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In the last decade, the index-based weather products (also called weather derivatives) have been gaining attention in the climate resilience discussion. Weather derivatives are designed to help companies hedging against climate variability. These products, that can be market-traded or over-the-counter, compensate individuals based on a pre-defined weather index. Thus, pay-offs of a weather derivative depend on a weather index and not, as with traditional types of insurance, on the actual amount of money lost due to adverse weather. One of the major drawbacks that may prevent weather derivatives to catch on is the impact of the Geographical Basis Risk (GBR), that is the deviation of weather conditions at different locations. In fact, when the reference weather station is not located in the immediate vicinity of the site of interest the hedging effectiveness may be reduced. In this paper, we contribute to the existing literature on GBR by proposing an optimization method that may help in offering a tailored solution, while at the same time keeping a standardized instrument as a reference. Using a historical record of Italian temperatures, strikes for temperatures are the choice variables of a penalty function containing pay-offs of a reference station and all other stations. Further, altitude and latitude of meteorological stations are shown to be relevant predictors to explain GBR. This can be an interesting starting point for the design of weather derivatives, since, from a unique station where the “reference” derivative is priced, all the other stations may be easily settled.

In the last decade, the index-based weather products (also called weather derivatives) have been gaining attention in the climate resilience discussion. Weather derivatives are designed to help companies hedging against climate variability. These products, that can be market-traded or over-the-counter, compensate individuals based on a pre-defined weather index. Thus, pay-offs of a weather derivative depend on a weather index and not, as with traditional types of insurance, on the actual amount of money lost due to adverse weather. One of the major drawbacks that may prevent weather derivatives to catch on is the impact of the Geographical Basis Risk (GBR), that is the deviation of weather conditions at different locations. In fact, when the reference weather station is not located in the immediate vicinity of the site of interest the hedging effectiveness may be reduced. In this paper, we contribute to the existing literature on GBR by proposing an optimization method that may help in offering a tailored solution, while at the same time keeping a standardized instrument as a reference. Using a historical record of Italian temperatures, strikes for temperatures are the choice variables of a penalty function containing pay-offs of a reference station and all other stations. Further, altitude and latitude of meteorological stations are shown to be relevant predictors to explain GBR. This can be an interesting starting point for the design of weather derivatives, since, from a unique station where the “reference” derivative is priced, all the other stations may be easily settled.

We propose a model with economic and environmental domains that interact with each other. The economic sphere is described by a Solow growth model, in which productivity is not exogenous but negatively affected by the stock of pollution that stems from the production process. A regulator can charge a tax on production, and the resources collected from taxation are used to reduce pollution. The resulting model consists of a two dimensional discrete dynamical system, and we study the role of taxation from both a static and a dynamical point of view. The focus is on the determination of the conditions under which taxation has a positive effect on the environment and leads to economic growth. Moreover, we show that a suitable environmental policy can allow recovering both local and global stability of the steady states. On the contrary, we show that, if the policy is not adequate, the system can exhibit endogenous oscillating and chaotic behavior and multistability phenomena.

The price of common stocks, defined as the sum of all future discounted dividends, is at the heart of both the dividend discount models (DDM) and the stochastic DDM (SDDM). Gordon and Shapiro (Manag Sci 3:102–110 1956 ) assume a deterministic and constant dividends’ growth rate, whereas Hurley and Johnson (Financ Anal J 4:50–54 1994 , J Portf Manag 25(1)27–31 1998 ) and Yao (J Portf Manag 23(4)99–103 1997 ) introduce randomness by letting the growth rate be a finite-state random variable and random dividends behave in a Markovian fashion. In this second case expected stock price is determined, but what if higher-order moments are needed? In order to address a number of financial topics, the present contribution presents an explicit formula for the covariance between (possibly) correlated stock prices.

We propose an evolutionary model to study the transition toward green technology under the influence of innovation. Clean and dirty technologies are selected according to their profitability under an environmental tax, which depends on the overall pollution level. Pollution itself evolves dynamically: it results from the emissions of the two types of producers, naturally decays, and is reduced through the implementation of the current abatement technology. The regulator collects tax revenues and allocates them between the implementation of the existing abatement technology and its innovation, which increases the stock of knowledge and thereby enhances abatement effectiveness. From a static perspective, we show the existence of steady states, both with homogeneous populations of clean or dirty producers and with heterogeneous populations where both technologies coexist. We discuss the mechanisms through which these steady states emerge and how they may evolve into one another. From a dynamical perspective, we characterize the resulting scenarios, showing how innovation can foster a green transition if coupled with a suitable level of taxation. At the same time, we investigate how improper environmental policies may also produce unintended outcomes, such as environmental deterioration, reversion to dirty technology, or economic unsustainability.

We analyze and quantify, in a financial market with parameter uncertainty and for a Constant Relative Risk Aversion investor, the utility effects of two different boundedly rational (i.e., sub-optimal) investment strategies (namely, myopic and unconditional strategies) and compare them between each other and with the utility effect of full information. We show that effects are mainly caused by full information and predictability, being the effect of learning marginal. We also investigate the saver's decision of whether to manage her/his portfolio personally (DIY investor) or hire, against the payment of a management fee, a professional investor and find that delegation is mainly motivated by the belief that professional advisors are, depending on investment horizon and risk aversion, either better informed (\"insiders\") or more capable of gathering and processing information rather than their ability of learning from financial data. In particular, for very short investment horizons, delegation is primarily, if not exclusively, motivated by the beliefs that professional investors are better informed.

This article extends, in a stochastic environment, the Yagil (1987) model which establishes, in a deterministic dividend discount model, a range for the exchange ratio in a stock-for-stock merger agreement. Here, we generalize Yagil's work letting both pre- and post-merger dividends grow randomly over time. If Yagil focuses only on changes in stock prices before and after the merger, our stochastic environment allows to keep in account both shares' expected values and variance, letting us to identify a more complex bargaining region whose shape depends on mean and standard deviation of the dividends' growth rate.

We maximize the expected utility from terminal wealth for an HARA investor when the market price of risk is an unobservable random variable. We compute the optimal portfolio explicitly and explore the effects of learning by comparing it with the corresponding myopic policy. In particular, we show that, for a market price of risk constant in sign, the ratio between the portfolio under partial observation and its myopic counterpart increases with respect to risk tolerance. As a consequence, the absolute value of the partial observation case is larger (smaller) than the myopic one if the investor is more (less) risk tolerant than the logarithmic investor. Moreover, our explicit computations enable to study in details the so called hedging demand induced by learning about market price of risk.

We maximize the expected utility from terminal wealth for an HARA investor when the market price of risk is an unobservable random variable. We compute the optimal portfolio explicitly and explore the effects of learning by comparing it with the corresponding myopic policy. In particular, we show that, for a market price of risk constant in sign, the ratio between the portfolio under partial observation and its myopic counterpart increases with respect to risk tolerance. As a consequence, the absolute value of the partial observation case is larger (smaller) than the myopic one if the investor is more (less) risk tolerant than the logarithmic investor. Moreover, our explicit computations enable to study in details the so called hedging demand induced by learning about market price of risk.

Dividend discount models have been developed in a deterministic setting. Some authors (Hurley and Johnson, 1994 and 1998; Yao, 1997) have introduced randomness in terms of stochastic growth rates, delivering closed-form expressions for the expected value of stock prices. This paper extends such previous results by determining a formula for the covariance between random stock prices when the dividends' rates of growth are correlated. The formula is eventually applied to real market data.