exploit price differences across time, space, and sources of energy in the face of energy futures term structure risk. Financial hedging of this risk is thus standard practice. Market incompleteness, such as limited futures liquidity, complicates the management of this activity. We apply quadratic hedging, a pragmatic approach to mitigate financial risk when markets are incomplete, to the management of term structure risk in real option models of merchant energy trading operations. We develop a model that, in contrast to known applications of this methodology, pools cash flows across dates, establish the structure of its optimal policy, which is intractable to obtain, and use it to propose a novel computationally efficient heuristic. This method is provably optimal under a martingale assumption for the futures curve evolution. Our technique performs near optimally in a realistic numerical study focused on natural gas storage in which this assumption does not hold, outperforming a benchmark that relies on it. The procedure put forth in this paper has potential applicability beyond this setting.

16:30-18:00 Session 4A: Cryptocurrencies and Gold
Location: Room 1
Success of Initial Coin Offerings
PRESENTER: Michael Dakos
DISCUSSANT: Manuela Pedio

ABSTRACT. This paper examines the success determinants of initial coin offerings (ICOs). We review the relevant literature, develop our hypotheses and conduct preliminary data analysis. ICO fundraising success proxies and their determinants will be analysed using linear and probit regression models.

Time-Varying Risk Exposures of Cryptocurrencies: Are They a New Asset Class?
PRESENTER: Manuela Pedio

ABSTRACT. Despite the interest in cryptocurrencies has recently increased, in the literature a consensus has not yet been reached on whether they represent or not a new asset class, spanning risks and payoffs sufficiently different from the traditional ones. We contribute to this debate by studying the exposure of cryptocurrency returns to stock market risk factors (namely, the six Fama French factors), to precious metal commodity returns, and to cryptocurrency-specific risk-factors (namely, crypto-momentum, a sentiment index based on Google searches, and supply factors, i.e., electricity and computer power). Because economic facts lead us to believe that those exposures are likely to be time-varying, we rely on Bayesian methods, which incorporate dynamic model averaging. These methods not only feature time-varying coefficients, but also allow for the entire forecasting model to change over time. We estimate our flexible models on weekly data for four popular cryptocurrencies, namely Bitcoin, Ethereum, Litecoin and Ripple. We find that cryptocurrencies are not systematically exposed to stock market factors, precious metal commodities or supply factors with exception of some occasional spikes of the coefficients during our sample. On the contrary, they display a time-varying but significant exposure to a sentiment index and to crypto-momentum. However, cryptocurrencies display considerable diversification power in a portfolio perspective and as such they can generate differential Sharpe ratios and certainty equivalent returns in spite their overall predictability turns out to be weaker vs. traditional asset classes.

Green Gold - A Gold Mining Perspective
DISCUSSANT: Michael Dakos

ABSTRACT. Gold is a long-lasting, durable and thus sustainable metal and asset. However, mining for gold often adversely affects the environment. This study proposes an alternative to mitigate these negative externalities and costs of gold mining. Instead of digging out gold for investment purposes we propose to leave it in the ground and let nature act as a natural vault and custodian legally protected by gold firms and the government. Empirically, we analyse whether portfolios of gold exploration companies with access to such “green” gold also provide exposure to the world price of gold. The results demonstrate that gold mining is not necessary to give investors access to gold.

16:30-18:00 Session 4B: Derivatives and Risk-Neutral Dynamics
Location: Room 2
Estimation of the risk neutral freight rate dynamics: A nonparametric approach
PRESENTER: Nikos Nomikos
DISCUSSANT: Annika Kemper

ABSTRACT. In this paper, we present a new approach for estimating the drift of the risk neutral spot freight rate process, which is not observable, directly from Forward Freight Agreement (FFA) prices without requiring knowledge of some prior closed-form model solution. We demonstrate our approach is robust by means of numerical simulations. We make empirical experiments which involve the estimation of two standard parametric models from the relevant literature and a non-parametric model using data from the Baltic Capesize, Panamax and Supramax indices. We find that our proposed methodology with a non-parametric estimation approach yields the lowest pricing errors across different maturities. Finally, we obtain the market price of risk as the difference between the drift under the physical and risk neutral measures and analyze its behaviour in the in-sample and out-sample periods, and find that the market price of risk estimated with a non-parametric approach evolves consistently with the considered indices.

The Market Price of Risk for Delivery Periods: Pricing Swaps and Options in Electricity Markets
PRESENTER: Annika Kemper

ABSTRACT. In electricity markets futures deliver the underlying over a period and thus function as a swap contract. In this paper we introduce a market price of risk for delivery periods of electricity swaps. In particular, we suggest a weighted geometric average of an artificial geometric electricity futures price over the corresponding delivery period. This leads to a geometric electricity swap price dynamics without any approximation requirements. Our framework allows to include typical features as the Samuelson effect, seasonalities as well as a stochastic volatility in the absence of arbitrage. We show that our suggested model is suitable for pricing options on electricity swaps using the Heston method. Especially, we illustrate the related pricing procedure for electricity swaps and options in the setting of Arismendi et al. (2016), Schneider and Tavin (2018) and Fanelli and Schmeck (2019).

Jumps in Commodity Prices: New Approaches for Pricing Options
DISCUSSANT: Nikos Nomikos

ABSTRACT. We present a new term-structure model for commodity futures prices based on Trolle and Schwartz (2009), which we extend by incorporating multiple jump processes. Our work explores the valuation of plain vanilla options on futures prices when the spot price follows a log-normal process, the forward cost of carry curve and the volatility are stochastic variables, and the spot price and the forward cost of carry allow for time-dampening jumps. We obtain an analytical representation of the characteristic function of the futures prices and, hence, also for option prices. We price options on WTI crude oil futures contracts using our model and extant models. We obtain higher accuracy than earlier models and save significantly in computing time.

16:30-18:00 Session 4C: Forecasting
Location: Room 3
Oil price analysts' forecasts
DISCUSSANT: Massimo Guidolin

ABSTRACT. Crude oil analysts provide forecasts on future spot prices, which arecollected by the Bloomberg database. We exploit this survey to compareanalysts’ forecasting ability to futures contracts and also among analyststhemselves. We address the problems that arise with unstructured analystforecast data and use the Mean-Squared Prediction Error (MSPE) relativeto the no-change forecast and a bootstrapped Diebold and Mariano test.We show that the applied approach represents a substantial improvementwhen compared to the standard MSPE methodology as it corrects forvolatility and maturity effects on the measures of forecasting performance.Finally, we establish that futures prices supersede analyst forecasts andelaborate a performance-based ranking of analyst firms.

Distilling Large Information Sets to Forecast Commodity Returns: Automatic Variable Selection or Hidden Markov Models
PRESENTER: Massimo Guidolin
DISCUSSANT: Bartosz Uniejewski

ABSTRACT. We investigate the out-of-sample, recursive predictive accuracy for (fully hedged) commodity future returns of two sets of forecasting models, i.e., hidden Markov chain models in which the coefficients of predictive regressions follow a regime-switching process and stepwise variable selection algorithms in which the coefficients of predictors not selected are set to zero. We perform the analysis under four alternative loss functions, i.e., squared and the absolute value, and the realized, portfolio Sharpe ratio and MV utility when the portfolio is built upon optimal weights computed solving a standard MV portfolio problem. We find that neither HMM or stepwise regressions manage to systematically (or even just frequently) outperform a plain vanilla AR benchmark according to RMSFE or MAFE statistical loss functions. However, in particular, stepwise variable selection methods create economic value in out-of-sample mean-variance portfolio tests. Because we impose transaction costs not only ex-post but also ex-ante, so that an investor uses the forecasts of a model only when they increase expected utility, the economic value improvement is maximum when transaction costs are taken into account.

Regularized Quantile Regression Averaging for probabilistic electricity price forecasting

ABSTRACT. Quantile Regression Averaging (QRA) has sparked interest in the electricity price forecasting community after its unprecedented success in the Global Energy Forecasting Competition 2014, where the top two winning teams in the price track used variants of QRA. However, recent studies have reported the method's vulnerability to low quality predictors when the set of regressors is larger than just a few. To address this issue, we consider a regularized variant of QRA, which utilizes the Least Absolute Shrinkage and Selection Operator (LASSO) to automatically select the relevant regressors. We evaluate the introduced technique – dubbed LASSO QRA or LQRA for short – using datasets from the Polish and Nordic power markets, a set of 25 point forecasts obtained for calibration windows of different lengths and 20 different values of the regularization parameter. By comparing against nearly 30 benchmarks, we provide evidence for its superior predictive performance in terms of the Kupiec test, the pinball score and the test for conditional predictive accuracy.

16:30-18:00 Session 4D: Risk Analysis and Hedging II
Location: Room 4
Convenience Yield Risk
PRESENTER: Robert Wichmann
DISCUSSANT: Giacomo Morelli

ABSTRACT. This paper proposes a measure of convenience yield risk that incorporates seasonality, and more than only the front slope of the term structure of commodity futures. We find that convenience yield risk predicts commodity futures returns in the cross section and the time series. The predictive power is not captured by known commodity predictors. Our measure is related to the Samuelson effect, the basis, and global financial conditions.

Responsible Investments Reduce Market Risks
PRESENTER: Giacomo Morelli

ABSTRACT. Sustainability is considered one of the driving factors of revenue growth and a key topic in recent research. This paper investigates the effects of sustainability on the volatility of European stock returns. Using the Environmental score (ES) provided by Thomson Reuters as a proxy for sustainability, we first exploit an Expectation-Maximization (E-M) algorithm to discriminate companies included in the STOXX Europe 600 index into two groups composed by high and low ES. Second, we build global minimum variance portfolios (GMV) and estimate the volatility of the high and low portfolios obtained through ARCH-type models. Finally, we compute well-known market risk measures such as the Value-at-Risk (VaR) and the Expected Tail Loss (ETL) to assess market risks for investing green. Overall, high ES portfolios outperform their low ES counterparts and are shown to be safer in terms of VaR and ETL, during periods of market distress. The results are remarkable for the Energy sector.

Dynamic Optimality of Airline Fuel Cost Hedging
DISCUSSANT: Marcel Prokopczuk

ABSTRACT. Hedging creates value only when the policy is optimal. This paper takes US airlines as an example and derives their optimal fuel cost hedging ratio as a function of the airline’s revenue and cost sensitivities, and the relative composition of demand and supply shocks in the oil price movement. We construct a market hedging demand index and use the correlation of an airline’s hedging ratio with the index to measure the dynamic optimality of its hedging practice. The measure strongly and positively predicts the airline’s Tobin's Q, but less than one third of their hedging practices show positive dynamic optimality.

18:30-20:00 Session 5: Keynote Speech: Lutz Kilian
Location: Room 5
Oil Prices, Gasoline Prices and Inflation Expectations
PRESENTER: Lutz Kilian

ABSTRACT. It has long been suspected, given the salience of gasoline prices, that fluctuations in gasoline prices shift households’ one-year inflation expectations. Assessing this view empirically requires the use of dynamic structural models to quantify the cumulative effect of gasoline price shocks on household inflation expectations at each point in time. We find that, on average, gasoline price shocks account for about one third of the variation in these expectations. The cumulative increase in household inflation expectations from early 2009 to early 2013, in particular, is almost entirely explained by unexpectedly rising gasoline prices. Since much of this effect occurred only toward the end of this period, however, this result does not support the popular view that the improved fit of the Phillips curve augmented by household inflation expectations during 2009-13 is explained by rising gasoline prices.