This book combines academic research and practical expertise on alternative assets and trading strategies in a unique way. The asset classes that are discussed include: credit risk, cross-asset derivatives, energy, private equity, freight agreements, alternative real assets (ARA), and socially responsible investments (SRI). The coverage on trading and investment strategies are directed at portfolio insurance, especially constant proportion portfolio insurance (CPPI) and constant proportion debt obligation (CPDO) strategies, robust portfolio optimization, and hedging strategies for exotic options.
Sample Chapter(s)
Chapter 1: Socially Responsible Investments (276 KB)
https://doi.org/10.1142/9789814280112_fmatter
The following sections are included:
https://doi.org/10.1142/9789814280112_0001
Within the last two decades, the market of socially responsible investing (SRI) has seen unprecedented growth and has become more and more important, not only because of the current financial crisis. This chapter gives a survey of the asset class SRI in general, i.e., market development and investment possibilities. Moreover, the question “How sustainable is sustainability?” is addressed by analyzing SAM Group sustainability rankings of the years 2001–2007. Furthermore, the ability of SRI to contribute to diversification within a portfolio is scrutinized. The analysis is based on simulated returns generated by an autoregressive Markov-Switching model and accounts for different levels of investors' risk aversion. Optimal portfolios consisting of stocks, bonds, and the respective SRI index show that risk–averse investors mix SRI to an established portfolio consisting of bonds and stocks to reduce the risk and increase the performance. Additionally, the asset class SRI is found to be a substitute for the asset class stocks.
https://doi.org/10.1142/9789814280112_0002
This chapter first provides a comprehensive overview of private equity by categorizing the private equity investments into financing stages, divestment strategies, and types of financing. Different ways of investing in the asset class “private equity” are characterized, ranging from direct investments, which are hard to access, to listed private equity (LPE) investments, which provide a liquid means for investors to consider private equity in their portfolios. A Markov–Switching model is presented, which is able to capture the characteristics of the asset class LPE. By applying several risk measures and optimization frameworks, the question of the optimal fraction for an LPE investment in an investor's portfolio is scrutinized. Depending on the risk aversion of the investor, the optimal fraction of an LPE investment in this study ranges between 0% for a very risk averse investor, 7.5–11.8% for a moderately risky investor, and 16.9–27.9% for an investor willing to take higher risks.
https://doi.org/10.1142/9789814280112_0003
In this chapter, we are introducing the asset class “Alternative Real Assets”. This asset class provides access to “real” investment opportunities, real meaning both partially inflation protected and solid, via limited partnership fund structures. This (sub-)asset class comprises fund vehicles that invest, for example, in infrastructure, shipping, or renewable energy projects. For some of these alternative real assets, financial incentives are provided, such as preferential feed-in tariffs for solar–generated electricity.
We discuss alternative real assets, including a comparison to other asset classes and a description of distinctive features, with a focus on photovoltaic investments. The bottom-up modeling approach of individual photovoltaic projects and the approach to incorporate these investments in portfolio analytics are presented. Based on a comprehensive framework, we are finally able to evaluate the benefits of photovoltaic investments in a multi-asset portfolio.
Investing in photovoltaic facilities is environmentally supportive. We are able, as well, to show that under reasonable assumptions, financing solar plants is an attractive asset class, both stand-alone and in a portfolio context.
https://doi.org/10.1142/9789814280112_0004
Despite the fact that the freight market is one of the oldest markets, it found only limited attention in financial (mathematics) studies. One possible reason may be that this market is very intransparent and only active market participants such as shipowners, dockyards, and commodity traders were fully informed on market activities. With the launch of freight market derivatives, however, many other financial institutions started to actively trade on the freight market, for instance by taking positions on future global trade activities via Forward Freight Agreements and Freight Futures. This article provides an introduction to the freight market and its most commonly used financial assets.We investigate the relationship between the freight market, freight derivatives, and macro economic variables. Further, we show how these relationships can be used to better explain and predict the future price movements of freight derivatives. To achieve this, we propose to use classical statistical methods, such as Vector Auto Regression and Vector Error Correction Models, to incorporate the information from spot markets and related explanatory variables into the prediction process.
https://doi.org/10.1142/9789814280112_0005
Power forward contracts deliver electricity over a specified period, and can be viewed as a portfolio of forwards with maturity at each time instant in the delivery period. We investigate the implied power forward dynamics from a geometric Brownian motion specification of the forward price, which turns out to have a very complicated structure. Lognormal approximations are argued for, and we demonstrate that they work excellently in many situations. In particular, we focus on the approximation suggested by Bjerksund et al. [8], where the volatility of the power forward is simply the average of the fixed maturity forward volatility. Although giving a superior model to the moment matched dynamics, it fails to estimate the tails of the power forward distribution in some cases with extreme volatility and mean-reversion. We provide analytical bounds in terms of geometric Brownian motions for the power forward dynamics, and also compare the covariance structure with those implied by a geometric Brownian motion.
https://doi.org/10.1142/9789814280112_0006
Certificates have become very popular in Germany, Austria, and Switzerland in the last few years. From a technical and legal point of view they are bonds. Thus, their value actually also depends on the rating and creditworthiness of the issuing company. This aspect is in general neglected in the pricing of these products. In the following, we present a model which overcomes this lack and incorporates the default risk of the issuing company in the pricing.We derive closed-form expressions for index, basket, and bonus certificates under issuer risk in a Black–Scholes model framework. The results are analyzed for different scenarios and compared with valuations in a model which neglects issuer risk.
https://doi.org/10.1142/9789814280112_0007
This chapter presents a consistent, scenario-based asset allocation framework for analyzing traditional financial instruments and credit instruments in a portfolio context. Our framework accounts for the distinct return characteristics of credit instruments by incorporating potential defaults into the total return calculation. We generate correlated default times with a Normal Inverse Gaussian one-factor copula. To determine optimal portfolios, we use a mean-variance and a conditional value at risk optimization. Performing a case study for the U.S. market, we find that the mean-variance optimization overestimates the benefits of low-rated credit instruments. Though, optimal portfolios always contain a considerable proportion of credit instruments.
https://doi.org/10.1142/9789814280112_0008
The dependence of extreme financial events among different asset classes is taken under consideration on a portfolio level. For this, a new product group, called cross asset portfolio derivatives, is introduced and explained in the light of related existing products and pricing methods.A classification is presented and features of these products are described. Finally, two modeling and pricing frameworks using multivariate stochastic processes and (hierarchical) copulas, respectively, are suggested.
https://doi.org/10.1142/9789814280112_0009
Dynamic portfolio strategies are an interesting alternative to classical option-based investment and protection strategies. One of the most prominent techniques is Constant Proportion Portfolio Insurance (CPPI). In this chapter, we provide a review of various techniques and formulate a general framework for investment and protection strategies. The common feature of this strategy is that it empowers the investor to replicate various option like pay-off profiles without the usage of options. These strategies may replicate a simple floor type or advanced path-dependent look-back options that implement all-time high strategies with a given participation rate. We illustrate the different strategies that employ features like various types of lock-in, trailing, leverage, and risky portfolio strategies with historical simulations. We include features that allow the simulation under realistic market conditions taking into account transaction costs and the avoidance of excessive rebalancing through transaction filters. We discuss the use of exchange traded funds (ETF) to invest in broadly diversified multi-asset portfolios. The goal of this chapter was to illustrate different protection strategies and to show how a practical implementation of these strategies could look like. This chapter can serve as a guideline for simple spread-sheet models.
https://doi.org/10.1142/9789814280112_0010
Portfolio insurance strategies are designed to achieve a minimum level of wealth while at the same time participating in upward moving markets. The most prominent examples of dynamic versions are option-based strategies with synthetic put and constant proportion portfolio insurance strategies. It is well known that, in a Black/Scholes type model setup, these strategies can be achieved as optimal solution by forcing an exogenously given guarantee into the expected utility maximization problem of an investor with CRRA utility function. The CPPI approach is attained by the introduction of a subsistence level, the OBPI approach stems from an additional constraint on the terminal portfolio value. We bring these results together in order to explain when and why OBPI strategies are better than CPPI strategies and vice versa. We determine the utility losses, which are caused by introducing a terminal guarantee into the unconstrained maximization approach. In addition, we focus on utility losses, which are due to market frictions such as discrete-time trading, transaction costs, and borrowing constraints.
https://doi.org/10.1142/9789814280112_0011
Constant proportion portfolio insurance (CPPI) and constant proportion debt obligations (CPDO) strategies have recently created derivative instruments, which try to protect a portfolio against failure events and have only been adopted in the credit market for the last couple of years. Since their introduction, CPPI strategies have been popular because they provide protection while at the same time they offer high yields. CPDOs were only introduced into the market in 2006 and can be considered as a variation of the CPPI with as main difference the fact that CPDOs do not provide principal protection. Both CPPI and CPDO strategies take investment positions in a risk-free bond and a risky portfolio (often one or more credit default swaps). At each step, the portfolio is rebalanced and the level of risk taken will depend on the distance between the current value of the portfolio and the necessary amount needed to fulfill all the future obligations.
We first analyze in detail the dynamics of both investment strategies and afterwards test the safetyness of both products under a multivariate Lévy setting. More precise we first propose a quick way to calibrate a multivariate Variance Gamma (VG) process on correlated spreads, which can then be used to quantify the gap risk for CPPIs and CPDOs.
https://doi.org/10.1142/9789814280112_0012
In recent years, new ideas for the robustification of the traditional Markowitz frontier have appeared in the literature. Based on one of these ideas — the so-called robust counterparts — we introduce the concept of the robustified efficient frontier.As mean– variance efficient portfolios are frequently used as risky assets for CPPI strategies, we investigate the behavior of such strategies under estimation risk. Based on a toy example, we explain the main idea how the concept of a robustified frontier can be used to improve the performance of CPPI strategies. For this purpose, we compare the theoretical performance of CPPI strategies based on the original and the robust efficient frontier.
https://doi.org/10.1142/9789814280112_0013
In this chapter, we propose a robust asset allocation methodology, when there is some ambiguity concerning the distribution of asset returns. The investor considers several prior models for the assets distribution and displays an ambiguity aversion against them. We have developed a two-step ambiguity robust methodology that offers the advantage to be more tractable and easier to implement than the various approaches proposed in the literature. This methodology decomposes the ambiguity aversion into a model-specific ambiguity aversion as well as relative ambiguity aversion for each model across the set of different priors. The optimal solutions inferred by each prior are transformed through a generic absolute ambiguity function ψ. Then, the transformed solutions are mixed together through a measure π that reflects the relative ambiguity aversion of the investor for the different priors considered. This methodology is then illustrated through the study of an empirical example on European data.
https://doi.org/10.1142/9789814280112_0014
In this chapter, we give a survey of results for semi-static hedging strategies for exotic options under different model assumptions and also in a model-independent framework. Semi-static hedging strategies consist of rebalancing the underlying portfolio only at certain pre-specified timepoints during the lifetime of the hedged derivative, as opposed to classical dynamic hedging, where adjustments have to be made continuously in time. In many market situations (and in particular in times of limited liquidity), this alternative approach to the hedging problem is quite useful and has become an increasingly popular research topic over the last years.We summarize the results on barrier options as well as strongly path-dependent options such as Asian or lookback options. Finally, it is shown how perfect semi-static hedging strategies for discretely observed options can be developed in quite general Markov-type models.
https://doi.org/10.1142/9789814280112_0015
We consider variance-optimal hedging when trading is restricted to a finite time set. Using Laplace transform methods, we derive semi-explicit formulas for the variance-optimal initial capital and hedging strategy in affine stochastic volatility models. For the corresponding minimal expected-squared hedging error, we propose a closed-form approximation as well as a simulation approach. The results are illustrated by computing the relevant quantities in a time-changed Lévy model.
https://doi.org/10.1142/9789814280112_bmatter
The following sections are included:
Rüdiger Kiesel heads the chair for “Energy Trading and Financial Services” at the University Duisburg-Essen. Previously he has been Director of the Institute for Mathematical Finance at the University of Ulm. He also held positions as Lecturer and Reader for actuarial science and financial mathematics at Birkbeck College, University of London and the London School of Economics, where he is still a Visiting Professor. He is also a Visiting Professor at the Center of Applied Mathematics, Oslo University. His main research areas are currently design and analysis of credit risk models, valuation and hedging of derivatives (interest-rate, credit- and energy-related), methods of risk transfer and structuring of risk (securitization), risk management for power utility companies and the stochastic modelling of financial markets using Lévy-type processes. He is co-author of the Springer Finance monograph Risk-Neutral Valuation (now in its second edition). Professor Kiesel also consults financial institutions and regulators on (credit- and energy-) risk management, derivative pricing models and asset allocation.
Matthias Scherer is Senior Researcher at the HVB-Institute for Mathematical Finance at the Technische Universität München. With a doctorate from the University of Ulm, he coordinates the elite-graduate programme, “Finance and Information Management” and teaches various courses in mathematical finance. His research focus lies on credit-risk modelling, multivariate models, and dependence concepts.
Rudi Zagst is Professor of Mathematical Finance, Director of the Center of Mathematics and Head of the Institute for Mathematical Finance at TUM (Technische Universität München). He is also President of risklab germany, a German-based consulting company offering advanced asset management solutions. He is a consultant and a professional trainer to a number of leading institutions. His current research interests are in financial engineering, credit risk modelling and quantitative asset management. Prior to his current positions he headed the Product Development group in the Institutional Investment Management at Hypovereinsbank, the Consulting group at Allfonds International Asset Management GmbH and was Managing Director of the RiskLab GmbH — Private Research Institute for Financial Studies. He was awarded “Professor of the Year 2007” in Germany by the magazine Unicum Beruf for linking practice and education in an outstanding way. He is author of the book Interest Rate Management with Springer Finance and co-author of the books Zertifikate spielend beherrschen and Zu nah an der Sonne — Die gröβten Pleiten der Finanzgeschichte with Finanzbuch Verlag. He holds a PhD in Natural Sciences from the University of Ulm, Germany.
Sample Chapter(s)
Chapter 1: Socially Responsible Investments (276 KB)