Steven R. Dunbar
Department of Mathematics
203 Avery Hall
Lincoln, NE 68588-0130
http://www.math.unl.edu
Voice: 402-472-3731
Fax: 402-472-8466

Stochastic Processes and

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Duration of the Gambler’s Ruin

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Note: These pages are prepared with MathJax. MathJax is an open source JavaScript display engine for mathematics that works in all browsers. See http://mathjax.org for details on supported browsers, accessibility, copy-and-paste, and other features.

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### Rating

Mathematically Mature: may contain mathematics beyond calculus with proofs.

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### Section Starter Question

Consider a gambler who wins or loses a dollar on each turn of a fair game with probabilities $p=1∕2$ and $q=1∕2$ respectively. Let his initial capital be $10. The game continues until the gambler’s capital either is reduced to 0 or has increased to$20. What is the length of the shortest possible game the gambler could play? What are the chances of this shortest possible game? What is the length of the second shortest possible game? How would you ﬁnd the probability of this second shortest possible game occurring?

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### Key Concepts

1. The principle of ﬁrst-step analysis, also known as conditional expectations, provides equations for important properties of coin-ﬂipping games and random walks. The important properties include ruin probabilities and the duration of the game until ruin.
2. Diﬀerence equations derived from ﬁrst-step analysis or conditional expectations provide the way to deduce the expected length of the game in the gambler’s ruin, just as for the probability of ruin or victory.

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### Vocabulary

1. Expectation by conditioning is the process of deriving a diﬀerence equation for the expectation by conditioning the outcome over an exhaustive, mutually exclusive set of events, each of which leads to a simpler probability calculation, then weighting by the probability of each outcome of the conditioning events.
2. First Step Analysis is how J. Michael Steele refers to the simple expectation by conditioning process that we use to analyze the ruin probabilities and expected duration. It is a more speciﬁc description for coin-tossing games of the more general technique of expectation by conditioning.

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### Mathematical Ideas

#### Understanding a Stochastic Process

We start with a sequence of Bernoulli random variables, ${Y}_{1},{Y}_{2},{Y}_{3},\dots$ where ${Y}_{i}=+1$ with probability $p$ and ${Y}_{i}=-1$ with probability $q$. We start with an initial value ${T}_{0}$ and set ${Y}_{0}={T}_{0}$ for convenience. We deﬁne the sequence of sums ${T}_{n}={\sum }_{i=0}^{n}{Y}_{i}$. We want to understand the stochastic process ${T}_{1},{T}_{2},{T}_{3},\dots$. It turns out this is a complicated sequence to understand in full, so we single out particular simpler features to understand ﬁrst. For example, we can look at how many trials the process will experience until it achieves the value $0$ or $a$. In symbols, consider It is possible to consider the probability distribution of this newly deﬁned random variable. Even this turns out to be complicated, so we look at the expected value of the number of trials, $D=𝔼\left[N\right]$. This is a special case of a larger class of probability problems called ﬁrst-passage distributions for ﬁrst-passage times.

The principle of ﬁrst-step analysis, also known as conditional expectations, provides equations for important properties of coin-ﬂipping games and random walks. The important properties include ruin probabilities and the duration of the game until ruin. Diﬀerence equations derived from ﬁrst-step analysis or conditional expectations provide the way to deduce the expected length of the game in the gambler’s ruin, just as for the probability of ruin or victory. Expectation by conditioning is the process of deriving a diﬀerence equation for the expectation by conditioning the outcome over an exhaustive, mutually exclusive set of events, each of which leads to a simpler probability calculation, then weighting by the probability of each outcome of the conditioning events. First Step Analysis is how J. Michael Steele refers to the simple expectation by conditioning process that we use to analyze the ruin probabilities and expected duration. It is a more speciﬁc description for coin-tossing games of the more general technique of expectation by conditioning.

#### Expected length of the game

Note that in the following we implicitly assume that the expected duration of the game is ﬁnite. This fact is true, see below for a proof.

Theorem 1. The expected duration of the game in the classical ruin problem is

and

Proof. If the ﬁrst trial results in success, the game continues as if the initial position had been ${T}_{0}+1$. The conditional expectation of the duration conditioned on success at the ﬁrst trial is therefore ${D}_{{T}_{0}+1}+1$. Likewise if the ﬁrst trial results in a loss, the duration conditioned on the loss at the ﬁrst trial is ${D}_{{T}_{0}-1}+1$.

This argument shows that the expected duration satisﬁes the diﬀerence equation, obtained by expectation by conditioning

${D}_{{T}_{0}}=p{D}_{{T}_{0}+1}+q{D}_{{T}_{0}-1}+1$

with the boundary conditions

${D}_{0}=0,{D}_{a}=0.$

The appearance of the term 1 makes the diﬀerence equation non-homogeneous. Taking a cue from linear algebra, or more speciﬁcally the theory of linear non-homogeneous diﬀerential equations, we need to ﬁnd the general solution to the homogeneous equation

${D}_{{T}_{0}}^{h}=p{D}_{{T}_{0}+1}^{h}+q{D}_{{T}_{0}-1}^{h}$

and a particular solution to the non-homogeneous equation. We already know the general solution to the homogeneous equation is ${D}_{{T}_{0}}^{h}=A+B{\left(q∕p\right)}^{{T}_{0}}$. The best way to ﬁnd the particular solution is inspired guessing, based on good experience. We can re-write the non-homogeneous equation for the particular solution as

$-1=p{D}_{{T}_{0}+1}-{D}_{{T}_{0}}+q{D}_{{T}_{0}-1}.$

The right side is a weighted second diﬀerence, a diﬀerence equation analog of the second derivative. Functions whose second derivative is a constant are quadratic functions. Therefore, it make sense to try a function of the form ${D}_{{T}_{0}}^{p}=k+l{T}_{0}+m{T}_{0}^{2}$. In the exercises, we show that the particular solution is actually ${D}_{{T}_{0}}={T}_{0}∕\left(q-p\right)$ if $p\ne q$.

It follows that the general solution of the duration equation is:

${D}_{{T}_{0}}={T}_{0}∕\left(q-p\right)+A+B{\left(q∕p\right)}^{{T}_{0}}.$

The boundary conditions require that

$\begin{array}{llll}\hfill A+B& =0\phantom{\rule{2em}{0ex}}& \hfill & \\ \multicolumn{4}{c}{\text{and}}\\ \phantom{\rule{2em}{0ex}}\\ \hfill A+B{\left(q∕p\right)}^{a}+a∕\left(q-p\right)& =0.\phantom{\rule{2em}{0ex}}& \hfill & \phantom{\rule{2em}{0ex}}\end{array}$

Solving for $A$ and $B$, we ﬁnd

${D}_{{T}_{0}}=\frac{{T}_{0}}{q-p}-\frac{a}{q-p}\frac{1-{\left(q∕p\right)}^{{T}_{0}}}{1-{\left(q∕p\right)}^{a}}.$

The calculations are not valid if $p=1∕2=q$. In this case, the particular solution ${T}_{0}∕\left(q-p\right)$ no longer makes sense for the equation

${D}_{{T}_{0}}=\frac{1}{2}{D}_{{T}_{0}+1}+\frac{1}{2}{D}_{{T}_{0}-1}+1$

The reasoning about the particular solution remains the same however, and we can show that the particular solution is $-{{T}_{0}}^{2}$. It follows that the general solution is of the form ${D}_{{T}_{0}}=-{{T}_{0}}^{2}+A+B{T}_{0}$. The required solution satisfying the boundary conditions is

${D}_{{T}_{0}}={T}_{0}\left(a-{T}_{0}\right).$

Corollary 1. Playing until ruin with no upper goal for victory against an inﬁnitely rich adversary, the expected duration of the game until ruin is

and

Proof. Pass to the limit $a\to \infty$ in the preceding formulas. □

The duration can be considerably longer than we expect naively. For instance in a fair game, with two players with $500 each ﬂipping a coin until one is ruined, the average duration of the game is 250,000 trials. If a gambler has only$1 and his adversary $1000, with a fair coin toss, the average duration of the game is 999 trials, although some games will be quite short! Very long games can occur with suﬃcient probability to give a long average. #### Some Calculations for Illustration  $p$ $q$ ${T}_{0}$ $a$ Probability Expected of Ruin Duration 0.5 0.5 9 10 0.1000 9 0.5 0.5 90 100 0.1000 900 0.5 0.5 900 1,000 0.1000 90,000 0.5 0.5 950 1,000 0.0500 47,500 0.5 0.5 8,000 10,000 0.2000 16,000,000 0.45 0.55 9 10 0.2101 11 0.45 0.55 90 100 0.8656 766 0.45 0.55 99 100 0.1818 172 0.4 0.6 90 100 0.9827 441 0.4 0.6 99 100 0.3333 162 #### Proof that the duration is ﬁnite The following discussion of ﬁniteness of the duration of the game is adapted from [2] by J. Michael Steele. When we check the arguments for the probability of ruin or the duration of the game, we ﬁnd a logical gap. We have assumed that the duration ${D}_{{T}_{0}}$ of the game is ﬁnite. How do we know for sure that the gambler’s net winnings will eventually reach $a$ or $0$? This important fact requires proof. The proof uses a common argument in probability, an “extreme case argument”. We identify an “extreme” event with a small but positive probability of occurring. We are interested in the complementary “good” event which at least avoids the extreme event. Therefore the complementary event must happen with probability not quite $1$. The avoidance must happen inﬁnitely many independent times, but the probability of such a run of “good” events must go to zero. For the gambler’s ruin, we are interested in the event of the game continuing forever. Consider the extreme event that the gambler wins $a$ times in a row. If the gambler is not already ruined (at 0), then such a streak of $a$ wins in a row is guaranteed to boost his fortune above $a$ and end the game in victory for the gambler. Such a run of luck is unlikely, but it has positive probability, in fact, probability $P={p}^{a}$. We let ${E}_{k}$ denote the event that the gambler wins on each turn in the time interval $\left[ka,\left(k+1\right)a-1\right]$, so the ${E}_{k}$ are independent events. Hence the complementary events ${E}_{k}^{C}=\Omega -{E}_{k}$ are also independent. Then $D>na$ at least implies that all of the ${E}_{k}$ with $0\le k\le n$ fail to occur. Thus, we ﬁnd $ℙ\left[{D}_{{T}_{0}}>na\right]\le ℙ\left[\bigcap _{k=0}^{n}{E}_{k}^{C}\right]={\left(1-P\right)}^{n}.$ Note that $ℙ\left[{D}_{{T}_{0}}=\infty \phantom{\rule{0.3em}{0ex}}|\phantom{\rule{0.3em}{0ex}}{T}_{0}=z\right]\le ℙ\left[D>na\phantom{\rule{0.3em}{0ex}}|\phantom{\rule{0.3em}{0ex}}{T}_{0}=z\right]$ for all $n$. Hence, $ℙ\left[{D}_{{T}_{0}}=\infty \right]=0$, justifying our earlier assumption. #### Sources This section is adapted from [2] with additional background information from [1]. ### Algorithms, Scripts, Simulations #### Algorithm The goal is to simulate the duration until ruin or victory as a function of starting value. First set the probability $p$, number of Bernoulli trials $n$, and number of experiments $k$. Set the ruin and victory values $r$ and $v$, also interpreted as the boundaries for the random walk. For each starting value from ruin to victory, ﬁll an $n×k$ matrix with the Bernoulli random variables. Languages with multi-dimensional arrays keep the data in a three-dimensional array of size $n×k×\left(v-r+1\right)$. Cumulatively sum the Bernoulli random variables to create the fortune or random walk. For each starting value, for each random walk or fortune path, ﬁnd the duration until ruin or victory. For each starting value, ﬁnd the mean of the duration until ruin or victory. Finally, ﬁnd a least squares polynomial ﬁt for the duration as a function of the starting value. Geogebra GeoGebra script for duration.. R 1p <- 0.5 2n <- 300 3k <- 200 4 5victory <- 10 6# top boundary for random walk 7ruin <- 0 8# bottom boundary for random walk 9interval <- victory - ruin + 1 10 11winLose <- 2 * (array( 0+(runif(n*k*interval) <= p), dim=c(n,k, 12interval))) - 1 13# 0+ coerces Boolean to numeric 14totals <- apply( winLose, 2:3, cumsum) 15# the second argument ‘‘2:3’’ means column-wise in each panel 16start <- outer( array(1, dim=c(n+1,k)), ruin:victory, "*") 17 18paths <- array( 0 , dim=c(n+1, k, interval) ) 19paths[2:(n+1), 1:k, 1:interval] <- totals 20paths <- paths + start 21 22hitVictory <- apply(paths, 2:3, (function(x)match(victory,x, nomatch=n+2))); 23hitRuin <- apply(paths, 2:3, (function(x)match(ruin, x, nomatch=n+2))); 24# the second argument ‘‘2:3’’ means column-wise in each panel 25# If no ruin or victory on a walk, nomatch=n+2 sets the hitting 26# time to be two more than the number of steps, one more than 27# the column length. Without the nomatch option, get NA which 28# works poorly with the comparison hitRuin < hitVictory next. 29 30duration <- pmin(hitVictory, hitRuin) - 1 31# Subtract 1 since R arrays are 1-based, so duration is 1 less than index 32is.na(duration) = duration > n 33# Remove durations greater than length of trials 34meanDuration = colMeans( duration, na.rm=TRUE) 35 36startValues <- (ruin:victory); 37durationFunction <- lm( meanDuration ~ poly(startValues,2,raw=TRUE) ) 38# lm is the R function for linear models, a more general view of 39# least squares linear fitting for response ~ terms 40 41plot(startValues, meanDuration, col = "blue"); 42lines(startValues, predict(durationFunction, data=startValues), col = "red") 43 44cat(sprintf("Duration function is: %f + %f x + %f x^2 \n", 45 coefficients(durationFunction)[1], coefficients(durationFunction)[2], 46 coefficients(durationFunction)[3] )) 47 48 0cAp0x1-1300049: Octave 1p = 0.5; 2n = 300; 3k = 200; 4 5victory = 10; 6# top boundary for random walk 7ruin = 0; 8# bottom boundary for random walk 9 10probRuinBeforeVictory = zeros(1, victory-ruin+1); 11for start = ruin:victory 12 13 winLose = 2 * (rand(n,k) <= p) - 1; 14 # -1 for Tails, 1 for Heads 15 totals = cumsum(winLose); 16 # -n..n (every other integer) binomial rv sample 17 18 paths = [zeros(1,k); totals] + start; 19 victoryOrRuin = zeros(1,k); 20 for j = 1:k 21 hitVictory = find(paths(:,j) >= victory); 22 hitRuin = find(paths(:,j) <= ruin); 23 if ( !rows(hitVictory) && !rows(hitRuin) ) 24 # no victory, no ruin 25 # do nothing 26 elseif ( rows(hitVictory) && !rows(hitRuin) ) 27 # victory, no ruin 28 victoryOrRuin(j) = hitVictory(1)-1; 29 ## subtract 1 since vectors are 1-based 30 elseif ( !rows(hitVictory) && rows(hitRuin) ) 31 # no victory, but hit ruin 32 victoryOrRuin(j) = -(hitRuin(1)-1); 33 ## subtract 1 since vectors are 1-based 34 else # ( rows(hitvictory) && rows(hitruin) ) 35 # victory and ruin 36 if ( hitVictory(1) < hitRuin(1) ) 37 victoryOrRuin(j) = hitVictory(1)-1; 38 # code hitting victory 39 ## subtract 1 since vectors are 1-based 40 else 41 victoryOrRuin(j) = -(hitRuin(1)-1); 42 # code hitting ruin as negative 43 ## subtract 1 since vectors are 1-based 44 endif 45 endif 46 endfor 47 48 durationUntilVictoryOrRuin(start + (-ruin+1)) = mean(abs( victoryOrRuin )); 49 50endfor 51 52durationFunction = polyfit([ruin:victory], durationUntilVictoryOrRuin, \ 53 2); 54 55plot([ruin:victory], durationUntilVictoryOrRuin, +r); 56hold on; 57 58x = linspace(ruin, victory, 101); 59fittedDuration = polyval(durationFunction, x); 60plot(x, fittedDuration, -); 61hold off; 62 63disp("Duration function is a_2 + a_1 x + a_0 x^2 where:") 64disp("a_2"), disp(durationFunction(3)), 65disp("a_1"), disp(durationFunction(2)), 66disp("a_0"), disp(durationFunction(1)) 67 68 0cAp1x1-1300069: Perl 1use PDL::NiceSlice; 2 3$p        = 0.5;
4$n = 300; 5$k        = 200;
6$victory = 10; 7$ruin     = 0;
8$interval =$victory - $ruin + 1; 9$winLose  = 2 * ( random( $k,$n, $interval ) <=$p ) - 1;
10$totals = ( cumusumover$winLose->xchg( 0, 1 ) )->transpose;
11$start = zeroes($k, $n + 1,$interval )->zlinvals( $ruin,$victory );
12
13$paths = zeroes($k, $n + 1,$interval );
14
15# use PDL:NiceSlice on next line
16# Note the use of the concat operator here.
17$paths ( 0 : ($k - 1 ), 1 : $n, 0 : ($interval - 1 ) ) .= $totals; 18 19$paths      = $paths +$start;
20$hitVictory =$paths->setbadif( $paths <$victory );
21$hitRuin =$paths->setbadif( $paths >$ruin );
22
23$victoryIndex = 24 ($hitVictory ( ,, : )->xchg( 0, 1 )->minimum_ind )
25    ->inplace->setbadtoval( $n + 1 ); 26$ruinIndex =
27    ( $hitRuin ( ,, : )->xchg( 0, 1 )->maximum_ind ) 28 ->inplace->setbadtoval($n + 1 );
29
30$durationUntilRuinOrVictory = 31 ($victoryIndex->glue( 2, $ruinIndex )->xchg( 2, 1 ) )->xchg( 0, 1 ) 32 ->setvaltobad($n + 1 )->minimum;
33( $mean,$prms, $median,$min, $max,$adev, $rms ) = 34 statsover($durationUntilRuinOrVictory);
35
36use PDL::Fit::Polynomial;
37$x = zeroes($interval)->xlinvals( $ruin,$victory );
38( $ruinFunction,$coeffs ) = fitpoly1d $x,$mean, 3;
39print "Duration function is: ", $coeffs (0), "+",$coeffs (1), "x+",
40    $coeffs (2), "x^2", "\n"; 41 0cAp2x1-1300042: SciPy 1import scipy 2 3p = 0.5 4n = 300 5k = 200 6victory = 10; 7ruin = 0; 8interval = victory - ruin + 1 9 10winLose = 2*( scipy.random.random((n,k,interval)) <= p ) - 1 11totals = scipy.cumsum(winLose, axis = 0) 12 13start = scipy.multiply.outer( scipy.ones((n+1,k), dtype=int), scipy.arange(ruin, victory+1, dtype=int)) 14paths = scipy.zeros((n+1,k,interval), dtype=int) 15paths[ 1:n+1, :,:] = totals 16paths = paths + start 17 18def match(a,b,nomatch=None): 19 return b.index(a) if a in b else nomatch 20# arguments: a is a scalar, b is a python list, value of nomatch is scalar 21# returns the position of first match of its first argument in its second argument 22# but if a is not there, returns the value nomatch 23# modeled on the R function "match", but with less generality 24 25hitVictory = scipy.apply_along_axis(lambda x:( match(victory,x.tolist(),nomatch=n+2)), 0, paths) 26hitRuin = scipy.apply_along_axis(lambda x:match(ruin,x.tolist(),nomatch=n+2), 0, paths) 27# If no ruin or victory on a walk, nomatch=n+2 sets the hitting 28# time to be two more than the number of steps, one more than 29# the column length. 30durationUntilRuinOrVictory = scipy.minimum(hitVictory, hitRuin) 31 32import numpy.ma 33durationMasked = scipy.ma.masked_greater(durationUntilRuinOrVictory, n) 34 35meanDuration = scipy.mean(durationUntilRuinOrVictory, axis = 0) 36durationFunction = scipy.polyfit( scipy.arange(ruin, victory+1, dtype=int), meanDuration, 2) 37print "Duration function is: ", durationFunction[2], "+", durationFunction[1], "x+", durationFunction[0], "x^2" 38# should return coefficients to (x-ruin)*(victory - x), descending degree order 39 0cAp3x1-1300040: _______________________________________________________________________________________________ ### Problems to Work for Understanding 1. Using a trial function of the form ${D}_{{T}_{0}}^{p}=k+l{T}_{0}+m{T}_{0}^{2}$, show that a particular solution of the non-homogeneous equation ${D}_{{T}_{0}}=p{D}_{{T}_{0}+1}+q{D}_{{T}_{0}-1}+1$ is ${T}_{0}∕\left(q-p\right)$. 2. Using a trial function of the form ${D}_{{T}_{0}}^{p}=k+l{T}_{0}+m{T}_{0}^{2}$, show that a particular solution of the non-homogeneous equation ${D}_{{T}_{0}}=\frac{1}{2}{D}_{{T}_{0}+1}+\frac{1}{2}{D}_{{T}_{0}-1}+1$ is $-{T}_{0}^{2}$. 1. A gambler starts with$2 and wants to win $2 more to get to a total of$4 before being ruined by losing all his money. He plays a coin-ﬂipping game, with a coin that changes with his fortune.
1. If the gambler has $2 he plays with a coin that gives probability $p=1∕2$ of winning a dollar and probability $q=1∕2$ of losing a dollar. 2. If the gambler has$3 he plays with a coin that gives probability $p=1∕4$ of winning a dollar and probability $q=3∕4$ of losing a dollar.
3. If the gambler has $1 he plays with a coin that gives probability $p=3∕4$ of winning a dollar and probability $q=1∕4$ of losing a dollar. Use “ﬁrst step analysis” to write three equations in three unknowns (with two additional boundary conditions) that give the expected duration of the game that the gambler plays. Solve the equations to ﬁnd the expected duration. 2. A gambler plays a coin ﬂipping game in which the probability of winning on a ﬂip is $p=0.4$ and the probability of losing on a ﬂip is $q=1-p=0.6$. The gambler wants to reach the victory level of$16 before being ruined with a fortune of $0. The gambler starts with$8, bets $2 on each ﬂip when the fortune is$6, $8,$10 and bets $4 when the fortune is$4 or $12. Compute the probability of ruin in this game. 3. This problem is adapted from Stochastic Calculus and Financial Applications by J. Michael Steele, Springer, New York, 2001, Chapter 1, Section 1.6, page 9. Information on buy-backs is adapted from investorwords.com. This problem suggests how results on biased random walks can be worked into more realistic models. Consider a naive model for a stock that has a support level of$20/share because of a corporate buy-back program. (This means the company will buy back stock if shares dip below $20 per share. In the case of stocks, this reduces the number of shares outstanding, giving each remaining shareholder a larger percentage ownership of the company. This is usually considered a sign that the company’s management is optimistic about the future and believes that the current share price is undervalued. Reasons for buy-backs include putting unused cash to use, raising earnings per share, increasing internal control of the company, and obtaining stock for employee stock option plans or pension plans.) Suppose also that the stock price moves randomly with a downward bias when the price is above$20, and randomly with an upward bias when the price is below $20. To make the problem concrete, we let ${Y}_{n}$ denote the stock price at time $n$, and we express our stock support hypothesis by the assumptions that $\begin{array}{rcll}ℙ\left[{Y}_{n+1}=21|{Y}_{n}=20\right]& =& 9∕10& \text{}\\ ℙ\left[{Y}_{n+1}=19|{Y}_{n}=20\right]& =& 1∕10& \text{}\end{array}$ We then reﬂect the downward bias at price levels above$20 by requiring that for $k>20$:

$\begin{array}{rcll}ℙ\left[{Y}_{n+1}=k+1|{Y}_{n}=k\right]& =& 1∕3& \text{}\\ ℙ\left[{Y}_{n+1}=k-1|{Y}_{n}=k\right]& =& 2∕3.& \text{}\end{array}$

We then reﬂect the upward bias at price levels below $20 by requiring that for $k<20$: $\begin{array}{rcll}ℙ\left[{Y}_{n+1}=k+1|{Y}_{n}=k\right]& =& 2∕3& \text{}\\ ℙ\left[{Y}_{n+1}=k-1|{Y}_{n}=k\right]& =& 1∕3& \text{}\end{array}$ Using the methods of “single-step analysis” calculate the expected time for the stock to fall from$25 through the support level all the way down to \$18. (There is no reasonable way to solve this problem using formulas. Instead you will have to go back to basic principles of single-step or ﬁrst-step analysis to solve the problem.)

4. Several North American professional sports leagues have a “best-of-seven” format for their seasonal championship (the World Series for Major League Baseball, the NBA Finals for the National Basketball Association and the Stanley Cup Finals for the National Hockey League.) A best-of-seven playoﬀ is a sequence of games between two teams in which one team must win four games to win the series. If one team has won four games before all seven games have been played, remaining games are omitted.
1. Explain why or why not the ﬁrst-step analysis for the expected duration model for victory-or-ruin is sensible for estimating the expected length of a best-of-seven championship series.
2. How many games would we expect to be needed to complete a best-of-seven series if each team has a $50$ percent chance of winning each individual game? What modeling assumptions are you making?
3. Using the same assumptions how many games are expected to complete a best-of-seven series if one team has a $60$ percent chance of winning each individual game? A $70$ per cent chance?
4. Using the same assumptions, make a graph of the expected number of games as a function of $p$, the probability of one team winning an individual game.
5. Perform some simulations of the coin-ﬂipping game, varying $p$ and the start value. How does the value of $p$ aﬀect the experimental duration of victory and ruin?
6. Modify the simulations by changing the value of $p$ and comparing the experimental results for each starting value to the theoretical duration function.
7. Modify the duration scripts to perform simulations of the duration calculations in the table in the section Some Calculations for Illustration and compare the results.

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### References

[1]   William Feller. An Introduction to Probability Theory and Its Applications, Volume I, volume I. John Wiley and Sons, third edition, 1973. QA 273 F3712.

[2]   J. Michael Steele. Stochastic Calculus and Financial Applications. Springer-Verlag, 2001. QA 274.2 S 74.

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1. Virtual Labs in Probability. Games of Chance. Scroll down and select the Red and Black Experiment (marked in red in the Applets Section. Read the description since the scenario is slightly diﬀerent but equivalent to the description above.)
2. University of California, San Diego, Department of Mathematics, A.M. Garsia. A java applet that simulates how long it takes for a gambler to go broke. You can control how much money you and the casino start with, the house odds, and the maximum number of games. Results are a graph and a summary table. Submitted by Matt Odell, September 8, 2003.

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