This chapter develops on risk processes which, perhaps, are most suitable for computer visualization of all insurance objects. At the same time, risk processes are basic instruments for any non-life actuary – they are vital for calculating the amount of loss that an insurance company may incur.

In the last decade Markov-regime switching (MRS) models have been extensively used for modeling the unique behavior of spot prices in wholesale electricity markets. This popularity stems from the models’ relative parsimony and the ability to capture the stylized facts, in particular the mean...

This paper is intended as a guide to building insurance risk (loss) models. A typical model for insurance risk, the so-called collective risk model, treats the aggregate loss as having a compound distribution with two main components: one characterizing the arrival of claims and another...

Pickands constants appear in the asymptotic formulas for extremes of Gaussian processes. The explicit formula of Pickands constants does not exist. Moreover, in the literature there is no numerical approximation. In this paper we compute numerically Pickands constants by the use of change of...

The main purpose of the paper is to present, how derivatives valuing methodology, known from financial and commodities markets, can be applied to the electricity market. We compare an application of three recent models. We start with the convenience yield approach, then we analyse the...

We examine convenience yields in the EU-wide CO2 emissions trading scheme (EU-ETS) during the first Kyoto commitment period (2008-2012). We find that the market has changed from initial backwardation to contango with significantly negative convenience yields in futures contracts. We further...

In this paper we investigate whether considering the fine structure of half-hourly electricity prices, the market closing prices of fundamentals (natural gas, coal and CO2) and the system-wide demand can lead to significantly more accurate short- and mid-term forecasts of APX UK baseload prices....

Majority of the load forecasting literature has been on point forecasting, which provides the expected value for each step throughout the forecast horizon. In the smart grid era, the electricity demand is more active and less predictable than ever before. As a result, probabilistic load...

We show that incorporating the intra-day and inter-zone relationships of electricity prices in the Pennsylvania--New Jersey--Maryland (PJM) Interconnection improves the accuracy of short- and medium-term forecasts of average daily prices for a major PJM market hub -- the Dominion Hub in...

Probabilistic load forecasting is becoming crucial in today's power systems planning and operations. We propose a novel methodology to compute interval forecasts of electricity demand, which applies a Quantile Regression Averaging (QRA) technique to a set of independent expert point forecasts....