Risk Minimizing Option Pricing in a Semi-Markov Modulated Market

We address risk minimizing option pricing in a semi-Markov modulated market where the floating interest rate depends on a finite state semi-Markov process. The growth rate and the volatility of the stock also depend on the semi-Markov process. Using the Föllmer–Schweizer decomposition we find the locally risk minimizing price for European options and the corresponding hedging strategy. We develop suitable numerical methods for computing option prices.

Asymptotic analysis of option pricing in a Markov modulated market

We address asymptotic analysis of option pricing in a regime switching market where the risk free interest rate, growth rate and the volatility of the stocks depend on a finite state Markov chain. We study two variations of the chain namely, when the chain is moving very fast compared to the underlying asset price and when it is moving very slow. Using quadratic hedging and asymptotic expansion, we derive corrections on the locally risk minimizing option price.

Predicting price-tag for customized goods

We discuss a dynamic pricing model which will aid automobile manufacturer in choosing the right price for customer segment. Though there is oligopoly market structure, the customers get "locked" into a particular technology/company which virtually makes the situation akin to a monopoly. There are associated network externalities and positive feedback. The key idea in monopoly pricing lies in extracting the customer surplus by exploiting the respective elasticities of demand. We present a Walrasian general equilibrium approach to determine the segment price. We compare the prices obtained from optimization model with that from Walrasian dynamics. The results are encouraging and can serve as a critical factor in Customer Relationship Management (CRM) and thereby effectively manage the lock-in.

Generators contribution towards loads and line flows - A case study

n many parts of the world, the goal of electricity supply industries is always the introduction of competition and a lowering of the average consumer price. Because of this it has become much more important to be able to determine which generators are supplying a particular load, how much use each generator is making of a transmission line and what is generator's contribution to the system losses. In this paper a case study on generator contributions towards loads and transmission flows are illustrated with an equivalent 11-bus system, a part of Indian Southern Grid, based on the concepts of circuit flow directions, for normal and network contingency conditions.

Learning dynamic prices in multiseller electronic retail markets with price sensitive customers, stochastic demands, and inventory replenishments

In this paper, we use reinforcement learning (RL) as a tool to study price dynamics in an electronic retail market consisting of two competing sellers, and price sensitive and lead time sensitive customers. Sellers, offering identical products, compete on price to satisfy stochastically arriving demands (customers), and follow standard inventory control and replenishment policies to manage their inventories. In such a generalized setting, RL techniques have not previously been applied. We consider two representative cases: 1) no information case, were none of the sellers has any information about customer queue levels, inventory levels, or prices at the competitors; and 2) partial information case, where every seller has information about the customer queue levels and inventory levels of the competitors. Sellers employ automated pricing agents, or pricebots, which use RL-based pricing algorithms to reset the prices at random intervals based on factors such as number of back orders, inventory levels, and replenishment lead times, with the objective of maximizing discounted cumulative profit. In the no information case, we show that a seller who uses Q-learning outperforms a seller who uses derivative following (DF). In the partial information case, we model the problem as a Markovian game and use actor-critic based RL to learn dynamic prices. We believe our approach to solving these problems is a new and promising way of setting dynamic prices in multiseller environments with stochastic demands, price sensitive customers, and inventory replenishments.

Patent Price Dynamics in the Context of Patent Age and Patent Latent Variables

The decision to patent a technology is a difficult one to make for the top management of any organization. The expected value that the patent might deliver in the market is an important factor that impacts this judgement. Earlier researchers have suggested that patent prices are better indicators of value of a patent and that auction prices are the best way of determining value. However, the lack of public data on pricing has prevented research on understanding the dynamics of patent pricing. Our paper uses singleton patent auction price data of Ocean Tomo LLC to study the prices of patents. We describe price characteristics of these patents. The price of these patents was correlated with their age, and a significant correlation was found. A price - age matrix was developed and we describe the price characteristics of patents using four quadrants of the matrix, namely young and old patents with low and high prices. We also found that patents owned by small firms get transacted more often and inventor owned patents attracted a better price than assignee owned patents.

Modelling electricity demand with representative load curves

Models for electricity planning require inclusion of demand. Depending on the type of planning, the demand is usually represented as an annual demand for electricity (GWh), a peak demand (MW) or in the form of annual load-duration curves. The demand for electricity varies with the seasons, economic activities, etc. Existing schemes do not capture the dynamics of demand variations that are important for planning. For this purpose, we introduce the concept of representative load curves (RLCs). Advantages of RLCs are demonstrated in a case study for the state of Karnataka in India. Multiple discriminant analysis is used to cluster the 365 daily load curves for 1993-94 into nine RLCs. Further analyses of these RLCs help to identify important factors, namely, seasonal, industrial, agricultural, and residential (water heating and air-cooling) demand variations besides rationing by the utility. (C) 1999 Elsevier Science Ltd. All rights reserved.

Potential of biomass energy for electricity generation in sub-Saharan Africa

The paper explores the biomass based power generation potential of Africa. Access to electricity in sub-Saharan Africa (SSA) is about 26% and falls to less than 1% in the rural areas. On the basis of the agricultural and forest produce of this region, the residues generated after processing are estimated for all the countries. The paper also addresses the use of gasification technology - an efficient thermo-chemical process for distributed power generation - either to replace fossil fuel in an existing diesel engine based power generation system or to generate electricity using a gas engine. This approach enables the implementation of electrification programs in the rural sector and gives access to grid quality power. This study estimates power generation potential at about 5000 MW and 10,000 MW by using 30% of residues generated during agro processing and 10% of forest residues from the wood processing industry, respectively. A power generation potential of 15000 MW could generate 100 terawatt-hours (TWh), about 15% of current generation in SSA. The paper also summarizes some of the experience in using the biomass gasification technology for power generation in Africa and India. The paper also highlights the techno economics and key barriers to promotion of biomass energy in sub-Saharan Africa. (C) 2011 International Energy Initiative. Published by Elsevier Inc. All rights reserved.

Decision support system for regional electricity planning

Electricity appears to be the energy carrier of choice for modern economics since growth in electricity has outpaced growth in the demand for fuels. A decision maker (DM) for accurate and efficient decisions in electricity distribution requires the sector wise and location wise electricity consumption information to predict the requirement of electricity. In this regard, an interactive computer-based Decision Support System (DSS) has been developed to compile, analyse and present the data at disaggregated levels for regional energy planning. This helps in providing the precise information needed to make timely decisions related to transmission and distribution planning leading to increased efficiency and productivity. This paper discusses the design and implementation of a DSS, which facilitates to analyse the consumption of electricity at various hierarchical levels (division, taluk, sub division, feeder) for selected periods. This DSS is validated with the data of transmission and distribution systems of Kolar district in Karnataka State, India.

Dynamics between patent latent variables and patent price

This paper focuses on studying the relationship between patent latent variables and patent price. From the existing literature, seven patent latent variables, namely age, generality, originality, foreign filings, technology field, forward citations, and backward citations were identified as having an influence on patent value. We used Ocean Tomo's patent auction price data in this study. We transformed the price and the predictor variables (excluding the dummy variables) to its logarithmic value. The OLS estimates revealed that forward citations and foreign filings were positively correlated to price. Both the variables jointly explained 14.79% of the variance in patent pricing. We did not find sufficient evidence to come up with any definite conclusions on the relationship between price and the variables such as age, technology field, generality, backward citations and originality. The Heckman two-stage sample selection model was used to test for selection bias. (C) 2011 Elsevier Ltd. All rights reserved.

Pricing Defaultable Bonds in a Markov Modulated Market

We address the problem of pricing defaultable bonds in a Markov modulated market. Using Merton's structural approach we show that various types of defaultable bonds are combination of European type contingent claims. Thus pricing a defaultable bond is tantamount to pricing a contingent claim in a Markov modulated market. Since the market is incomplete, we use the method of quadratic hedging and minimal martingale measure to derive locally risk minimizing derivative prices, hedging strategies and the corresponding residual risks. The price of defaultable bonds are obtained as solutions to a system of PDEs with weak coupling subject to appropriate terminal and boundary conditions. We solve the system of PDEs numerically and carry out a numerical investigation for the defaultable bond prices. We compare their credit spreads with some of the existing models. We observe higher spreads in the Markov modulated market. We show how business cycles can be easily incorporated in the proposed framework. We demonstrate the impact on spreads of the inclusion of rare states that attempt to capture a tight liquidity situation. These states are characterized by low risk-free interest rate, high payout rate and high volatility.