Ratio Put Write Explained

Best Binary Options Brokers 2020:
  • Binarium
    Binarium

    Top Binary Options Broker 2020!
    Perfect For Beginners and Middle-Leveled Traders!
    Free Education How To Trade!
    Free Demo Account!
    Big Sign-up Bonus!

  • Binomo
    Binomo

    Good Choice For Experienced Traders!

Put Ratio Backspread

DEFINITION of Put Ratio Backspread

A put ratio backspread is an options trading strategy that combines short puts and long puts to create a position whose profit and loss potential depends on the ratio of these puts. A put ratio backspread is so called because it seeks to profit from the volatility of the underlying stock, and combines short and long puts in a certain ratio at the discretion of the options investor. It is constructed to have unlimited potential profit with limited loss, or limited potential profit with the prospect of unlimited loss, depending on how it is structured. The ratio of long to short puts is typically 2:1, 3:2 or 3:1.

BREAKING DOWN Put Ratio Backspread

A put ratio backspread combines short puts and long puts and seeks to profit from the volatility of the underlying stock. For example, a stock trading at $29.50 may have one-month puts trading as follows: $30 puts trading at $1.16 and $29 puts trading at 62 cents. A trader who is bearish on the underlying stock and wishes to structure a put ratio backspread that would profit from a decline in the stock, could buy two $29 put contracts for a total cost of $124 and sell short a $30 put contract to receive the $116 premium. (Remember that each option contract represents 100 shares.) The net cost of this 2:1 put ratio backspread, without taking commissions into account, is, therefore, $8.

If the stock declines to $28 at expiration, the trade breaks even (leaving aside the marginal $8 cost of putting on the trade.) If the stock falls to $27 at option expiry, the gross gain is $100; at $26, the gross gain is $200 and so on.

If, on the other hand, the stock appreciates to $30 by option expiry, the maximum loss is restricted to the cost of the trade or $8. The loss is restricted to $8 regardless of how high the stock trades by option expiry.

Put-Write ETFs as Alternative Income Options

In a previous post, we covered buy-write strategies and related ETFs, which are currently offering very attractive yield levels. Today, we’ll look a bit further into the buy-write’s cousin, the put-write.

What is a Put-Write Strategy?

Like buy-writes, put-writes also earn income by writing options to collect premiums with the hope that the options will expire worthless. Unlike a buy-write, where the call is covered by the stock, put-writes either write naked puts or must maintain a cash account to cover the payout if the put is exercised.

Relationship to Buy-Writes

There is a fundamental relationship between put-writes and buy-writes that can be seen by rearranging put-call parity:

where P, C, and S are the put, call, and stock prices, respectively, K is the strike price, and D is the discount factor to maturity. This assumes no dividends and European options, but the intuition is still helpful.

The left-hand side of the equation is the put-write and cash invested to cover the strike price at maturity (long a zero-coupon bond); the right-hand side is the buy-write written at the same strike. From this we can equate an out-of-the-money (OTM) buy-write strategy to a cash-secured in-the-money (ITM) put-write. Likewise, we could say that a cash-secured OTM put-write is equivalent to an ITM buy-write.

In reality, the relationship is more of a similarity than an equivalence. Puts and calls are often American options, which can be exercised before maturity. So even though writing a covered call (or cash secured put) that is ITM may have similar mechanics to writing an OTM covered call (or cash secured put), the OTM option is generally preferable to reduce the risk of early exercise.

Put-Write ETFs

…or more appropriately, ETF. Currently, the only ETF available for put-write strategies is the U.S. Equity High Volatility Put Write Index Fund ETF (HVPW). It has $52 million in AUM and an expense ratio of 95 bps. Every two months, it sells 15% OTM cash-secured puts on 20 of the largest stocks with the highest implied volatility. The idea is that investors will pay a premium for insurance on these stocks, allowing the fund’s management to target a 9% annual distribution, a goal that the ETF has been able to achieve since its inception in Feb. 2020.

Many of HVPW’s recent puts have been sold on the large U.S. airlines and other travel related stocks and companies in technology, consumer discretionary, and healthcare. From period to period, about seven of the companies remain in the portfolio. When a put is exercised early, the ETF holds the stock until the next turnover date. This opens the fund up to risk of further declines in the stock price prior to the sale, but, from a quantitative perspective, the stock could also mean revert and generate a positive return for the fund. HVPW uses liquidity constraints to reduce the risks associated with liquidating the stock position on the rebalance day.

Likelihood of a Payout

Put-write strategies rely on the puts expiring OTM. Thus, they have significant downside potential in a bear market. However, the hope is that the premium can offset losses even a few puts are exercised.

The following chart graphs the probability of a single 15% OTM put option expiring ITM (with Black-Scholes assumptions) for a range of volatilities. It also shows the annual yield that the option would generate assuming it always expires worthless. The calculations assume a 10 bps risk-free rate and 2 months to expiry.

From the chart, we can see that a volatility of at least 43% is required in order to generate 9% annually in premiums. At this volatility the probability of the put expiring ITM is 17%.

A Portfolio of Puts

Of course, using a single option would not be prudent risk management. Assuming this is done independently six times in a year on options with a 17% chance of expiring ITM, the probability of never having a payout is only 33%, and that only accounts for if it will lose money, not how much.

Thankfully, diversification can help. HVPW writes options on 20 stocks. The average of the trailing 100-day volatility of the current holdings is 44%, and the average probability that the options expire in the money is 17%. Assuming independence, there is a 98% probability that at least one of the options will expire in the money in each two month period. Indeed, since HVPW’s index went live in Feb. 2020, an average of 3-4 options have expired in-the-money in each period, with a maximum of 10 in Feb-Apr 2020 and a minimum of 0 in Jun-Aug 2020.

A few options ending slightly in the money will not break the bank, but a strong, highly correlated bear market could be detrimental. The hope is that, when a stock is declining, the implied volatility of its options is higher than the realized volatility so that HVPW can sell over-priced options. These outsized premiums are intended to mitigate payouts for the exercised puts. HVPW may write puts on the most volatile stocks, but its limited upside potential and offsetting premiums on the downside should reduce its volatility relative to the underlyings.

Our View

As with buy-write strategies, put write strategies comes with unique risks that many investors may not be fully aware of. Option payoffs are inherently nonlinear, and understanding the behavior of the options in different market environments is a key to understanding the strategy as a whole.

Aside from the general mechanics of the put-write strategy, there are many parameters that investors should know before investing: the strike price of the options, how diversified the fund is, what happens to the stock if the put is exercised early, and what types of assets the puts are being written on. All of these factors can affect the yield of the strategy and the total return.

Put-write strategies can be a great way to increase portfolio income, especially in a low and rising rate environment (stay tuned for more on this in a future post), but during a strong bear market, they can be exposed. Specifically, for HVPW, currently the only put-write ETF, as the volatility of the underlying securities increases, more puts will have a higher probability of expiring in the money (or being exercised early since they are American puts). However, during sideways and bull markets, the high volatility of the underlying securities should be beneficial since the strategy can capitalize on inflated option premiums while benefiting from fewer put payouts. As with buy-writes, ultimately, put-write strategies may be a good source of income diversification as long as one is aware of their highly path-dependent nature and how the strategy is specifically structured.

Note: Newfound does not currently utilize any put-write ETFs in its strategies but may choose to do so in the future. Newfound encourages investors to seek the advice of a financial advisor as the appropriateness of a particular investment or strategy will depend on an investor’s individual circumstances and objectives.

If you are interested in learning more about other income generating asset classes along with a discussion on how they may perform in a rising rate environment, check out our previous posts on Bank Loans, MLPs, Convertibles, and Preferreds.

Nathan Faber

Nathan is a Portfolio Manager at Newfound Research, a quantitative asset manager offering a suite of separately managed accounts and mutual funds. At Newfound, Nathan is responsible for investment research, strategy development, and supporting the portfolio management team. Prior to joining Newfound, he was a chemical engineer at URS, a global engineering firm in the oil, natural gas, and biofuels industry where he was responsible for process simulation development, project economic analysis, and the creation of in-house software. Nathan holds a Master of Science in Computational Finance from Carnegie Mellon University and graduated summa cum laude from Case Western Reserve University with a Bachelor of Science in Chemical Engineering and a minor in Mathematics.

Оптимизация запросов. Основы EXPLAIN в PostgreSQL (часть 2)

Предыдущие части:

Что происходит на физическом уровне при выполнениии нашего запроса? Разберёмся. Мой сервер поднят на Ubuntu 13.10. Используются дисковые кэши уровня ОС.
Останавливаю PostgreSQL, принудительно фиксирую изменения в файловой системе, очищаю кэши, запускаю PostgreSQL:

Теперь кэши очищены, пробуем выполнить запрос с опцией BUFFERS

QUERY PLAN
— Seq Scan on foo (cost=0.00..18334.10 rows=1000010 width=37) (actual time=0.525..734.754 rows=1000010 loops=1)
Buffers: shared read=8334
Total runtime: 1253.177 ms
(3 rows)

Таблица считывается частями — блоками. Кэш пуст. Таблица полностью считывается с диска. Для этого пришлось считать 8334 блока.
Buffers: shared read — количество блоков, считанное с диска.

Повторим последний запрос

QUERY PLAN
— Seq Scan on foo (cost=0.00..18334.10 rows=1000010 width=37) (actual time=0.173..693.000 rows=1000010 loops=1)
Buffers: shared hit=32 read=8302
Total runtime: 1208.433 ms
(3 rows)

Buffers: shared hit — количество блоков, считанных из кэша PostgreSQL.
Если повторите этот запрос несколько раз, то увидите, как PostgreSQL с каждым разом всё больше данных берёт из кэша. С каждым запросом PostgreSQL наполняет свой кэш.
Операции чтения из кэша быстрее, чем операции чтения с диска. Можете заметить эту тенденцию, отслеживая значение Total runtime .
Объём кэша определяется константой shared_buffers в файле postgresql.conf .

WHERE

Добавим в запрос условие

QUERY PLAN
— Seq Scan on foo (cost=0.00..20834.12 rows=999522 width=37)
Filter: (c1 > 500)
(2 rows)

Индексов у таблицы нет. При выполнении запроса последовательно считывается каждая запись таблицы ( Seq Scan ). Каждая запись сравнивается с условием c1 > 500 . Если условие выполняется, запись вводится в результат. Иначе — отбрасывается. Filter означает именно такое поведение.
Значение cost , что логично, увеличилось.
Ожидаемое количество строк результата — rows — уменьшилось.
В оригинале даются объяснения, почему cost принимает именно такое значение, а также каким образом рассчитывается ожидаемое количество строк.

Пора создать индексы.

QUERY PLAN
— Seq Scan on foo (cost=0.00..20834.12 rows=999519 width=37)
Filter: (c1 > 500)
(2 rows)

Ожидаемое количество строк изменилось. Уточнилось. В остальном ничего нового. Что же с индексом?

QUERY PLAN
— Seq Scan on foo (cost=0.00..20834.12 rows=999519 width=37) (actual time=0.572..848.895 rows=999500 loops=1)
Filter: (c1 > 500)
Rows Removed by Filter: 510
Total runtime: 1330.788 ms
(4 rows)

Отфильтровано только 510 строк из более чем миллиона. Пришлось считать более 99,9% таблицы.

Принудительно заставим использовать индекс, запретив Seq Scan:

QUERY PLAN
— Index Scan using foo_c1_idx on foo (cost=0.42..34623.01 rows=999519 width=37) (actual time=0.178..1018.045 rows=999500 loops=1)
Index Cond: (c1 > 500)
Total runtime: 1434.429 ms
(3 rows)

Index Scan , Index Cond вместо Filter — используется индекс foo_c1_idx .
При выборке практически всей таблицы использование индекса только увеличивает cost и время выполнения запроса. Планировщик не глуп!

Не забываем отменить запрет на использование Seq Scan:

QUERY PLAN
— Index Scan using foo_c1_idx on foo (cost=0.42..25.78 rows=491 width=37)
Index Cond: (c1 foo_c1_idx для условия c1 . Для c2

‘abcd%’::text используется фильтр.
Обратите внимание, что в выводе результатов используется POSIX формат оператора LIKE.

Если в условии только текстовое поле:

QUERY PLAN
— Seq Scan on foo (cost=0.00..20834.12 rows=100 width=37) (actual time=14.497..412.030 rows=10 loops=1)
Filter: (c2

‘abcd%’::text)
Rows Removed by Filter: 1000000
Total runtime: 412.120 ms
(4 rows)

Ожидаемо, Seq Scan .

Строим индекс по c2 :

QUERY PLAN
— Seq Scan on foo (cost=0.00..20834.12 rows=100 width=37) (actual time=20.992..424.946 rows=10 loops=1)
Filter: (c2

‘abcd%’::text)
Rows Removed by Filter: 1000000
Total runtime: 425.039 ms
(4 rows)

Опять Seq Scan ? Индекс не используется потому, что база у меня для текстовых полей использует формат UTF-8.
При создании индекса в таких случаях надо использовать класс оператора text_pattern_ops :

QUERY PLAN
— Bitmap Heap Scan on foo (cost=4.58..55.20 rows=100 width=37)
Filter: (c2

‘abcd%’::text)
-> Bitmap Index Scan on foo_c2_idx1 (cost=0.00..4.55 rows=13 width=0)
Index Cond: ((c2

‘abcd’::text) AND (c2

Bitmap Index Scan — используется индекс foo_c2_idx1 для определения нужных нам записей, а затем PostgreSQL лезет в саму таблицу: ( Bitmap Heap Scan ) -, чтобы убедиться, что эти записи на самом деле существуют. Такое поведение связано с версионностью PostgreSQL.

Если выбирать не всю строку, а только поле, по которому построен индекс

QUERY PLAN
— Index Only Scan using foo_c1_idx on foo (cost=0.42..25.78 rows=491 width=4)
Index Cond: (c1 Index Only Scan выполняется быстрее, чем Index Scan за счёт того, что не требуется читать строку таблицы полностью: width=4 .

Резюме

  • Seq Scan — читается вся таблица.
  • Index Scan — используется индекс для условий WHERE, читает таблицу при отборе строк.
  • Bitmap Index Scan — сначала Index Scan, затем контроль выборки по таблице. Эффективно для большого количества строк.
  • Index Only Scan — самый быстрый. Читается только индекс.
Best Binary Options Brokers 2020:
  • Binarium
    Binarium

    Top Binary Options Broker 2020!
    Perfect For Beginners and Middle-Leveled Traders!
    Free Education How To Trade!
    Free Demo Account!
    Big Sign-up Bonus!

  • Binomo
    Binomo

    Good Choice For Experienced Traders!

Like this post? Please share to your friends:
Binary Options Trading, Strategies and Robots
Leave a Reply

;-) :| :x :twisted: :smile: :shock: :sad: :roll: :razz: :oops: :o :mrgreen: :lol: :idea: :grin: :evil: :cry: :cool: :arrow: :???: :?: :!: