PD007100IN-SC
FORM 2
THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENTS RULES, 2003
COMPLETE SPECIFICATION
(See section 10, rule 13)
1. Title of the invention: GLOBALLY OPTIMUM TRADING POSITIONS IN RISKNEUTRAL
MEASURE
2. Applicant(s)
NAME NATIONALITY ADDRESS
TATA CONSULTANCY
SERVICES LIMITED
Indian Nirmal Building, 9th Floor,
Nariman Point, Mumbai,
Maharashtra 400021, India
3. Preamble to the description
COMPLETE SPECIFICATION
The following specification particularly describes the invention and the manner in which it
is to be performed.
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2
TECHNICAL FIELD
[0001] The present subject matter relates, in general, to a path-independent European
Contingent Claim and, in particular, to a system and a computer-implemented method for
evaluating globally optimum trading positions for the path-independent European Contingent
Claim.
BACKGROUND
[0002] In today’s competitive business environment, investment banks make profit by
trading financial instruments, such as derivatives. A derivative is a contract between two parties,
namely, a buyer and a seller. The seller of the contract is obligated to deliver to the buyer, a
payoff that is contingent upon the performance of an underlying asset. In one example, a
derivative may be an option written on the underlying asset. The underlying asset may be a
stock, a currency, or a commodity. In some derivatives, payoffs have to be delivered at a fixed
time to maturity. Such derivatives are in general known as European Contingent Claims (ECC).
Examples of ECC include a European call or put option. The payoff of a European call option
may be mathematically denoted by H = max [0, ST - K], wherein (H) represents the payoff of the
European call option, (K) represents strike price and (ST ) represents the price of the underlying
asset at the time of maturity of the European call option. Further, the ECC may be a pathindependent
option, which means its payoff depends only on the price of the underlying asset at
the time of maturity.
[0003] Selling or buying an option always implies some exposure to financial risk. In
case of the European call option, the holder of an option pays a premium to buy the underlying
asset at a strike price at the time of maturity of the option. The strike price is the contracted price
at which the underlying asset can be purchased or sold at the time of maturity of the option. If the
market price of the underlying asset exceeds the strike price, it is profitable for the holder of the
option to buy the underlying asset from the option seller, and then sell the underlying asset at the
market price to make a profit. Since the European call option provides to its buyer the right, but
not the obligation to buy, the buyer may thus have a chance to make a potentially infinite profit
at the cost of losing the amount which he has paid for the option, i.e., the premium. The seller, on
the other hand, has an obligation to sell the underlying asset to the holder at the strike price,
which may be less than the market price of the underlying asset on the date of maturity of the
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option. Therefore, for an option seller the amount at risk is potentially infinite due to the
uncertain nature of the price of the underlying asset. Thus, option sellers typically use various
hedging strategies to minimize such risk.
SUMMARY
[0004] This summary is provided to introduce concepts related to evaluating globally
optimum trading positions in a risk-neutral measure. These concepts are further described below
in the detailed description. This summary is not intended to identify essential features of the
claimed subject matter nor is it intended for use in determining or limiting the scope of the
claimed subject matter.
[0005] A trading position evaluation system for evaluating globally optimum trading
positions in a risk-neutral measure includes an option price determination module configured to
determine a current option price and a shifted option price of an underlying asset of a European
Contingent Claim (ECC) at a trading time instance amongst a plurality of trading time instances
obtained from a trader, based on ECC data and market data. The ECC data comprises data
associated with the ECC and the underlying asset of the ECC, and the market data comprises
annualized volatility of the underlying asset and risk-free interest rate of the market. Based on
the current option price and the shifted option price, a position evaluation module evaluates a
trading position at the trading time instance that minimizes global variance of profit and loss to
the trader.
BRIEF DESCRIPTION OF THE DRAWINGS
[0006] The detailed description is described with reference to the accompanying figure(s).
In the figure(s), the left-most digit(s) of a reference number identifies the figure in which the
reference number first appears. The same numbers are used throughout the figure(s) to reference
like features and components. Some embodiments of systems and/or methods in accordance with
embodiments of the present subject matter are now described, by way of example only, and with
reference to the accompanying figure(s), in which:
[0007] Fig. 1 illustrates a network environment implementing a trading position
evaluation system, according to an embodiment of the present subject matter.
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[0008] Fig. 2a illustrates components of the trading position evaluation system, according
to an embodiment of the present subject matter.
[0009] Figs. 2b-2f illustrate an exemplary data set for evaluating trading positions, and
graphical representations depicting comparison of a global variance of profit and loss obtained
by the present trading position evaluation system and a conventional system.
[0010] Fig. 3 illustrates a method for evaluating trading positions that are globally
optimum in a risk-neutral measure, according to an embodiment of the present subject matter.
DETAILED DESCRIPTION
[0011] The trading of financial instruments, such as a path-independent ECC and other
derivatives over computer networks, such as the Internet has become a common activity.
Generally, any form of market trading involves a risk and so does the ECC trading. The risk to
an ECC buyer is limited to premium he has paid to an ECC seller. However, the risk to the ECC
seller is potentially unlimited, while the profit earned by the ECC seller from the ECC sale alone
is limited to the premiums earned. Accordingly, the ECC seller may hedge his risk by trading an
asset underlying the ECC. Such an asset is hereinafter referred as underlying asset. The trading
decisions taken by the ECC seller constitute the seller’s hedging strategy. The net profit/loss
incurred by the ECC seller at the time of maturity from selling the ECC and the hedging process
is called as the hedging error. The hedging error represents the ECC seller’s risk that the ECC
seller may incur even after hedging. A judicious choice of a hedging strategy by the ECC seller
may lead to a lower residual risk.
[0012] Conventional hedging techniques are often postulated on unrealistic assumptions
that trades can be made continuously in time. When such techniques are used in realistic settings
involving multiple discrete trading time instances, they fail to provide trading positions that are
globally optimum, i.e., the trading positions that minimizes overall risk to a trader, for example
the ECC seller in this case, at the time of maturity.
[0013] The present subject matter describes a system and a computer-implemented
method for evaluating trading positions for a path-independent ECC. Such trading positions are
evaluated at a plurality of discrete time instances starting from the time of initiation of the ECC
till the time of maturity. Such trading positions provide minimum global variance of profit/loss
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to a trader, say, an ECC seller. The term global variance may be understood as variance of
overall profit and loss to the trader starting from the time of initiation of the ECC till the time of
maturity.
[0014] The calculation of variance requires a choice of probability measure. A
probability measure provides the probability of occurrence of different financial events, and
represents the quantification of a subjective view of the relative likelihoods of various future
events/scenarios. Each market player may use a different probability measure reflecting his or
her own subjective views. The collective subjective perception of all the market players is
captured by the so-called market probability measure. Owing to the large number of market
players and constantly changing subjective views, it is very difficult to characterize the market
probability measure. An alternative is the risk-neutral probability measure (referred to as simply
a risk-neutral measure hereinafter), which is conveniently characterized by the property that the
expected rate of return of any market asset in the risk-neutral measure equals the risk-free
interest rate offered by the economy. Moreover, as per the theory of asset pricing, the risk-neutral
measure determines the prices of all derivative assets in the market.
[0015] The system and method, in accordance with the present subject matter, involves
evaluating trading positions. The trading positions evaluated by the present system and method
minimize the global variance of the profit and loss to a trader in the risk-neutral measure. The
system as described herein is a trading position evaluation system.
[0016] Initially, a database for storing data associated with the path-independent ECC is
maintained according to one implementation. The database can be an external repository
associated with the trading position evaluation system, or an internal repository within the
trading position evaluation system. In the description hereinafter, a path-independent ECC is
referred to as ECC, and the data associated with the path-independent ECC is referred to as ECC
data. The ECC data may include the path-independent ECC defined by its payoff, time of
initiation, time to maturity, premium, price of the underlying asset of the path-independent ECC
at the time of initiation which is known as spot price, strike price of the path-independent ECC,
and current market prices of call and put options. In one example, the ECC data stored in the
database may be obtained from the users, such as traders.
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[0017] In the above mentioned implementation, the database is further populated with
historical data including historical market prices of the underlying asset of the ECC. The
historical market prices for the underlying asset can be automatically obtained from a data
source, such as National Stock Exchange (NSE) website at regular time intervals, for example, at
the end of the day and stored into the database. The data stored in the database may be retrieved
whenever the trading positions are to be evaluated. Further, the data contained within such
database may be updated, whenever required. For example, new data may be added into the
database, existing data can be modified, or non-useful data may be deleted from the database.
[0018] In one implementation, the volatility of the underlying asset is computed based on
the historical data associated with the underlying asset. To compute the volatility, historical
market prices of the underlying asset for a predefined period, say, past two years, are retrieved
from the database and log-returns are computed for the underlying asset based on the retrieved
historical market prices. Thereafter, log-returns are fitted to a best-fit distribution to generate a
plurality of scenarios. The best-fit distribution may be a Normal distribution, a Poisson
distribution, a T-distribution, or any other known distribution that fits best to the log-returns. The
scenarios, thus, generated may include already existing scenarios that has occurred in the past
and other scenarios that have not existed in the past but may have a likelihood of occurring in the
future. The scenarios, thus, generated are fitted to a normal distribution to compute the volatility
of the underlying asset. The computed volatility is thereafter annualized.
[0019] Further, a risk-free interest rate of the market is computed based upon the
retrieved ECC data. The computed annualized volatility and the risk-free interest rate are stored
into the database as market data. The database, thus, contains the ECC data, the historical data,
and the market data. The data contained in the database can be retrieved by the trading position
evaluation system for the purpose of evaluating trading positions. In one implementation, the
market data, such as annualized volatility and risk-free interest rate can also be computed in realtime
during evaluation of the trading position. The manner in which evaluation of trading
position takes place is described henceforth.
[0020] A trader may provide a plurality of trading time instances starting from the time
of initiation till the time of maturity of the ECC as an input to the trading position evaluation
system for trading of an underlying asset. Such trading time instances are the discrete time
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instances at which the trader would like to trade the underlying asset of the ECC. Upon receiving
trader’s input, such as trading time instances, the trading position evaluation system retrieves the
ECC data and the market data associated with the underlying asset from the database. For each
of the trading time instances specified by the trader, the trading position evaluation system then
evaluates a trading position that provides minimum global variance of profit and loss to the
trader.
[0021] To evaluate the trading position at a particular trading time instance, the trading
position evaluation system determines a current option price and a shifted option price of the
underlying asset based on the retrieved ECC data and the market data. Such a determination of
the current option price and the shifted option price, in one implementation, may take place using
a Black-Scholes pricing method or a Monte-Carlo pricing method. Subsequently, the trading
position in the underlying asset is evaluated based on the determined current option price and the
shifted option price. The trading position conveys to the trader of the ECC, the number of units
of the underlying asset to be held by the trader of the ECC at a particular trading time instance
until the next trading time instance.
[0022] Thus, the trading position evaluated at each of the specified trading time instances
starting from the time of initiation of the ECC till the time to maturity when taken together
allows the trader to achieve minimum variance of overall profit and loss to the trader, such as an
ECC seller, at the time of maturity. As mentioned previously, such a variance of overall profit
and loss from the time of initiation till the time of maturity is known as global variance. Thus,
minimum global variance of profit and loss can be achieved by evaluating the trading positions
at different trading time instances. Therefore, a possibility of risk incurred by the trader,
especially, the ECC seller, at the time of maturity is minimized. The ECC seller, for example,
may liquidate the underlying asset at the time of maturity in order to deliver the payoff to the
ECC buyer at a minimum risk.
[0023] In the present subject matter, the trading positions are evaluated by using a simple
analytical closed-form expression, which is provided in the later section. The evaluated trading
positions efficiently minimize risk exposure to the traders. Based on the trading positions, a
trader would know how many units of the underlying asset should be held at each trading time
instance so that the overall risk exposure to the trader at the time of maturity is minimized.
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[0024] The following disclosure describes system and method of evaluating the trading
positions that are globally optimum in the risk-neutral measure .While aspects of the described
system and method can be implemented in any number of different computing systems,
environments, and/or configurations, embodiments for the information extraction system are
described in the context of the following exemplary system(s) and method(s).
[0025] Fig. 1 illustrates a network environment 100 implementing a trading position
evaluation system 102, in accordance with an embodiment of the present subject matter. In one
implementation, the network environment 100 can be a public network environment, including
thousands of personal computers, laptops, various servers, such as blade servers, and other
computing devices. In another implementation, the network environment 100 can be a private
network environment with a limited number of computing devices, such as personal computers,
servers, laptops, and/or communication devices, such as mobile phones and smart phones.
[0026] The trading position evaluation system 102 is communicatively connected to a
plurality of user devices 104-1, 104-2, 104-3...104-N, collectively referred to as user devices 104
and individually referred to as a user device 104, through a network 106. In one implementation,
a plurality of users, such as traders may use the user devices 104 to communicate with the
trading position evaluation system 102.
[0027] The trading position evaluation system 102 and the user devices 104 may be
implemented in a variety of computing devices, including, servers, a desktop personal computer,
a notebook or portable computer, a workstation, a mainframe computer, a laptop and/or
communication device, such as mobile phones and smart phones. Further, in one
implementation, the trading position evaluation system 102 may be a distributed or centralized
network system in which different computing devices may host one or more of the hardware or
software components of the trading position evaluation system 102.
[0028] The trading position evaluation system 102 may be connected to the user devices
104 over the network 106 through one or more communication links. The communication links
between the trading position evaluation system 102 and the user devices 104 are enabled through
a desired form of communication, for example, via dial-up modem connections, cable links,
digital subscriber lines (DSL), wireless, or satellite links, or any other suitable form of
communication.
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[0029] The network 106 may be a wireless network, a wired network, or a combination
thereof. The network 106 can also be an individual network or a collection of many such
individual networks, interconnected with each other and functioning as a single large network,
e.g., the Internet or an intranet. The network 106 can be implemented as one of the different
types of networks, such as intranet, local area network (LAN), wide area network (WAN), the
internet, and such. The network 106 may either be a dedicated network or a shared network,
which represents an association of the different types of networks that use a variety of protocols,
for example, Hypertext Transfer Protocol (HTTP), Transmission Control Protocol/Internet
Protocol (TCP/IP), etc., to communicate with each other. Further, the network 106 may include
network devices, such as network switches, hubs, routers, for providing a link between the
trading position evaluation system 102 and the user devices 104. The network devices within the
network 106 may interact with the trading position evaluation system 102, and the user devices
104 through the communication links.
[0030] The network environment 100 further comprises a database 108 communicatively
coupled to the trading position evaluation system 102. The database 108 may store all data
inclusive of data associated with an ECC and its underlying asset sold by a trader,
interchangeably referred to as an ECC seller in the present description. For example, the database
108 may store an ECC data 110, a historical data 112, and a market data 114. As indicated
previously, the ECC data 110 include, but is not limited to, a path-independent ECC defined by
its payoff, time of initiation, time to maturity, premium, spot price of the underlying asset of the
ECC, strike price of the ECC, and current market prices of call and put options. The historical
data 112 includes historical market prices of an underlying asset of the ECC, and the market data
114 includes annualized volatility and risk-free interest rate.
[0031] Although the database 108 is shown external to the trading position evaluation
system 102, it will be appreciated by a person skilled in the art that the database 108 can also be
implemented internal to the trading position evaluation system 102, wherein the ECC data 110,
the historical data 112, and the market data 114 may be stored within a memory component of
the trading position evaluation system 102.
[0032] According to an implementation of the present subject matter, the trading position
evaluation system 102 includes a position evaluation module 116 that retrieves the ECC data 110
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and the market data 114 from the database 108 and evaluates trading positions in the underlying
asset at a plurality of trading time instances. The trading positions evaluated by the trading
position evaluation system 102 are globally optimum in the risk-neutral measure. Such trading
positions are interchangeably referred to as globally optimum trading positions. The trading
position is indicative of the number of units of the underlying asset to be held by the seller of the
ECC from a particular trading time instance until the next trading time instance. Such trading
position minimizes overall risk to the seller starting from the time of initiation till the time of
maturity of the ECC. The manner in which the trading position evaluation system 102 evaluates
the trading positions is explained in greater detail according to the Fig. 2a.
[0033] Fig. 2a illustrates various components of the trading position evaluation system
102, according to an embodiment of the present subject matter.
[0034] In said embodiment, the trading position evaluation system 102 includes one or
more processor(s) 202, a memory 206 coupled to the processor(s) 202, and interface(s) 204. The
processor(s) 202 may be implemented as one or more microprocessors, microcomputers,
microcontrollers, digital signal processors, central processing units, state machines, logic
circuitries, and/or any devices that manipulate signals based on operational instructions. Among
other capabilities, the processor(s) 202 are configured to fetch and execute computer-readable
instructions and data stored in the memory 206.
[0035] The interface(s) 204 may include a variety of software and hardware interfaces,
for example, the interface(s) 204 may enable the trading position evaluation system 102 to
communicate over the network 106, and may include one or more interface for peripheral
device(s), such as a keyboard, a mouse, an external memory, a printer, etc. Further, the
interface(s) 204 may include ports for connecting the trading position evaluation system 102
with other computing devices, such as web servers and external databases. The interface(s) 204
may facilitate multiple communications within a wide variety of protocols and networks, such as
a network, including wired networks, e.g., LAN, cable, etc., and wireless networks, e.g., WLAN,
satellite, etc.
[0036] The memory 206 may include any computer-readable medium known in the art
including, for example, volatile memory, such as static random access memory (SRAM) and
dynamic random access memory (DRAM), and/or non-volatile memory, such as read only
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memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and
magnetic tapes. The memory 206 also includes modules 208 and data 210. The module(s) 208
include routines, programs, objects, components, data structures, etc., which perform particular
tasks or implement particular abstract data types. The module(s) 208 further include, in addition
to the position evaluation module 116, a volatility computation module 212, an interest rate
calculation module 214, an option price determination module 216, and other module(s) 218.
[0037] The data 210 serves, amongst other things, as a repository for storing data
processed, received and generated by one or more of the modules 208. The data 210 includes the
ECC data 110, the historical data 112, and the market data 114, parameter data 224, and other
data 226. The ECC data 110 contains an ECC defined by its payoff, time of initiation, time to
maturity of the ECC, its premium, spot price, strike price, and current market price of the call
and put options. The historical data 112 includes historical market prices of an underlying asset
of the ECC. The market data 114 includes annualized volatility and risk-free interest rate. The
parameter data 224 includes current option price and shifted option price. The other data 226
includes data generated as a result of the execution of one or more other modules 218.
[0038] In the present embodiment, the ECC data 110, the historical data 112, and the
market data 114 are depicted to be stored within the data 210, which is a repository internal to
the trading position evaluation system 102. However, as described in the previous embodiment,
the ECC data 110, the historical data 112, and the market data 114 may also be stored in the
database 108 that is external to the trading position evaluation system 102.
[0039] According to the present subject matter, the volatility computation module 212
retrieves historical data 112 for a predefined period, for example, past one year, from the data
210. As described previously, the historical data 112 includes historical market prices of the
underlying asset. Based on the retrieved historical data 112, the volatility computation module
212 computes log-returns of the underlying asset. In one implementation, volatility computation
module 212 computes the log-returns using the equation (1) provided below:

,
1 ,…, 1 .… (1)
wherein, represents a log-return of the underlying asset for th period,
represents the historical market price of the underlying asset for th period, and
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represents a part of the historical data 112.
[0040] Subsequent to computing the log-returns, the volatility computation module 212
is configured to fit the log-returns to a best-fit distribution. The best-fit distribution may be a
Normal distribution, a Poisson distribution, a T-distribution, or any other known distribution that
fits best to the log-returns, to generate a plurality of scenarios. The volatility computation module
212 then fits the generated scenarios to a normal distribution to compute volatility of the
underlying asset. The computed volatility is thereafter annualized. Further, the interest rate
calculation module 214 of the trading position evaluation system 102 is configured to retrieve the
ECC data 110 and compute the risk-free interest rate of the market based on the retrieved ECC
data 110. According to one implementation, the interest rate calculation module 214 computes
the risk-free interest rate using the equation (2) provided below:


.… (2)
wherein, represents the risk-free interest rate,
represents the strike price of the ECC,
T represents the time to maturity,
! and " represent the current market prices of call and put options, and
# represents the spot price of the underlying asset of the ECC.
[0041] The annualized volatility (σ) and risk-free interest rate (r) are stored as the market
data 114 and can be retrieved by the trading position evaluation system 102 while evaluating the
trading positions. Alternatively, the annualized volatility (σ) and risk-free interest rate (r) may be
computed in real-time during evaluation of the trading positions. The manner in which the
trading position evaluation system 102 evaluates the trading positions is described henceforth.
[0042] The trading position evaluation system 102 receives a plurality of trading time
instances from a trader starting from the time of initialization till the time to maturity of the ECC.
The trading time instances are the time instances at which the trader would like to trade. In the
context of the present subject matter, the trading time instances are mathematically represented
by the expression (3).
$#, $,…. . , $& .… (3)
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[0043] In the above equation, ($#) represents the first trading time instance, which is also
referred to as time of initiation, and ($&), represents last trading time instance, which is also
referred to as time of maturity.
[0044] At each of the trading time instances, the option price determination module 216
determines a current option price and a shifted option price of the underlying asset based on the
ECC data 110 and the market data 114. In one example, the current option price and the shifted
option price may be determined using a Black-Scholes pricing method or a Monte-Carlo pricing
method. In one implementation for a European call option, the option price determination
module 216 determines the current option price using the equations (4), (5), and (6) provided
below.
'($) , )* )+(,* -.(/01*+(,2* , 3
1,…. . , …. (4)
wherein, , 4&5601
7 89.:;
; <(/01*
=>(/01* , 3
1,…. . , .… (5)
,2 4&5601
7 89.:;
; <(/01*
=>(/01* , 3
1,…. . , .… (6)
wherein, $& and $) represents trading time instances,
) represents the price of underlying asset at $),
? represents the annualized volatility of the underlying asset,
represents the risk-free interest rate,
represents the strike price, and
+(,* and +(,2* represents cumulative distribution function of intermediate terms ,
and ,2 .
[0045] In said implementation, the option price determination module 216 determines the
shifted option price of the underlying asset using the equation (7).
'@$), -=;A0)B -=;A0)+(,* -.(/01*+(,2* , 3
1 ,…. , .... (7)
wherein , and ,2 are calculated using the equations (5) and (6) provided above with )
replaced by -=;A0).
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[0046] The current option price and the shifted option price computed by the option price
determination module 216 may be stored as the parameter data 224 within the trading position
evaluation system 102.
[0047] Based on the current option price and the shifted option price, the position
evaluation module 116 of the trading position evaluation system 102 is configured to evaluate a
trading position at each trading time instance. The trading positions, thus, evaluated are globally
optimum in the risk-neutral measure. As indicated earlier, the trading positions conveys to the
trader, the number of units of the underlying asset to be held until the next trading time instance.
Thus, the trading positions evaluated at each of the trading time instances starting from the time
of initialization of the ECC till the time to maturity when taken together allows the seller to
achieve minimum global variance of profit and loss at the time of maturity. The position
evaluation module 116 is configured to compute the trading position at a particular trading time
instance using the equation (8) provided below.
Δ)D E501,F:;G0018E(01,01*
(F:;G0*0101
, 3
1,…. . , .… (8)
wherein, Δ)D represents trading position that are globally optimum in a risk-neutral measure at
(i-1)th trading time instance,
'($), )* represents current option price of the underlying asset,
) represents the current market price of the underlying asset,
'@$), -=;A0)B represents shifted option price of the underlying asset,
(-=;A0*) represents shifted price of the underlying asset at a trading time instance
$) , and
H) is the time difference between two consecutive trading time instances.
[0048] The position evaluation module 116 evaluates the trading position at each trading
time instance. At the time of maturity, the trader liquidates the computed trading positions and
delivers the payoff to the buyer. In an example, a seller of the ECC gets premium (β) from the
buyer and purchases Δ*1 units of the underlying asset at price (S0) at trading time instance (T0).
Thereafter, at trading time instance (T1), the seller sells Δ*1 units of the underlying asset at price
(S1) and repurchases Δ*2 units of the underlying asset at price (S1) and this continues till the time
to maturity (Tn). The seller then, at the time of maturity (Tn) liquates the position, i.e., Δ*n units
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of the underlying asset at price (Sn) and delivers the payoff (H) to the buyer of the ECC. Thus,
according to the present subject matter, the trading positions that are globally optimum in the
risk-neutral measure are evaluated by using a simple analytical closed-form expression, i.e., the
equation (8).
[0049] Figs. 2b-2f illustrate an exemplary data set for evaluating trading positions and
graphical representations depicting comparison of global variance of profit and loss obtained by
the present trading position evaluation system 102 and the conventional system. As shown in the
Fig. 2b, the data set 230 containing data related to an ECC written on an underlying asset, such
as stock of State Bank of India, Maruti, Jindal Steel, and Bharat Heavy Electrical Limited is
taken as input for evaluation of trading positions at a plurality of trading time instances. For
example, ECC data 110, such as time of initiation of the ECC and time to maturity of the ECC,
and historical data 112 of the underlying asset for a defined period indicated in the data set 230 is
received as input. Based on the data set 230, trading positions at the plurality of trading time
instances are evaluated separately by the trading position evaluation system 102 and the
conventional system. In one implementation, the trading positions are computed assuming
trading is performed at inter-trading duration of one day, five days, seven days and forty-five
days (Static). The inter-trading durations may be understood as the time intervals between two
trading time instances. The conventional system referred herein is a traditional hedging system
based on Black-Scholes hedging strategy.
[0050] Based on the resulting trading positions, a global variance of profit and loss to the
trader as obtained by the trading position evaluation system 102 and the conventional system is
compared with one another. Such a comparison for each stock is illustrated in the form of
graphical representations provided in Figs. 2c-2f. Specifically, Fig 2c illustrates comparison of
the global variance of profit/loss obtained by the trading position evaluation system 102 and the
conventional system for the underlying asset, i.e., stock of State Bank of India, at different
trading time instances. Likewise, Figs. 2d-2f illustrate such a comparison for stocks of Maruti,
Jindal Steel and Bharat Heavy Electricals Limited, respectively. As clearly depicted in the Figs.
2c-2f, the global variance of profit/loss obtained by the present trading position evaluation
system 102 is lower than the global variance obtained by the conventional system. Further, the
Figs 2c-2f also convey that the present trading position evaluation system 102 gets better than
the conventional system as hedging is performed more discretely.
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[0051] Fig. 3 illustrates a method 300 for evaluating the trading positions that are
globally optimum in a risk-neutral measure, in accordance to an embodiment of the present
subject matter. The method 300 is implemented in computing device, such as a trading position
evaluation system 102. The method may be described in the general context of computer
executable instructions. Generally, computer executable instructions can include routines,
programs, objects, components, data structures, procedures, modules, functions, etc., that
perform particular functions or implement particular abstract data types. The method may also be
practiced in a distributed computing environment where functions are performed by remote
processing devices that are linked through a communications network.
[0052] The order in which the method is described is not intended to be construed as a
limitation, and any number of the described method blocks can be combined in any order to
implement the method, or an alternative method. Furthermore, the method can be implemented
in any suitable hardware, software, firmware or combination thereof.
[0053] At block 302, the method 300 includes retrieving ECC data 110 and market data
114 associated with an underlying asset of a path-independent ECC. The ECC data 110 may
include the data associated with the ECC such as, its payoff (H), time of initiation (T0), time to
maturity (Tn), premium (β), spot price, strike price (K) and current market prices of call and put
options. The market data 114 includes the annualized volatility (σ) of the underlying asset and
the risk-free interest rate (r) of the market.
[0054] At block 304 of the method 300, a current option price and a shifted option price
of the underlying asset are determined. The current option price and the shifted option price of
the underlying asset are determined at a trading time instance based on the ECC data 110 and the
market data 114. The trading time instance is provided by a trader of the ECC. In accordance
with one implementation of the present subject matter, the option price determination module
216 determines the current option price and the shifted option price of the underlying asset based
on equation (4), (5), (6), and (7) described in the previous section.
[0055] At block 306 of the method 300, a trading position in the underlying asset at the
trading time instance is evaluated based on the current option price and the shifted option price.
The evaluated trading position is globally optimum in a risk-neutral measure. Such a trading
position is also referred as globally optimum trading position in the present description. In one
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implementation, the position evaluation module 116 evaluates the globally optimum trading
position of the underlying asset based on the equation (8) described in the previous section.
[0056] The method blocks described above are repeated at each of a plurality of trading
time instance provided by the trader to evaluate the trading positions at each trading time
instance. At the last trading time instance, the trader such as the seller of the ECC liquidates the
underlying asset and delivers the payoff to the buyer in order to minimize the global variance of
profit and loss at the time of maturity of the ECC.
[0057] Although embodiments for methods and systems for evaluating trading positions
that are globally optimum in the risk-neutral measure have been described in a language specific
to structural features and/or methods, it is to be understood that the invention is not necessarily
limited to the specific features or methods described. Rather, the specific features and methods
are disclosed as exemplary embodiments for evaluating the globally optimum trading positions
in the risk-neutral measure.
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I/We claim:
1. A trading position evaluation system (102) comprising:
a processor (202); and
a memory (206) coupled to the processor (202), the memory (206) comprising:
an option price determination module (216) configured to determine a current
option price and a shifted option price of an underlying asset of a European
Contingent Claim (ECC), at a trading time instance amongst a plurality of trading
time instances obtained from a trader, based on ECC data (110) and market data
(114), wherein the ECC data (110) comprises data associated with the ECC and
the underlying asset, and the market data (114) comprises annualized volatility of
the underlying asset and risk-free interest rate of market; and
a position evaluation module (116) configured to evaluate a trading position
in the underlying asset at the trading time instance based on the current option
price and the shifted option price, wherein the trading position minimizes global
variance of profit and loss to the trader.
2. The trading position evaluation system (102) as claimed in claim 1 further comprising a
volatility computation module (212) is configured to:
retrieve historical data (112) of the underlying asset, wherein the historical data
(112) comprises historical market prices of the underlying asset;
compute log-returns of the underlying asset based on the historical data (112);
generate a plurality of scenarios based on fitting the log-returns into a best-fit
distribution;
fit the plurality of scenarios to a normal distribution to compute volatility of the
underlying asset; and
annualize the volatility to obtain the annualized volatility.
3. The trading position evaluation system (102) as claimed in claim 1, wherein the ECC
data (110) comprises time of initiation of the ECC, time to maturity of the ECC,
premium, spot price of the underlying asset of the ECC, strike price of the ECC, and
current market price of the call and put options.
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4. The trading position evaluation system (102) as claimed in claim 1 further comprising an
interest rate calculation module (214) configured to calculate the risk-free interest rate
based on the ECC data (110).
5. The trading position evaluation system (102) as claimed in claim 2, wherein the best-fit
distribution is any one of a Normal distribution, a Poisson distribution, and a Tdistribution.
6. A method for evaluating trading positions that are globally optimum in a risk-neutral
measure, wherein the method comprising:
receiving a plurality of trading time instances from a trader;
retrieving ECC data (110) and market data (114) associated with a European
Contingent Claim (ECC) from a database (108), wherein the ECC data (110) comprises
data associated with the ECC and an underlying asset of the ECC, and the market data
(114) comprises annualized volatility of the underlying asset and risk-free interest rate of
market;
computing a current option price and a shifted option price of the underlying asset
at each of the plurality trading time instances based on the ECC data (110) and the market
data (114); and
evaluating a trading position in the underlying asset at each of the plurality of
trading time instances based on the current option price and the shifted option price,
wherein the trading position minimizes global variance of profit and loss to the trader.
7. The method as claimed in claim 6 further comprising:
retrieving historical data (112) for a predefined period from the database (108);
evaluating log-returns of the underlying asset based on the historical data (112);
generating a plurality of scenarios based on fitting the log-returns into a best-fit
distribution;
fitting the plurality of scenarios to a normal distribution to compute the volatility
of the underlying asset; and
annualizing the volatility to obtain the annualized volatility.
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8. The method as claimed in claim 7, wherein the historical data (112) comprises historical
market prices of the underlying asset obtained from a data source.
9. The method as claimed in claim 6, wherein the ECC data (110) comprises time of
initiation of the ECC, time to maturity of the ECC, premium, spot price of the underlying
asset of the ECC, strike price of the ECC, and current market price of the call and put
options.
10. The method as claimed in claim 6 further comprising calculating the risk-free interest rate
based on the ECC data (110).
11. A computer-readable medium having embodied thereon a computer program for
executing a method comprising:
receiving a plurality of trading time instances from a trader;
retrieving ECC data (110) and market data (114) associated with a European
Contingent Claim (ECC) from a database (108), wherein the ECC data (110) comprises
data associated with the ECC and an underlying asset of the ECC, and the market data
(114) comprises annualized volatility of the underlying asset and risk-free interest rate of
market;
computing a current option price and a shifted option price of the underlying asset
at each of the plurality trading time instances based on the ECC data (110) and the market
data (114); and
evaluating a trading position in the underlying asset at each of the plurality of
trading time instances based on the current option price and the shifted option price,
wherein the trading position minimizes global variance of profit and loss to the trader.