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on Risk Management |
| By: | Fernandez, Pablo (IESE Business School) |
| Abstract: | The market risk premium is one of the most important but elusive parameters in finance. It is also called equity premium, market premium and risk premium. The term 'market risk premium' is difficult to understand because it is used to designate three different concepts: 1) Required market risk premium, which is the incremental return of a diversified portfolio (the market) over the risk-free rate (return of treasury bonds) required by an investor. It is needed for calculating the required return to equity (cost of equity). 2) Historical market risk premium, which is the historical differential return of the stock market over treasury bonds. 3) Expected market risk premium, which is the expected differential return of the stock market over treasury bonds. Many authors and finance practitioners assume that the expected market risk premium is equal to the historical market risk premium and to the required market risk premium. The CAPM assumes that the required market risk premium is equal to the expected market risk premium. However, the three concepts are different. The historical market risk premium is equal for all investors, but the required and the expected market risk premium are different for different investors. We also claim that there is no required market risk premium for the market as a whole: different investors use different required market risk premiums. |
| Keywords: | required market risk premium; historical market risk premium; expected market risk premium; risk premium; equity premium; market premium; |
| JEL: | G12 G31 M21 |
| Date: | 2004–10–17 |
| URL: | http://d.repec.org/n?u=RePEc:ebg:iesewp:d-0574&r=rmg |
| By: | Feng Dai (Zhengzhou Information Engineering University); Feng Han (Zhengzhou Information Engineering University) |
| Abstract: | Based on the structure models of options pricing on non-dividend-paying stock [16], this paper presents the choosing models and methods of optimal time of executing an American options for the first time. By using the models and methods, we can find the choosing criterion and optimal time to exercise the American options, i.e. the product of options price and its occurring probability is at maximum. So we can decide that an American option should be exercised or not in any time. The conclusions in this paper are more important in its consulting effect for single trader and organization investors to make their security market trade. |
| Keywords: | partial distribution; American options; structure pricing; optimal executing; analytic formula |
| JEL: | G |
| Date: | 2004–12–10 |
| URL: | http://d.repec.org/n?u=RePEc:wpa:wuwpfi:0412016&r=rmg |
| By: | Madnick, Stuart; Wang, Richard; Chettayar, Krishna; Dravis, Frank; Funk, James; Katz-Haas, Raïssa; Lee, Cindy; Lee, Yang; Xian, Xiang; Bhansali, Sumit |
| Abstract: | Corporate household (CHH) refers to the organizational information about the structure within the corporation and a variety of inter-organizational relationships. Knowledge derived from this data is becoming increasingly important for improving data quality in applications, such as Customer Relationship Management (CRM), Enterprise Resource Planning (ERP), Supply Chain Management (SCM), risk management, and sales and market promotion. Extending the concepts from our previous CHH research, we exemplify in this paper the importance of improved corporate household knowledge and processing in various business application areas. Additionally, we provide examples of CHH business rules that are often implicit and fragmented - understood and practiced by different domain experts across functional areas of the firm. This paper is intended to form a foundation for further research to systematically investigate, capture, and build a body of corporate householding knowledge across diverse business applications. |
| Keywords: | Corporate Householding, Data Quality, Organizational Structures, Interdependence, Name Matching, Entity Aggregation, Information Quality, Account Consolidation, Conflict of Interest, Risk Management, Customer Relationship Management (CRM), Supply Chain Management (SCM), Regulation and Disclosure, |
| Date: | 2004–12–10 |
| URL: | http://d.repec.org/n?u=RePEc:mit:sloanp:7401&r=rmg |
| By: | Anthony W. Lynch; Sinan Tan |
| Abstract: | The seminal work of Constantinides (1986) documents how, when the risky return is calibrated to the U.S. market return, the impact of transaction costs on per-annum liquidity premia is an order of magnitude smaller than the cost rate itself. A number of recent papers have formed portfolios sorted on liquidity measures and found a spread in expected per-annum return that is definitely not an order of magnitude smaller than the transaction cost spread: the expected per-annum return spread is found to be around 6-7% per annum. Our paper bridges the gap between Constantinides' theoretical result and the empirical magnitude of the liquidity premium by examining dynamic portfolio choice with transaction costs in a variety of more elaborate settings that move the problem closer to the one solved by real-world investors. In particular, we allow returns to be predictable and transaction costs to be stochastic, and we introduce wealth shocks, both stationary multiplicative and labor income. With predictable returns, we also allow the wealth shocks and transaction costs to be state dependent. We find that adding these real world complications to the canonical problem can cause transactions costs to produce per-annum liquidity premia that are no longer an order of magnitude smaller than the rate, but are instead the same order of magnitude. For example, predictable returns and i.i.d. labor income growth causes the liquidity premium for an agent with a wealth to monthly labor income ratio of 0 or 10 to be 1.68\% and 1.20\% respectively; these are 21-fold and 15-fold increases, respectively, relative to that in the standard i.i.d. return case. We conclude that the effect of proportional transaction costs on the standard consumption and portfolio allocation problem with i.i.d. returns can be materially altered by reasonable perturbations that bring the problem closer to the one investors are actually solving. |
| JEL: | G11 G12 |
| Date: | 2004–12 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:10994&r=rmg |
| By: | Nikesh Agarwal (Stockholm School of Economics); Vikash Madhogaria (Stockholm School of Economics); Supreena Narayanan (Stockholm School of Economics) |
| Abstract: | In this report, we explain and analyze a trading strategy, popularly known as Pairs trading. We begin by explaining what a pair trading strategy entails. Since there are various ways of implementing the strategy, we describe the methodology selected by us in section 3. Thereafter, we look at the returns from the strategy and benchmark it to the S&P 500 index in Section 4. In section 5, we examine the risks involved in pairs trading. Section 6 looks at some of the limitations we faced while trading and Section 7 points out some mistakes we made. Finally, we discuss some risk control measures in Section 8 and conclude in Section 9 with comments on whether we would implement the strategy in real life and if so, with what changes. |
| JEL: | G |
| Date: | 2004–12–12 |
| URL: | http://d.repec.org/n?u=RePEc:wpa:wuwpfi:0412018&r=rmg |