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Some, such as liquidity risk, are addressed in this chapter, due to their critical and universal effect on issues of price risk management For a discussion of other types of risk management, such as credit and operational risk management, see Risk Management by Crouhy, Galai, and Mark During the early years of my career as a trader, Refco, one of the larger exchange-traded derivatives brokers, used Risk Is Everywhere as its advertising slogan I have always liked the phrase because it succinctly dispels one of the great myths of investing, namely, that we can somehow avoid financial risk This myth suggests that investment vehicles that do not guarantee a return of principal are risky and that we can somehow avoid risk by putting our money under the mattresses, in a bank, or in T-bills.

NONCENTERED SYSTEMS (SMOOTHED ANALYSIS)

Obviously, investing in vehicles that do not guarantee a return of principal entails greater risk than those that do; however, in choosing investment vehicles like T-bills, we end up accepting the opportunity risk inherent in our forfeiting a potentially higher rate of return This comparison of guaranteed return of principal investment vehicles with riskier investment alternatives illuminates an inescapable law of investment: The higher the risk, the greater the reward (and conversely, the lower the risk, the lower the reward) Promises of high reward and low risk suggest three possibilities:.

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In other words, we see that BSM-inspired dynamic hedging of knockout options can have two clear effects over the spot underlying market: 1) limit or even terminate the normal workings of market movements (force an upward market to stop going up or even start going down); 2) suddenly and perhaps dramatically accelerate the underlying trend (in case the barrier cannot be prevented from being breached).

be affected One process by which this might happen is that respondents could build up a greater sense of trust of the interviewer and the survey organisation as time passes If asked some questions on a sensitive topic at wave 3, say, they might then be more willing to reveal truthful answers than they would have been at wave 1 This example also illustrates that some forms of conditioning can result in improvements to data quality It is not the case that conditioning always damages quality Another way in which quality might improve over waves of a panel is if respondents learn that it is useful to prepare for the interview by assembling certain documents Examples of such documents might be pay slips, utility bills, health records or examination results, depending on the survey topics.

Figure 6.3 Proactive dependent interviewing: design options and effects. Q question , FF feed forward . White boxes indicate design options, grey boxes indicate effects and arrows indicate conceptual associations.

Panel conditioning is more likely to occur for certain topics and certain types of questions than for others, meaning that the extent to which the researcher needs to be concerned about panel conditioning and the measures that might be introduced to minimise any negative consequences of it should depend on the survey content Panel conditioning in the context of attitudinal measures is discussed in 7 Sample Attrition Sample attrition (also referred to as panel attrition) refers to the continued loss of respondents from the sample due to nonresponse at each wave of a longitudinal survey The term attrition is usually used to refer to a monotone process whereby sample members can change from being respondents to nonrespondents but not vice versa.

Long/Short Equity 38%

In fact, many longitudinal surveys continue to attempt to collect data from sample members after they have been nonrespondents at one or more waves, and are often successful in doing so Generally, then, the issue is that many sample members may have been nonrespondents at one or more wave, whether or not their response pattern is one of monotone attrition The response rate at any one wave of a longitudinal survey may be just as good as that for any other survey but after, say, five waves the proportion of sample units that have responded at every wave may be quite low Thus, the effective response rate for longitudinal analysis for which data from every wave is required may be lower than the response rates that we are used to having on cross-sectional surveys After several waves there is also a risk that responding sample sizes can become unacceptably small.

Minimising sample attrition is consequently a major preoccupation of researchers responsible for longitudinal surveys The factors affecting sample attrition are discussed in 10 s 11, 12 and 13 discuss various strategies for minimising attrition while s 14, 15 and 16 address issues of how to deal appropriately with attrition at the analysis stage Initial and Ongoing Costs The time dimension of a longitudinal survey brings with it a set of extra tasks for the survey organisation In particular, the design and planning work that must take place prior to wave 1 is considerably greater than that required prior to a cross-sectional survey The wave 1 instrument(s) cannot be designed independently of the instruments for later waves as it is the combination of data from each wave that will provide the necessary survey estimates.

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