nep-rmg New Economics Papers
on Risk Management
Issue of 2015‒08‒01
nine papers chosen by

  1. Comonotonic Monte Carlo and its applications in option pricing and quantification of risk By Alain Chateauneuf; Mina Mostoufi; David Vyncke
  2. Remote Sensing and Risk Management Tools By Hatfield, Jerry
  3. Systemic Risk, Aggregate Demand, and Commodity Prices By Javier G. Gómez-Pineda; Dominique Guillaume; Kadir Tanyeri
  4. Why Quantitative Structuring? By Andrei N. Soklakov
  5. Risking-sharing Efficiency of Hedging Strategies By van Kooten, G Cornelis; Guo, Changhao; Sun, Baojing
  6. Is “Good Enough” Good Enough when Hedging Agricultural Commodities? By Dahlgran, Roger A
  7. Risk Reduction and the 2014 Farm Bill By Hungerford, Ashley; O'Donoghue, Erik; Motamed, Mesbah
  8. Detect & Describe: Deep learning of bank stress in the news By Samuel R\"onnqvist; Peter Sarlin
  9. What is the Expected Return on the Market? By Martin, Ian

  1. By: Alain Chateauneuf (IPAG Business School et Centre d'Economie de la Sorbonne - Paris School of Economics); Mina Mostoufi (Centre d'Economie de la Sorbonne - Paris School of Economics); David Vyncke (Universiteit Gent)
    Abstract: Monte Carlo (MC) simulation is a technique that provides approximate solutions to a broad range of mathematical problems. A drawback of the method is its high computational cost, especially in a high-dimensional setting, such as estimating the Tail Value-at-Risk for large portfolios or pricing basket options and Asian options. For these types of problems, one can construct an upper bound in the convex order by replacing the copula by the comonotonic copula. This comonotonic upper bound can be computed very quickly, but it gives only a rough approximation. In this paper we introduce the Comonotonic Monte Carlo (CoMC) similation, by using the comonotonic approximation as a control variate. The CoMC is of broad applicability and numerical results show a remarkable speed improvement. We illustrate the method for estimating Tail Value-at-Risk and pricing basket options and Asian options when the logreturns follow a Black-Scholes model or a variance gamma model
    Keywords: Control Variate Monte Carlo; Comonotonicity; Option pricing
    JEL: G17 C02 C13 C15 C63
    Date: 2015–02
  2. By: Hatfield, Jerry
    Keywords: Agricultural and Food Policy, Risk and Uncertainty,
    Date: 2015–02–19
  3. By: Javier G. Gómez-Pineda (Banco de la República de Colombia); Dominique Guillaume; Kadir Tanyeri
    Abstract: The paper presents a global model for analysis and projections. The model features a handful of elements that make it suitable for analyzing three broad sets of topics; first, systemic risk and its transmission to country risk premiums; second, the transmission from country risk premiums to demand-related variables such as the output gap, the trade balance, and unemployment; and third, the transmission from commodity prices to country inflation. The model incorporates one systemic risk channel and two foreign channels, specifically, a foreign aggregate demand channel and a foreign exchange rate channel. The model is estimated with Bayesian methods. In addition, the effect of risk on aggregate demand is calibrated with the aid of a VAR. Among the results are that the episodes of surges in systemic risk identified in the paper were transmitted to country risk premiums and aggregate demand--related variables; that the effect of systemic risk shocks on world economic activity is large, and that the busts in the world output gap correspond with the major financial events identified by the estimated time series for the unobserved systemic risk. In addition, systemic risk shocks are important drivers of output gaps while country risk premium shocks can have important effects on the trade balance. Surprisingly, commodity prices, in particular the price of oil, are shown to be demand driven; hence, demand related factors may play a nontrivial role in explaining noncore inflation. The model performed well at one- and four-quarter horizons compared to a survey of analysts' forecasts. In addition, systemic risk shocks were important at explaining the forecast variance of the world output gap, country output gaps, the price of oil, and country risk premiums. The breath of reach of systemic risk shocks back the efforts for financial surveillance with a systemic focus. Classification JEL: F32, F37, F41, F31, F47, E58
    Keywords: Systemic risk, Financial linkages, Capital flows, Global imbalances Commodity prices
    Date: 2015–07
  4. By: Andrei N. Soklakov
    Abstract: Look at the quality-driven success of manufactured goods. Wouldn't it be great if the quality of financial products became just as apparent? A positive answer to this question leads us to Quantitative Structuring -- a technology of manufacturing financial products. This paper is a fast introduction into Quantitative Structuring. More detailed discussions are presented as a set of appendices. Further details are given in the form of references to specific results including applications beyond product design: from model risk to economics.
    Date: 2015–07
  5. By: van Kooten, G Cornelis; Guo, Changhao; Sun, Baojing
    Abstract: Since agricultural production is significantly and directly influenced by weather, financial weather products based on temperature have been developed in recent decades. The crop producer can now hedge adverse temperature outcomes in either the exchange market or the over-the-corner (OTC) market. However, exchange-traded contracts invariably carry geographic basis risk because of differences in the market-quoted and local temperature outcomes. OTC option contracts, on the other hand, are at risk of possible default by the counterparty. Therefore, a portfolio combining OTC with exchange-traded contracts could potentially be used by crop producers to reduce overall income risk. In this paper, we examine the performances of these three alternative hedging strategies on the uncertainty of crop producer’s income. Using a case study for western Canada, we find that a portfolio that combines OTC and exchange-traded contracts provides a most effective means of reducing potential risks, compared with stand-alone OTC contracts or exchange-market contracts because of their higher default and geographic basis risks, respectively.
    Keywords: hedging agricultural risk, financial weather derivatives, default and basis risk, agricultural risk management, Agribusiness, Agricultural Finance, Crop Production/Industries, Financial Economics, Q14, G32, G11, C54, C63,
    Date: 2015
  6. By: Dahlgran, Roger A
    Abstract: First draft. Selected paper prepared for presentation at the 2015 Agricultural and Applied Economics Association and Western Agricultural Economics Association Annual Meeting San Francisco, CA , July 26-28, 2015.
    Keywords: hedging, cross-hedging, localized basis, price-risk management, Agribusiness, Financial Economics, Marketing, Risk and Uncertainty, Q13,
    Date: 2015
  7. By: Hungerford, Ashley; O'Donoghue, Erik; Motamed, Mesbah
    Abstract: The 2014 Agricultural Act introduced several risk management programs for commodities. Price Loss Coverage (PLC) and Agricultural Risk Coverage (ARC) provide price and revenue protection, respectively, to eligible producers of covered commodities. Also in addition to existing federally-backed crop insurance policies, the Supplemental Coverage Option (SCO), a new program, provides subsidized add-on insurance coverage to producers of rice, cotton, corn, soybeans, sorghum, wheat, and spring barley. Through simulations of prices and yields, we examine the relationship between the support payments generated by these new programs and the magnitude of the risk reduction they produce, both under their current parameters as well as alternatives. The simulations also reveal the distribution of risk reduction among counties across the United States.
    Keywords: 2014 Agricultural Act, Yield Distribution, Copulas, Commodity Support, Agricultural Risk Coverage, Supplemental Coverage Option, Price Loss Coverage, Agricultural and Food Policy, Risk and Uncertainty, G32, Q18, Q14,
    Date: 2015
  8. By: Samuel R\"onnqvist; Peter Sarlin
    Abstract: News is a pertinent source of information on financial risks and stress factors, which nevertheless is challenging to harness due to the sparse and unstructured nature of natural text. We propose an approach based on distributional semantics and deep learning with neural networks to model and link text to a scarce set of bank distress events. Through unsupervised training, we learn semantic vector representations of news articles as predictors of distress events. The predictive model that we learn can signal coinciding stress with an aggregated index at bank or European level, while crucially allowing for automatic extraction of text descriptions of the events, based on passages with high stress levels. The method offers insight that models based on other types of data cannot provide, while offering a general means for interpreting this type of semantic-predictive model. We model bank distress with data on 243 events and 6.6M news articles for 101 large European banks.
    Date: 2015–07
  9. By: Martin, Ian
    Abstract: This paper presents a new lower bound on the equity premium in terms of a volatility index, SVIX, that can be calculated from index option prices. This bound, which relies only on very weak assumptions, implies that the equity premium is extremely volatile, and that it rose above 20% at the height of the crisis in 2008. More aggressively, I argue that the lower bound---whose time-series average is about 5%---is approximately tight and that the high equity premia available at times of stress largely reflect high expected returns over the very short run. Under a stronger assumption, I show how to use option prices to measure the probability that the market goes up (or down) over some given horizon, and to compute the expected excess return on the market conditional on the market going up (or down).
    Keywords: equity premium; expected return; index options; predictive regression; return forecasting; SVIX; VIX
    JEL: G00 G1
    Date: 2015–07

General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.