Financial Engineering of Climate Uncertainty

The climatic condition influences and determines the various sectors in the economy. The emergence of weather derivatives in the financial market is the most palpable reflection of this reality. Derivative contracts climate use climate measures, analogous to the target asset for the pricing.

An institution that wishes to eliminate the risk of a given weather event, such as a period with little/excessive rain or temperature can purchase weather derivatives contracts, adjusting the unfavorable weather conditions to the estimated lost value and the return of the contracts.

Investment systems: a pragmatic text.

Investment / Trading systems are computer programs that send orders in the capital market. It's technology architecture is designed to support a decision system that sends signals to enter and exit on asset positions (buy and / or sell).

What ensures investment efficiency is the ability of these systems on making profit in the long run with the maximum possible security. The big question when we consider these systems is the ability to evaluate the performance and rank it as sufficient to ensure efficient management.

That is, put another way, how to separate systems that lack credibility on signals generating of which represent a significant result.

Managing Trading Systems: An Automation and Control Point of View.

Capital Market Investments are known as being risky process, requiring an adequate Risk Management (see Financial Risk Management , Volatility vs. Risk, HFT Risk ). Trading Systems are not out of this group, but trading systems have a particular element: It´s a systematic approach. Systematic doesn´t means profitable, however means tractable (see Financial Automation and Control: a new age).

When I say tractable, I’m referring to the mathematical model, where all the variables and parameters are disposal, ensure tracking.

On the tracking list could be included:

Why Quantitative Investment Outperforms?

Quantitative investments use computer systems to send buy and sell orders of financial assets. Are systems, often possessing artificial intelligence and complex econometric models in their algorithms. Its application is widespread in the U.S. market and on a continuous evolution.

Some Assets and Funds are available to perform this type of investment wisely, but there are few institutions that develop this approach with a significant degree of maturity.

The backtesting results of a carefully conducted and practical application of systems is that will provide the idea of their actual behavior, ie, if will provide positive returns or if the system will always lose in the long run. Its advantage is the ability to perform simulations and optimizations, and thereby enable the evolving investment process using a historical database. 

Risk Management on High-Frequency Trading (HFT)

The tools to measure risk, VaR and Stress for example, can also be used for higher frequencies but should be considered the fact that they have a non-linear behavior more pronounced on intraday returns, requiring a more refined analysis.

The big issue I see is to understand the strategy used to then understand what the most appropriate methodology to present a risk to the decision maker.

The HFT universe expands the universe of data exploration, with respect to risk some phenomena must be considered, for example, effects of liquidity throughout the day.

Financial Protection or Hedge

Hedge means exactly protection. Perform a hedge in a portfolio, means reducing the risk or volatility. It may be a foreign exchange exposure, for those who export, for example, which can be reduced through futures contracts, options, swaps. Currently a financial protection is actually quite available in the corporative environment and may be mediated by various financial institutions.

Performing the Hedge is to acquire contracts with opposite exposures that are on the portfolio's underlying asset which is unwanted oscillation. For instance, the portfolio can have a debt contract with a U.S. bank (debt exposure in U.S. dollars) and dollar-selling future. This process reduces the hedge foreign exchange exposure and inserts time mismatches, because each contract, debt and derivatives are tied to time and future dates. The hedge is dynamic, that means there needs to be managed over time. The contracts are locked in a time interval. Matching perfectly dates of the derivative positions in the portfolio you want to protect it is quite very difficult, which is called a perfect hedge.

Financial Risk Management

On Volatility versus Risk I commented on Risk and Volatility, without however mentioning practical activities related to the direct use of metrics for risk. 

 

Market risk, which is the possibility of financial loss due to the typical ups and downs of the market, should, as the intuition suggests, reflect the degree of oscillation that the asset provides.

The simplest metric, known and used for this purpose is the standard deviation. The standard deviation has inaccuracies in quantifying the risk, because strictly financial returns do not follow a normal distribution, which is the necessary premise to calculate the standard deviation. The metric of the standard deviation necessarily produce a bias, always deviated from the true value.

The biggest problem with the standard deviation, however, is not the premise that it performs when considering the normal data. The standard deviation by itself does not present the risk management of an efficient way, because it still lacks the statistical scenario was considered.

Value-at-Risk

The methodology Value-at-Risk (VaR) fills this gap. It was developed in order to produce management reports more objective and intuitive. His metric is well established in statistical terms and reflects exactly what one would expect from a market risk measure. The VaR results in a net asset value, or a possibility of financial loss. This possibility of financial loss is tied to a time interval and statistical significance. So we say: The portfolio has the possibility of losing $X on Y days (or hours) with a statistical significance of Z%. 

 

Financial processes, however, do not follow a behavior said homoscedastic or constant over time but heteroscedastic. The volatility can undergo dramatic changes as in times of crisis. These phenomena can cause a sharp drop in equity of a particular institution leading to bankruptcy in many cases. 

Stress 

An analysis complementary to VaR, called Stress analysis is used to simulate situations of panic and financial crises. These simulations analyze the tails of the distributions of returns and evaluate the portfolio in more extreme scenarios. It is possible to simulate historical crises, specific situations and scenarios to highly pessimistic and uncorrelated. 

It is necessary to understand the main feature of the market is the strong stochastic process which asset prices are submitted. Unexpected situations are possible and even expected, and it is impossible to predict turbulence and crisis situations with sufficient precision. The Financial Risk Management, however, has enough tools to evaluate and quantify the risks and fluctuations to which the portfolio is submitted.