THE DESIGN AND PRICING OF FIXED AND MOVING WINDOW CONTRACTS: AN APPLICATION OF ASIAN-BASKET OPTION PRICING METHODS TO THE HOG FINISHING SECTOR

Asian-Basket type moving window contracts are an increasingly used risk management tool in US hog sector. The moving window contract is decomposed into a portfolio of a long Asian-Basket put and a short Asian-Basket call option. A projected breakeven price is used to determine the floor price, and then Monte Carlo simulation methods are used to price both a moving and a fixed window contract. These methods provide unbiased pricing of fixed and moving window hog finishing contracts of one-year duration.Livestock Production/Industries,

Forecasting volatility in commodity markets

Commodity prices have historically been among the most volatile of international prices. Measured volatility (the standard deviation of price changes) has not been below 15 percent and at times has been more than 50 percent. Often the volatility of commodity prices has exceeded that of exchange rates and interest rates. The large price variations are caused by disturbances in demand and supply. Stockholding leads to some price smoothing, but when stocks are low, prices can jump sharply. As a result, commodity price series are not stationary and in some periods they jump abruptly to high levels or fall precipitously to low levels relative to their long-run average. Thus it is difficult to determine long-term price trends and the underlying distribution of prices. The volatility of commodity prices makes price forecasting difficult. Indeed, realized prices often deviate greatly from forecasted prices, which has led to the practice of giving forecasts probability ranges. But assigning probability ranges requires forecasting future price volatility, which, given uncertainties about true price distribution, is difficult. One potentially useful source of information for forecasting volatility is the volatility forecasts imbedded in the prices of options written on commodities traded in exchanges. Options give the holder the right to buy (call) or sell (put) a certain commodity at a certain date at a fixed (exercise) price. Options prices depend on several variables, one of which is the expected volatility up to the maturity date. Given a specific theoretical model, the market prices of options can be used to derive the market's expectations about price volatility and the price distribution. The authors systematically analyze different methods'abilities to forecast commodity price volatility (for several commodities). They collected the daily prices of commodity options and other variables for seven commodities (cocoa, corn, cotton, gold, silver, sugar, and wheat). They extracted the volatility forecasts implicit in options prices using several techniques. They compared several volatility forecasting methods, divided into three categories: (1) forecasts using only expectations derived form options prices; (2) forecasts using only time-series modeling; (3) forecasts that combine market expectations and time-series modeling (a new method devised for this purpose). They find that the volatility forecasts produced by method 3 outperform the first two as well as the naive forecast based on historical volatility. This result holds both in and out of sample for almost all commodities considered.Markets and Market Access,Access to Markets,Economic Theory&Research,Economic Forecasting,Science Education

HEDGE FUND REPLICATION STRATEGIES: THE GLOBAL MACRO CASE

This paper performs and analyzes hedge fund replication strategies using liquid exchange-traded instruments to build linear multi-factor models (“clones”) that mimic Hedge Funds returns. First, we follow Hasanhodzic and Lo (2006) six-factor model, using Barclay Hedge Indexes monthly returns for the period of January 1997 to August 2017 on seventeen hedge fund strategies. Next, we introduce variations and new propositions to the model in order to obtain closer risk-return characteristics, focusing on one particular hedge fund strategy: Global Macro. Finally, we use these results to base our conclusion and propose applications for this method.Our findings promote the use of shorter month period in rolling-windows approach and monthly rebalancing strategy for a faster reaction and adaptation to market conditions. Also, it suggests the addition of a strategic-specific factor to obtain better expected-return replications. These findings are particularly relevant to institutional investors that need diversification and could benefit from this asset class exposure, but many times are restricted from investing in hedge funds due to their high fee structure, illiquidity, and opaque tactics

Statistical Significance of the Netflix Challenge

Inspired by the legacy of the Netflix contest, we provide an overview of what has been learned---from our own efforts, and those of others---concerning the problems of collaborative filtering and recommender systems. The data set consists of about 100 million movie ratings (from 1 to 5 stars) involving some 480 thousand users and some 18 thousand movies; the associated ratings matrix is about 99% sparse. The goal is to predict ratings that users will give to movies; systems which can do this accurately have significant commercial applications, particularly on the world wide web. We discuss, in some detail, approaches to "baseline" modeling, singular value decomposition (SVD), as well as kNN (nearest neighbor) and neural network models; temporal effects, cross-validation issues, ensemble methods and other considerations are discussed as well. We compare existing models in a search for new models, and also discuss the mission-critical issues of penalization and parameter shrinkage which arise when the dimensions of a parameter space reaches into the millions. Although much work on such problems has been carried out by the computer science and machine learning communities, our goal here is to address a statistical audience, and to provide a primarily statistical treatment of the lessons that have been learned from this remarkable set of data.Comment: Published in at http://dx.doi.org/10.1214/11-STS368 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Dynamic network participation of functional connectivity hubs assessed by resting-state fMRI

Network studies of large-scale brain connectivity have demonstrated that highly connected areas, or "hubs," are a key feature of human functional and structural brain organization. We use resting-state functional MRI data and connectivity clustering to identify multi-network hubs and show that while hubs can belong to multiple networks their degree of integration into these different networks varies dynamically over time. The extent of the network variation was related to the connectedness of the hub. In addition, we found that these network dynamics were inversely related to positive self-generated thoughts reported by individuals and were further decreased with older age. Moreover, the left caudate varied its degree of participation between a default mode subnetwork and a limbic network. This variation was predictive of individual differences in the reports of past-related thoughts. These results support an association between ongoing thought processes and network dynamics and offer a new approach to investigate the brain dynamics underlying mental experience