### Table 2: Relative Performance with Moderate Knock-Out Probability

2001

"... In PAGE 21: ... Next, we test values of the estimator parameter r (the number of trials per step) in order to give guidelines for choosing the parameter in practice. Table2 gives the results for Examples 1a, 2a, and 3a, while Table 3 contains the results for the higher knock-out probability options of Examples 1b, 2b, and 3b. We focus on binary options, for which the methods are most effective.... ..."

Cited by 3

### Table 3: Relative Performance with High Knock-Out Probability

2001

"... In PAGE 21: ... Next, we test values of the estimator parameter r (the number of trials per step) in order to give guidelines for choosing the parameter in practice. Table 2 gives the results for Examples 1a, 2a, and 3a, while Table3 contains the results for the higher knock-out probability options of Examples 1b, 2b, and 3b. We focus on binary options, for which the methods are most effective.... In PAGE 21: ... This is reflected in several examples where increasing r too much eventually increases the computational expense. Table3 highlights one of the shortcomings of the negative binomial estimator. It is impractical to use it for Example 3b, in which the barrier only takes effect after two years.... ..."

Cited by 3

### Table 8: European knock-out call options in the NGARCH framework

1999

"... In PAGE 21: ... Alternatively, one can first obtain the relevant transition probability matrix over one week by raising the daily transition probability matrix to an appropriate power so that option valuation can be conducted on a weekly basis. Table8 examines barrier options identical to those in Table 3 except that we have changed the pricing framework from Black-Scholes to GARCH. The convergence speed as indicated in Table 8 improves over that in Table 7, and this phenomenon can be attributed to a shorter maturity of the options in Table 8 (from T = 0:5 down to T = 0:2).... In PAGE 21: ... Table 8 examines barrier options identical to those in Table 3 except that we have changed the pricing framework from Black-Scholes to GARCH. The convergence speed as indicated in Table 8 improves over that in Table 7, and this phenomenon can be attributed to a shorter maturity of the options in Table8 (from T = 0:5 down to T = 0:2). A shorter maturity requires a smaller number of states to obtain an equally good approximation.... ..."

Cited by 2

### TABLE 4 Phenotypic analysis of caveolin-deficient mice Cav-1 Knock-out Cav-2 Knock-out Cav-3 Knock-out

### Table 7. Knock-out-criteria used for the

1999

"... In PAGE 8: ... They are aggregates of those criteria, which were considered most important for the enterprise. Table7 summarizes these knock-out-criteria. judged as not sufficient for a mid-term implementation.... ..."

Cited by 2

### Table 8 gives the parameters for a two asset double knock-out pricing problem. The barriers are de ned as

1999

"... In PAGE 27: ...05 Time to maturity 0.25 years Exercise Price (K) $100 Barriers Applied Weekly Table8 : Parameters for the two asset double knock-out barrier problem. example problem is based on the worst of the two assets.... ..."

Cited by 6

### Table 2: Cross validation on knock-out pairs # hold-outs # trials % error

2003

"... In PAGE 13: ... This measure is conservative. Table2 shows the results of leave-n-out cross validation, where n equals to 1, 5, and 20. The results indicate that the algorithm can predict the knock-out effects with high degree of accuracy.... ..."

Cited by 5

### Table 2: Knock-out Probabilities of Down-and-Out Call Options n28Monthly Fren-

1997

"... In PAGE 11: ... A detailed analysis is in order. Table2 shows the knock-out probabilities of options with din0berent volatilities of the jump amplitude, n1b 2 n19 . The table illustrates a pattern of knock-out probabilities which roughly matches the pattern of the prices of down-and-out call options: Holding constant the volatil- ity of the underlying asset price S, over short maturities, an option with a more volatile jump component is more likely to be knocked-out than is an option with a more volatile din0busion component; over longer maturities, the relation is reversed: An increase in the jump volatility n1b n19 can substantially reduce the knock-out probability of long-term options.... In PAGE 12: ... Denote Dn28T n29 as the cumulative distribution function of the n0crst passage time to the barrier H for the pure din0busion process and denote Jn28T n29 as the cumulative distribution function of the n0crst passage time to the barrier for the pure jump process, where T is the maturity time. Using the result of Harrison n281990n29, wehave Dn28Tn29 = N n20 n00 lnn28S=Hn29+n28rn00n1b 2 =2n29T n1b p T ! +n28S=Hn29 n281n00 2r n1b 2 n29 N n20 n00 lnn28S=Hn29 n00 n28r n00 n1b 2 =2n29T n1b p T ! : n2811n29 Assuming that S =20at time zero and H =16as in Table2 , we obtain immediately that Dn281=4n29 = 0:00001 and Dn2818n29 = 0:599. There is no explicit expression for Jn28T n29.... In PAGE 14: ...out probabilities have not become large enough to change the properties of barrier options inherited from their standard counterparts. As the maturities become long enough, the knock-out probabilities of options with din0busion processes become much larger than the knock-out probabilities of options with jumps, holding n1b 2 S constant n28See Table2 n29. As a result, the options with more volatile jump components become more valuable.... ..."

### Table 2 Top-ranking knock-out experiments proposed for model discrimination

2005

"... In PAGE 3: ... R6 experiments were ranked by their projected information con- tent based on the inferred models (see Materials and meth- ods). Table2 reports the list of top-ranking experiments. This list coincides roughly with biological intuition, in the sense as discussed later, knocking out hubs only is not as effective as using the information-theoretic criteria.... ..."