20
Apr 21

Put debit spread

Put debit spread

This post parallels the one about the call debit spread. A combination of several options in one trade is called a strategy. Here we discuss a strategy called a put debit spread. The word "debit" in this name means that a trader has to pay for it. The rule of thumb is that if it is a debit (you pay for a strategy), then it is less risky than if it is a credit (you are paid). Let p(K) denote the price of the put with the strike K, suppressing all other variables that influence the put price.

Assumption. The market values higher events of higher probability. This is true if investors are rational and the market correctly reconciles views of different investors.

We need the following property: if K_{1}<K_{2} are two strike prices, then for the corresponding put prices (with the same expiration and underlying asset) one has p(K_{1})<p(K_{2}).

Proof.  A put price is higher if the probability of it being in the money at expiration is higher. Let S(T) be the stock price at expiration T. Since T is a moment in the future, S(T) is a random variable. For a given strike K, the put is said to be in the money at expiration if S(T)<K. If K_{1}<K_{2} and S(T)<K_{1}, then S(T)<K_{2}. It follows that the set \{ S(T)<K_{1}\} is a subset of the set \{S(T)<K_{2}\} . Hence the probability of the event \{S(T)<K_{2}\} is higher than that of the event \{S(T)<K_{1}\} and p(K_{2})>p(K_{1}).

Put debit spread strategy. Select two strikes K_{1}<K_{2}, buy p(K_{2}) (take a long position) and sell p(K_{1}) (take a short position). You pay p=p(K_{2})-p(K_{1})>0 for this.

Our purpose is to derive the payoff for this strategy. We remember that if S(T)\ge K, then the put p(K) expires worthless.

Case S(T)\ge K_{2}. In this case both options expire worthless and the payoff is the initial outlay: payoff =-p.

Case K_{1}\leq S(T)<K_{2}. Exercising the put p(K_{2}), in comparison with selling the stock at the market price you gain K_{2}-S(T). The second option expires worthless. The payoff is: payoff =K_{2}-S(T)-p.

Case S(T)<K_{1}. Both options are exercised. The gain from p(K_{2}) is, as above, K_{2}-S(T). The holder of the long put p(K_{1}) sells you stock at price K_{1}. Since your position is short, you have nothing to do but comply. The alternative would be to buy at the market price, so you lose S(T)-K_{1}. The payoff is: payoff =\left(K_{2}-S(T)\right) +\left( S(T)-K_{1}\right) -p=K_{2}-K_{1}-p.

Summarizing, we get:

payoff =\left\{\begin{array}{ll}  -p, & K_2\le S(T) \\  K_{2}-S(T)-p, & K_{1}\leq S(T)<K_{2}\\  K_{2}-K_{1}-p, & S(T)<K_{1}  \end{array}\right.

Normally, the strikes are chosen so that K_{2}-K_{1}>p. From the payoff expression we see then that the maximum profit is K_{2}-K_{1}-p>0, the maximum loss is -p and the breakeven stock price is S(T)=K_{2}-p. This is illustrated in Figure 1, where the stock price at expiration is on the horizontal axis.

Payoff from put debit spread

Figure 1. Payoff from put debit spread. Source: https://www.optionsbro.com/

Conclusion. For the strategy to be profitable, the price at expiration should satisfy S(T)< K_{2}-p. Buying a put debit spread is appropriate when the price is expected to stay in that range.

In comparison with the long put position p(K_{2}), taking at the same time the short call position -p(K_{1}) allows one to reduce the initial outlay. This is especially important when the stock volatility is high, resulting in a high put price. In the difference p(K_{2})-p(K_{1}) that volatility component partially cancels out.

Remark. There is an important issue of choosing the strikes. Let S denote the stock price now. The payoff expression allows us to rank the next choices in the order of increasing risk: 1) S<K_1<K_2 (both options are in the money, less risk), 2) K_1<S<K_2 and 3) K_1<K_2<S (both options are out of the money, highest risk).  Also remember that a put debit spread is less expensive than buying p(K_{2}) and selling p(K_{1}) in two separate transactions.

Exercise. Analyze a put credit spread, in which you sell p(K_{2}) and buy p(K_{1}).

21
Mar 21

Call debit spread

Call debit spread

A combination of several options in one trade is called a strategy. Here we discuss a strategy called a call debit spread. The word "debit" in this name means that a trader has to pay for it. The rule of thumb is that if it is a debit (you pay for a strategy), then it is less risky than if it is a credit (you are paid). Let c(K) denote the call price with the strike K, suppressing all other variables that influence the call price.

Assumption. The market values higher events of higher probability. This is true if investors are rational and the market correctly reconciles views of different investors.

We need the following property: if K_{1}<K_{2} are two strike prices, then for the corresponding call prices (with the same expiration and underlying asset) one has c(K_{1})>c(K_{2}).

Proof.  A call price is higher if the probability of it being in the money at expiration is higher. Let S(T) be the stock price at expiration T. Since T is a moment in the future, S(T) is a random variable. For a given strike K, the call is said to be in the money at expiration if S(T)>K. If K_{1}<K_{2} and S(T)>K_{2}, then S(T)>K_{1}. It follows that the set \{ S(T)>K_{2}\} is a subset of the set \{S(T)>K_{1}\} . Hence the probability of the event \{S(T)>K_{2}\} is lower than that of the event \{S(T)>K_{1}\} and c(K_{1})>c(K_{2}).

Call debit spread strategy. Select two strikes K_{1}<K_{2}, buy c(K_{1}) (take a long position) and sell c(K_{2}) (take a short position). You pay p=c(K_{1})-c(K_{2})>0 for this.

Our purpose is to derive the payoff for this strategy. We remember that if S(T)\leq K, then the call c(K) expires worthless.

Case S(T)\leq K_{1}. In this case both options expire worthless and the payoff is the initial outlay: payoff =-p.

Case K_{1}<S(T)\leq K_{2}. Exercising the call c(K_{1}) and immediately selling the stock at the market price you gain S(T)-K_{1}. The second option expires worthless. The payoff is: payoff =S(T)-K_{1}-p. (In fact, you are assigned stock and selling it is up to you).

Case K_{2}<S(T). Both options are exercised. The gain from c(K_{1}) is, as above, S(T)-K_{1}. The holder of the long call c(K_{2}) buys from you at price K_{2}. Since your position is short, you have nothing to do but comply. You buy at S(T) and sell at K_{2}. Thus the loss from -c(K_{2}) is K_{2}-S(T). The payoff is: payoff =\left(S(T)-K_{1}\right) +\left( K_{2}-S(T)\right) -p=K_{2}-K_{1}-p.

Summarizing, we get:

payoff =\left\{\begin{array}{ll}  -p, & S(T)\leq K_{1} \\  S(T)-K_{1}-p, & K_{1}<S(T)\leq K_{2} \\  K_{2}-K_{1}-p, & K_{2}<S(T)  \end{array}\right.

Normally, the strikes are chosen so that K_{2}-K_{1}>p. From the payoff expression we see then that the maximum profit is K_{2}-K_{1}-p>0, the maximum loss is -p and the breakeven stock price is S(T)=K_{1}+p. This is illustrated in Figure 1, where the stock price at expiration is on the horizontal axis.

Payoff for call debit strategy

Figure 1. Payoff for call debit strategy. Source: https://www.optionsbro.com/

Conclusion. For the strategy to be profitable, the price at expiration should satisfy S(T)\geq K_{1}+p. Buying a call debit spread is appropriate when the price is expected to stay in that range.

In comparison with the long call position c(K_{1}), taking at the same time the short call position -c(K_{2}) allows one to reduce the initial outlay. This is especially important when the stock volatility is high, resulting in a high call price. In the difference c(K_{1})-c(K_{2}) that volatility component partially cancels out.

Remark. There is an important issue of choosing the strikes. Let S denote the stock price now. The payoff expression allows us to rank the next choices in the order of increasing risk: 1) K_1<K_2<S (both options are in the money, less risk), 2) K_1<S<K_2 and 3) K_1<K_2<S (both options are out of the money, highest risk).  Also remember that a call debit spread is less expensive than buying c(K_{1}) and selling c(K_{2}) in two separate transactions.

Exercise. Analyze a call credit spread, in which you sell c(K_{1}) and buy c(K_{2}).

27
Jan 21

My book is gaining international recognition

AP Stats and Business Stats

Its content, organization and level justify its adoption as a textbook for introductory statistics for Econometrics in most American or European universities. The book's table of contents is somewhat standard, the innovation comes in a presentation that is crisp, concise, precise and directly relevant to the Econometrics course that will follow. I think instructors and students will appreciate the absence of unnecessary verbiage that permeates many existing textbooks.

Having read Professor Mynbaev's previous books and research articles I was not surprised with his clear writing and precision. However, I was surprised with an informal and almost conversational one-on-one style of writing which should please most students. The informality belies a careful presentation where great care has been taken to present the material in a pedagogical manner.

Carlos Martins-Filho
Professor of Economics
University of Colorado at Boulder
Boulder, USA

18
Oct 20

People need real knowledge

People need real knowledge

Traffic analysis

The number of visits to my website has exceeded 206,000. This number depends on what counts as a visit. An external counter, visible to everyone, writes cookies to the reader's computer and counts many visits from one reader as one. The number of individual readers has reached 23,000. The external counter does not give any more statistics. I will give all the numbers from the internal counter, which is visible only to the site owner.

I have a high percentage of complex content. After reading one post, the reader finds that the answer he is looking for depends on the preliminary material. He starts digging it and then has to go deeper and deeper. Hence the number 206,000, that is, one reader visits the site on average 9 times on different days. Sometimes a visitor from one post goes to another by link on the same day. Hence another figure: 310,000 readings.

I originally wrote simple things about basic statistics. Then I began to write accompanying materials for each advanced course that I taught at Kazakh-British Technical University (KBTU). The shift in the number and level of readership shows that people need deep knowledge, not bait for one-day moths.

For example, my simple post on basic statistics was read 2,300 times. In comparison, the more complex post on the Cobb-Douglas function has been read 7,100 times. This function is widely used in economics to model consumer preferences (utility function) and producer capabilities (production function). In all textbooks it is taught using two-dimensional graphs, as P. Samuelson proposed 85 years ago. In fact, two-dimensional graphs are obtained by projection of a three-dimensional graph, which I show, making everything clear and obvious.

The answer to one of the University of London (UoL) exam problems attracted 14,300 readers. It is so complicated that I split the answer into two parts, and there are links to additional material. On the UoL exam, students have to solve this problem in 20-30 minutes, which even I would not be able to do.

Why my site is unique

My site is unique in several ways. Firstly, I tell the truth about the AP Statistics books. This is a basic statistics course for those who need to interpret tables, graphs and simple statistics. If you have a head on your shoulders, and not a Google search engine, all you need to do is read a small book and look at the solutions. I praise one such book in my reviews. You don't need to attend a two-semester course and read an 800-page book. Moreover, one doesn't need 140 high-quality color photographs that have nothing to do with science and double the price of a book.

Many AP Statistics consumers (that's right, consumers, not students) believe that learning should be fun. Such people are attracted by a book with anecdotes that have no relation to statistics or the life of scientists. In the West, everyone depends on each other, and therefore all the reviews are written in a superlative degree and streamlined. Thank God, I do not depend on the Western labor market, and therefore I tell the truth. Part of my criticism, including the statistics textbook selected for the program "100 Textbooks" of the Ministry of Education and Science of Kazakhstan (MES), is on Facebook.

Secondly, I have the world's only online, free, complete matrix algebra tutorial with all the proofs. Free courses on Udemy, Coursera and edX are not far from AP Statistics in terms of level. Courses at MIT and Khan Academy are also simpler than mine, but have the advantage of being given in video format.

The third distinctive feature is that I help UoL students. It is a huge organization spanning 17 universities and colleges in the UK and with many branches in other parts of the world. The Economics program was developed by the London School of Economics (LSE), one of the world's leading universities.

The problem with LSE courses is that they are very difficult. After the exams, LSE puts out short recommendations on the Internet for solving problems like: here you need to use such and such a theory and such and such an idea. Complete solutions are not given for two reasons: they do not want to help future examinees and sometimes their problems or solutions contain errors (who does not make errors?). But they also delete short recommendations after a year. My site is the only place in the world where there are complete solutions to the most difficult problems of the last few years. It is not for nothing that the solution to one problem noted above attracted 14,000 visits.

Fourthly, my site is unique in terms of the variety of material: statistics, econometrics, algebra, optimization, and finance.

The average number of visits is about 100 per day. When it's time for students to take exams, it jumps to 1-2 thousand. The total amount of materials created in 5 years is equivalent to 5 textbooks. It takes from 2 hours to one day to create one post, depending on the level. After I published this analysis of the site traffic on Facebook, my colleague Nurlan Abiev decided to write posts for the site. I pay for the domain myself, $186 per year. It would be nice to make the site accessible to students and schoolchildren of Kazakhstan, but I don't have time to translate from English.

Once I was looking at the requirements of the MES for approval of electronic textbooks. They want several copies of printouts of all (!) materials and a solid payment for the examination of the site. As a result, all my efforts to create and maintain the site so far have been a personal initiative that does not have any support from the MES and its Committee on Science.

24
Jun 20

Solution to Question 2 from UoL exam 2018, Zone B

Solution to Question 2 from UoL exam 2018, Zone B

There are three companies, called A, B, and C, and each has a 4% chance of going bankrupt. The event that one of the three companies will go bankrupt is independent of the event that any other company will go bankrupt.

Company A has outstanding bonds, and a bond will have a net return of r = 0\% if the corporation does not go bankrupt, but it will have a net return of r = -100\%, i.e., losing everything invested, if it goes bankrupt. Suppose an investor buys $1000 worth of bonds of company A, which we will refer to as portfolio {P_1}.

Suppose also that there exists a security whose payout depends on the bankruptcy of companies B and C in a joint fashion. In particular, if neither B nor C go bankrupt, this derivative will have a net return of r = 0\%. If exactly one of B or C go bankrupt, it will have a net return of r = -50\%, i.e., losing half of the investment. If both B and C go bankrupt, it will have a net return of r = -100\%, i.e., losing the whole investment. Suppose an investor buys $1000 worth of this derivative, which is then called portfolio {P_2}.

(a) Calculate the VaR at the \alpha = 10\% critical level for portfolios P_1 and {P_2}. [30 marks]

Independence of events. Denote A,{A^c} the events that company A goes bankrupt and does not go bankrupt, resp. A similar notation will be used for the other two companies. The simple definition of independence of bankruptcy events P(A \cap B) = P(A)P(B) would be too difficult to apply to prove independence of all events that we need. A general definition of independence of variables is that their sigma-fields are independent (it will not be explained here). This general definition implies that in all cases below we can use multiplicativity of probability such as

P(B \cap C) = P(B)P(C) = {0.04^2} = 0.0016,\,\,P({B^c} \cap {C^c}) = {0.96^2} = 0.9216, P((B \cap {C^c}) \cup ({B^c} \cap C)) = P(B \cap {C^c}) + P({B^c} \cap C) = 2 \times 0.04 \times 0.96 = 0.0768.

The events here have a simple interpretation: the first is that “both B and C fail”, the second is “both B and C fail”, and the third is that “either (B fails and C does not) or (B does not fail and C does)” (they do not intersect and additivity of probability applies).

Let {r_A},{r_S} be returns on A and the security S, resp. From the problem statement it follows that these returns are described by the tables
Table 1

{r_A} Prob
0 0.96
-100 0.04

Table 2

{r_S} Prob
0 0.9216
-50 0.0768
-100 0.0016

Everywhere we will be working with percentages, so the dollar values don’t matter.

From Table 1 we conclude that the distribution function of return on A looks as follows:

Distribution function of portfolio A

Figure 1. Distribution function of portfolio A

At x=-100 the function jumps up by 0.04, at x=0 by another 0.96. The dashed line at y=0.1 is used in the definition of the VaR using the generalized inverse:

VaR_A^{0.1} = \inf \{ {x:{F_A}(x) \ge 0.1}\} = 0.

From Table 2 we see that the distribution function of return on S looks like this:

The first jump is at x=-100, the second at x=-50 and third one at x=0. As above, it follows that

VaR_S^{0.1} = \inf\{ {x:{F_S}(x) \ge 0.1}\} = 0.

(b) Calculate the VaR at the \alpha=10\% critical level for the joint portfolio {P_1} + {P_2}. [20 marks]

To find the return distribution for P_1 + P_2, we have to consider all pairs of events from Tables 1 and 2 using independence.

1.P({r_A}=0,{r_S}=0)=0.96\times 0.9216=0.884736

2.P({r_A}=-100,{r_S}=0)=0.04\times 0.9216=0.036864

3.P({r_A}=0,{r_S}=-50)=0.96\times 0.0768=0.073728

4.P({r_A}=-100,{r_S}=-50)=0.04\times 0.0768=0.003072

5.P({r_A}=0,{r_S}=-100)=0.96\times 0.0016=0.001536

6.P({r_A}=-100,{r_S}=-100)=0.04\times 0.0016=0.000064

Since we deal with a joint portfolio, percentages for separate portfolios should be translated into ones for the whole portfolio. For example, the loss of 100% on one portfolio and 0% on the other means 50% on the joint portfolio (investments are equal). There are two such losses, in lines 2 and 5, so the probabilities should be added. Thus, we obtain the table for the return r on the joint portfolio:

Table 3

r Prob
0 0.884736
-25 0.073728
-50 0.0384
-75 0.003072
-100 0.000064

Here only the first probability exceeds 0.1, so the definition of the generalized inverse gives

VaR_r^{0.1} = \inf \{ {x:{F_r}(x) \ge 0.1}\} = 0.

(c) Is VaR sub-additive in this example? Explain why the absence of sub-additivity may be a concern for risk managers. [20 marks]

To check sub-additivity, we need to pass to positive numbers, as explained in other posts. Zeros remain zeros, the inequality 0 \le 0 + 0 is true, so sub-additivity holds in this example. Lack of sub-additivity is an undesirable property for risk managers, because for them keeping the VaR at low levels for portfolio parts doesn’t mean having low VaR for the whole portfolio.

(d) The expected shortfall E{S^\alpha } at the \alpha critical level can be defined as

ES^\alpha= - E_t[R|R < - VaR_{t + 1}^\alpha],

where R is a return or dollar amount. Calculate the expected shortfall at the \alpha = 10\% critical level for portfolio P_2. Is this risk measure sub-additive? [30 marks]

Using the definition of conditional expectation and Table 3, we have (the time subscript can be omitted because the problem is static)
ES^{0.1}=-E[r|r<VaR_r^{0.1}]=-\frac{Er1_{\{r<VaR_r^{0.1}\}}}{{P(r<VaR_r^{0.1})}}=
=-\frac{-25\times 0.073728-50\times 0.0384-75\times 0.003072-100\times 0.000064}{0.073728+0.0384+0.003072+0.000064}=\frac{4}{0.115264}=34.7029.

There is a theoretical property that the expected shortfall is sub-additive.

22
Jun 20

Solution to Question 2 from UoL exam 2019, zone B

Solution to Question 2 from UoL exam 2019, zone B

Suppose the parameters in a GARCH (1,1) model

\sigma _{t + 1}^2 = \omega + \beta \sigma _t^2 + \alpha \varepsilon _t^2   (1)

are \omega = 0.000004,\ \alpha = 0.06,\ \beta = 0.93, the index t refers to days and {\varepsilon _t} is zero-mean white noise with conditional variance \sigma _t^2.

(a) What are the requirements for this process to be covariance stationary, and are they satisfied here? [20 marks]

If the coefficients satisfy the condition for positivity, \omega>0,\ \alpha,\beta\ge0, then the condition for covariance-stationarity is \alpha + \beta < 1. They are barely satisfied.

(b) What is the long-run average volatility? [20 marks]

We use the facts that {\sigma ^2} = E\sigma _{t + 1}^2 = E\left[ {E(\varepsilon _{t + 1}^2|{F_t})} \right] for all t. Applying the unconditional mean to regression (1) and using the LIE we get

{\sigma ^2}=E\sigma _{t+1}^2=E\left[{\omega+\beta\sigma _t^2+\alpha\varepsilon _t^2}\right]=\omega+\beta{\sigma^2}+\alpha{\sigma^2}

and

{\sigma^2}=\frac{\omega }{{1-\alpha-\beta}}=\frac{{0.000004}}{{1-0.06-0.93}}=0.0004.

(c) If the current volatility is 2.5% per day, what is your estimate of the volatility in 20, 40, and 60 days? [20 marks]

On p.107 of the Guide there is the derivation of the equation

\sigma _{t + h,t}^2 = \sigma _y^2 + {(\alpha + \beta )^{h - 1}}(\sigma _{t + 1,t}^2 - \sigma _y^2),\,\,h \ge 1.    (2)

I gave you a slightly easier derivation in my class, please use that one. If we interpret "current" as t+1 and "in twenty days" as t+1+20, then

\sigma _{t+21}^2=\sigma^2+(\alpha + \beta )^{20}(\sigma _{t+1}^2-\sigma^2) = 0.0004+\exp\left[ 20\ln(0.06+0.93)\right](0.025-0.0004) = 0.020521.

For h=41,61 use the same formula to get 0.016692, 0.013725, resp. I did it in Excel and don't envy you if you have to do it during an exam.

(d) Suppose that there is an event that decreases the current volatility by 1.5%to 1% per day. Estimate the effect on the volatility in 20, 40, and 60 days. [20 marks]

Calculations are the same, just replace 0.025 by 0.01. Alternatively, one can see that the previous values will go down by \exp[(h-1)\ln(0.06+0.93)]0.015, which results in volatility values 0.012146, 0.009934 and 0.008125.

(e) Explain what volatility should be used to price 20-, 40-and 60-day options, and explain how you would calculate the values. [20 marks]

The only unobservable input to the Black-Scholes option pricing formula is the stock price volatility. In the derivation of the formula the volatility is assumed to be constant. The value of the constant should depend on the forecast horizon. If we, say, forecast 20 days ahead, we should use a constant value for all 20 days. This constant can be obtained as an average of daily forecasts obtained from the GARCH model.

If the GARCH is not used, a simpler approach is applied. If the average daily volatility is {\sigma _d}, then assuming independent returns, over a period of n days volatility is {\sigma _{nd}} = \sqrt n {\sigma _d}.

In practice, traders go back from option prices to volatility. That is, they use observed option prices to solve the Black-Scholes formula for volatility (find the root of an equation with the price given). The resulting value is called implied volatility. If it is plugged back into the Black-Scholes formula, the observed option price will result.

21
Jun 20

Solution to Question 3 from UoL exam 2019, zone A

Solution to Question 3 from UoL exam 2019, zone A

(a) Define the concept of trade duration in financial markets and explain briefly why this concept is economically useful. What features do trade durations typically exhibit and how can we model these features? [25 marks]

High frequency traders (HFT) may trade every millisecond. Orders from traders arrive at random moments and therefore the trade times are not evenly spaced. It makes sense to model the differences

{x_j} = TIME_j - TIME_{j - 1}

between transaction times. (The Guide talks about differences between times of returns but I don’t like this because on small time frames people are interested in prices, not returns.) Those differences are called durations. They are economically interesting because 1) they tell us something about liquidity: periods of intense trading are generally periods of greater market liquidity than periods of sparse trading (there is also after-hours trading between 16:00 and 20:30, New York time, when trading may be intense but liquidity is low) and 2) durations relate directly to news arrivals and the adjustment of prices to news, and so have some use in discussions of market efficiency.

The trading session in the USA is from 9:30 to 16:00, New York time. Durations exhibit diurnality (that is, intraday seasonality): transactions are more frequent (durations are shorter) in the first and last hour of the trading session and less frequent around lunch, see Figure 16.6 from the Guide.

Higher frequency in the first hour results from traders rebalancing their portfolios after overnight news and in the last hour – from anticipation of news during the next night.

The main decomposition of durations is

{x_j} = {s_j}x_j^*,
so \log {x_j} = \log {s_j} + \log x_j^*,
\log {s_j} = \sum\limits_{i = 1}^{13} {\beta _j}{D_{i,j}}
\log {s_j} = \gamma _0 + \gamma _1HR{S_j} + \gamma _2HRS_j^2.

In the first equation {s_j} is the diurnal component and x_j^* is called a de-seasonalized duration (it has not been defined here). The second follows from the first.

I am not sure that you need the third equation. The fourth equation is used below. In the third equation \log {s_j} is regressed on dummies of half-hour periods (there are 13 of them in the trading session; the constant is not included to avoid the dummy trap). In the fourth equation it is regressed on the first and second power of the time variable HRS_j, which measures time in hours starting from the previous midnight. This is called a polynomial regression. Both regressions can capture diurnality.

(b) Describe the Engle and Russell (1998) autoregressive conditional duration (ACD) model. [25 marks]

Instead of the duration model considered in part (a) Engle and Russell suggest the ACD model

{x_j} = {s_j}x_j^*,
x_j^* = {\psi _j}{\varepsilon _j},
where \log {s_j} = {\beta _0} + {\gamma _1}HR{S_j} + {\beta _2}HRS_j^2,
x_j^\star = \frac{{x_j}}{{s_j}},\ {\varepsilon _j}|{F_t}\sim i.i.d.(1),
\psi _j = \omega + \beta \psi _{j - 1} + \alpha x_{j - 1}^*,\,\,\omega > 0,\,\,\alpha ,\beta \ge 0   (1)

The first decomposition is the same as above. The second equation decomposes the de-seasonalized duration into a product of deterministic and stochastic components. To understand the idea, we can compare (1) with the GARCH(1,1) model:

y_{t + 1} = {\mu _{t + 1}} + {\varepsilon _{t + 1}},
{\varepsilon _{t + 1}} = {\sigma _{t + 1}}{v_{t + 1}},
{v_{t + 1}}|{F_t}\sim F(0,1),
\sigma _{t + 1}^2 = \omega + \beta \sigma _t^2 + \alpha \varepsilon _t^2.   (2)

Equations (1) and (2) are similar. The assumptions about the random components are different: in (1) we have E{\varepsilon _j} = 1, in (2) E{v_j} = 0. This is because in (2) the epsilons are deviations from the mean and may change sign; in (1) the epsilons come from durations and should be positive. To obtain the last equation in (1) from the GARCH(1,1) in (2) one has to make replacements

\sigma _t^2\sim {\psi _j},\ \varepsilon _t^2 = {({y_{t + 1}} - {\mu _{t + 1}})^2}\sim x_{j - 1}^*. (3)

This is important to know, to understand the comparison of the ML method for the two models below.

(c) Compare the conditions for covariance stationarity, identification and positivity of the duration series for the ACD(1,1) to those for the GARCH(1,1). [25 marks]

Those conditions for GARCH are

Condition 1: \omega > 0,\ \alpha,\beta \ge 0, for positive variance,

Condition 2: \beta = 0 if \alpha = 0, for identification,

Condition 3: \alpha + \beta < 1, for covariance stationarity.

For ACD they are the same, because both are essentially ARMA models.

(d) Illustrate the relationship between the log-likelihood of the ACD(1,1) model and the estimation of a GARCH(1,1) model using the normal likelihood function. [25 marks]

Because of the assumption E{\varepsilon _j} = 1 in (1) we cannot use the normal distribution for (1). Instead the exponential random variable is used. It takes only positive values; its density is zero on the left half-axis and is an exponential function on the right half-axis:

Z\sim Exponential(\gamma ),

so f(z|\gamma ) = \frac{1}{\gamma }\exp\left(-\frac{z}{\gamma } \right) and EZ = \gamma .

Here \gamma is a positive number and f is the density. We take \gamma= 1 as required by the ACD model. This implies Ex_j^* = {\psi _j}E{\varepsilon _j} = {\psi _j} so x_j^* is distributed as Exponential({\psi _j}). Its density is

f(x_j^*|\psi _j)=f(x_j^*|\psi _j(\omega,\beta,\alpha))=\frac{1}{\psi _j}f\left(-\frac{x_j^*}{\psi _j}\right).

The rest is logical: plug \psi_j from the ACD model (1), then take log and then add those logs to obtain the log-likelihood. A. Patton gives the log-likelihood for GARCH, whose derivation I could not find in the book. But from (3) we know that there should be similarity after replacement \sigma _t^2\sim{\psi _j},\ x_{j - 1}^*\sim{({r_t} - {\mu _t})^2}. To this Patton adds that the GARCH likelihood is simply a linear transformation of the ACD likelihood.

20
Apr 20

FN3142 Chapter 14 Risk management and Value-at-Risk: Backtesting

FN3142 Chapter 14 Risk management and Value-at-Risk: Backtesting

Here I added three videos and corresponding pdf files on three topics:

Chapter 14. Part 1. Evaluating VAR forecasts

Chapter 14. Part 2. Conditional coverage tests

Chapter 14. Part 3. Diebold-Mariano test for VaR forecasts

Be sure to watch all of them because sometimes I make errors and correct them in later videos.

All files are here.

Unconditional coverage test

7
Apr 20

FN3142 Chapter 13. Risk management and Value-at-Risk: Models

FN3142 Chapter 13. Risk management and Value-at-Risk: Models

Chapter 13 is divided into 5 parts. For each part, there is a video with the supporting pdf file. Both have been created in Notability using an iPad. All files are here.

Part 1. Distribution function with two examples and generalized inverse function.

Part 2. Value-at-Risk definition

Part 3. Empirical distribution function and its estimation

Part 4. Models based on flexible distributions

Part 5. Semiparametric models, nonparametric estimation of densities and historical simulation.

Besides, in the subchapter named Expected shortfall you can find additional information. It is not in the guide but it was required by one of the past UoL exams.