In this paper, we show that large inflows into commodity investments, a recent phenomenon known as financialization, has changed the behavior and dependence structure between commodities and the general stock market. The common perception is that the increase in comovements is the result of distressed investors selling both assets during the 2007-2009 financial crisis. We show that financial distress alone cannot explain the size and persistence of comovements. Instead, we argue that commodities have become an investment style for institutional investors. Given that institutional investors continue to target funds into commodities, we predict spillovers between commodities and the stock market to remain high in the future.
This paper investigates the cross hedging performance of several oil forwards contracts using WTI, Brent, gasoil and heating oil to manage jet-fuel spot price exposure. We apply three econometric techniques that have been widely tested and applied in the cross hedging literature on foreign exchange and stock index futures markets. Using quotes from the financial industry on forward contracts, we can show that the optimal cross hedging instrument depends on the maturity of the instrument's forwards contract. The results highlight that the standard approach in the literature to use crude oil as a cross hedge is not optimal. By contrast, for short hedging horizons our results indicate that gasoil forwards contracts represent the highest cross hedging efficiency for jet-fuel spot price exposure, while for maturities of more than three months, the predominance of gasoil diminishes in comparison to WTI and Brent.
We describe a model for the optimization of the issuances of Public Debt securities developed together with the Italian Ministry of Economy and Finance. The goal is to determine the composition of the portfolio issued every month which minimizes a specific "cost function". Mathematically speaking, this is a stochastic optimal control problem with strong constraints imposed by national regulations and the Maastricht treaty. The stochastic component of the problem is represented by the evolution of interest rates. At this time the optimizer employs classic Linear Programming techniques. However more sophisticated techniques based on Model Predictive Control strategies are under development.
Using readily available data on daily high and lows prices, a simple estimation method of the efficient price volatility and bid-ask spread is developed. The model relies on general assumptions and it provides a closed-form solution for an unbiased estimator of efficient volatility.
Moreover, it provides a better treatment of the volume effect caused by trading discontinuity and non-trading time. Using a comprehensive data set of high-frequency FX rates, it is shown that the liquidity estimator proposed is highly correlated with the actual bid-ask spread and other measures of market liquidity
This paper examines the predictive power of five linear hedonic pricing models for the residential market with varying levels of complexity in their spatial and temporal structures. Unlike similar studies, we extend the out-of-sample forecast evaluation to one-day-ahead predictions with a rolling estimation window, which is a reasonable setting for many practical applications. We show that the in-sample fit and cross-validation prediction accuracy improve significantly when we account for spatial heterogeneity. In particular, for one-day-ahead forecasts, the spatiotemporal autoregressive (STAR) model demonstrates its superiority over model specifications with alternating spatial and temporal heterogeneity and dependence structures. In addition, sub-market fixed effects, constructed on the basis of statistical TREE methods, improve the results of predefined local rental markets further.
This paper proposes an empirical framework that is based on pre-trade transparency to test for information-based return co-movements among international commercial real estate markets. We introduce a benchmark portfolio that includes property markets with a higher pre-trade transparency to assess expected returns in less transparent markets. Investors can earn abnormal returns in opaque markets relative to the reference portfolio. Revealed post-trade information in the benchmark portfolio results in spillover effects to less transparent markets. Conditional on the reference portfolio, we analyze learning externalities to less transparent markets. Specially, we identify cultural familiarity as a source of learning-based return co-movements.
Increasing interconnectivity between electricity wholesale markets requires an
efficient allocation scheme in order to provide access to scarce cross-border
transmission capacities. In both the US and Europe, existing schemes have
primarily induced economically inefficient interconnector use given that flows have
to be nominated prior to spot market clearing. By contrast, the market coupling
mechanisms recently rolled out in parts of Europe avoid these inefficiencies by
implicitly allocating cross-border transmission capacity upon spot market clearance. In this paper, we show that these institutional aspects of market design clearly manifest in the empirical dynamics of both electricity spot and derivatives prices, and hence, do have important implications for pricing and hedging in these markets. Since traditional reduced-form models fail to reproduce such effects of market microstructure, we employ a fundamental multi-market model for electricity pricing in order to analyze how the key stylized facts of electricity prices are impacted by the different allocation schemes.
