JP2006221584A - Method and apparatus for evaluating transaction price of energy, and program - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To calculate the premium of energy without futures market. <P>SOLUTION: A transaction price evaluating device 10 performs the regression analysis of JCC based on the future price of Brent. The transaction price evaluating device 10 calculates the predicted value of the future price of Brent in the future by using volatility, normal random numbers or simulation periods or the like. The transaction price evaluating device 10 calculates the option price of JCC by applying the predicted value of the future price of Brent to a regression expression, and calculates the cash flow of a difference between the option price of JCC and the practice price of the option transaction. The transaction price evaluating device 10 calculates the expected value of the cash flow as the option premium of JCC. <P>COPYRIGHT: (C)2006,JPO&NCIPI

Description

  The present invention relates to an energy transaction price evaluation method, a transaction price evaluation device, and a program.

Option transactions and swap transactions are conducted to hedge the risk of future price fluctuations related to energy procurement such as fuel. In such transactions, the price of energy at a future point in time is predicted, and a premium according to the predicted price, strike price, volatility, etc. is determined. Patent Document 1 proposes a system that evaluates the market price close to the market price of an option by deriving a reasonable period and delta volatility using a volatility curved surface model.
JP 2002-288436 A

  However, since the conventional method such as the method of Patent Document 1 is based on the assumption that market data such as volatility is acquired, it is not possible to acquire market data for energy prices that do not have a futures market. I could not ask.

  The present invention has been made in view of such a background, and provides an energy transaction price evaluation method, a transaction price evaluation device, and a program capable of calculating an energy premium without a futures market. Objective.

  The invention according to claim 1 of the present invention for solving the above-mentioned problem is a method for evaluating a transaction price of a first energy without a futures market, and a computer including a CPU, a memory, and an output device, The history of the spot price of the first energy, the history of the future price of the second energy in the future market, and the volatility related to the future price are stored in the memory, and the future price of the second energy. The physical price of the first energy is subjected to regression analysis, and an input of an exercise price, which is a transaction price of the first energy at a future time point, is received, a history of the future price, the volatility, a normal random number, Based on the above, a future future price, which is a predicted value of the future price of the second energy at the future time point, is calculated a predetermined number of times, and the future future price is calculated. Applying each to the regression equation obtained by the regression analysis to calculate a future transaction price that is a predicted value of the transaction price of the first energy at the future time point, and calculating the future transaction price, the exercise price, The cash flow that is the difference between the two is calculated, the cash flow is aggregated to calculate a premium for the transaction price of the first energy, and the premium is output to the output device.

  According to the transaction price evaluation method of the present invention, the first energy option transaction, the swap transaction, and the forward transaction are performed for the first energy without the future market using the future price of the second energy with the future market. The premium in such as can be calculated. Accordingly, an operator such as an option transaction based on a relative transaction can verify, for example, the validity of a transaction price presented by the counterparty or present a reasonable premium to the counterparty.

Further, the invention according to claim 2 of the present invention is the energy transaction price evaluation method according to claim 1, wherein the computer calculates the future time point T, the volatility σ, and the normal random number. ε, where the futures price at the start of the period to be processed is F ₀ , and the futures price at the future time is F _T , the futures future price is calculated based on equation (1) described below I decided to.

Moreover, invention of Claim 3 among the present invention is the energy transaction price evaluation method of Claim 1, Comprising: The said computer calculates the said future transaction price for every predetermined period, The said predetermined period The average value of each future future price is applied to the regression equation.
In the evaluation of Asian options, since it is necessary to evaluate the average of the underlying assets during the period, it is necessary to recalculate the underlying asset price every predetermined period, for example, every day or every month. For example, an Asian option can be evaluated by obtaining a future transaction price (pass) for each predetermined period.

Further, the invention according to claim 4 of the present invention is the energy transaction price evaluation method according to claim 3, wherein the computer calculates the futures price at a certain time t from F _t and from the time t. When the futures price before the predetermined period is F _t−1 , the predetermined period is Δt, the volatility is σ, and the normal random number is ε, the future futures price is calculated based on the following formula (2). I decided to.

Moreover, invention of Claim 5 among the present invention is the energy transaction price evaluation method of Claim 1, Comprising: The said computer specifies the average period which is a period which calculates | requires the said average value. An input of period specifying information is received, the future transaction price for each predetermined period in the average period is calculated, and an average value of the future transaction price for each predetermined period is applied to the regression equation.
The processing load for generating a path by simulation increases explosively according to the predetermined period for calculating the future transaction price and the predetermined number of times. According to the invention of claim 5, only the average period is calculated. Since the path is generated, the processing load related to the simulation can be reduced.

  The invention according to claim 6 of the present invention is the energy transaction price evaluation method according to claim 1, wherein the computer accepts an input of a risk-free interest rate, Accordingly, the premium is discounted and the discounted premium is output to the output device.

  The invention according to claim 7 of the present invention is the energy transaction price evaluation method according to claim 1, wherein the computer is configured to add a regression error by the regression analysis to the future transaction price. And a first cash flow that is a difference between the exercise price and a second cash flow that is a difference between the exercise price and a price obtained by subtracting the regression error from the future transaction price, The first premium that totals cash flows and the second premium that totals the second cash flows are calculated, and the first and second premiums are output to the output device.

  An invention according to an eighth aspect of the present invention is the energy transaction price evaluation method according to the first aspect, wherein the computer uses the fluctuation range specifying information for specifying the fluctuation range of the volatility. The first future future price based on the lower limit value of the fluctuation range specified by the received fluctuation range designation information, the futures price, and the normal random number, and the upper limit value of the fluctuation range and the futures. A second future future price based on the price and the normal random number is calculated a predetermined number of times, and a first future transaction price obtained by applying each of the first future future prices to the regression equation; A first cash flow that is a difference between the first future transaction price and the exercise price, and calculating a second future transaction price obtained by applying each of future future prices to the regression equation; The second cash flow, which is the difference between the second future transaction price and the exercise price, is calculated, the first premium obtained by counting the first cash flow, and the second cash flow are calculated. The aggregated second premium is calculated, and the first and second premiums are output to the output device.

  The invention according to claim 9 of the present invention is an apparatus for evaluating a transaction price of a first energy without a futures market, wherein a CPU, a memory, an output device, and an actual price of the first energy are calculated. A spot history storage unit that stores a history, a futures history storage unit that stores a history of future prices of second energy with a futures market, a volatility storage unit that stores volatility related to the futures price, and the second A regression analysis unit that performs regression analysis of the spot price of the first energy based on the futures price of energy, and an exercise price input unit that receives an input of an exercise price that is a transaction price of the first energy at a future time point Based on the futures price history, the volatility, and a normal random number, the future price is a predicted value of the futures price related to the second energy at the future time point. A future futures price calculation unit that calculates a futures price a predetermined number of times, and applying each of the futures futures prices to the regression equation obtained by the regression analysis, and a predicted value of the transaction price of the first energy at the future time point A cash flow calculation unit that calculates a future transaction price and calculates a cash flow that is a difference between the future transaction price and the exercise price; and aggregating the cash flow to determine a transaction price of the first energy A premium calculation unit that calculates a premium and an output unit that outputs the premium to the output device are provided.

  According to a tenth aspect of the present invention, there is provided a program for evaluating a transaction price of a first energy without a futures market, wherein the computer includes a CPU, a memory, and an output device. Storing a history of a spot price of one energy, a history of a future price of a second energy with a futures market, and a volatility related to the future price in the memory; and the future price of the second energy Performing a regression analysis of the spot price of the first energy based on: a step of accepting an input of an exercise price that is a transaction price of the first energy at a future time point; a history of the future price and the volatility And the future future price, which is a predicted value of the future price of the second energy at the future time, based on the Calculating a future transaction price that is a predicted value of the transaction price of the first energy at the future time point by applying each of the future futures price to the regression equation obtained by the regression analysis; Calculating a cash flow that is the difference between the future transaction price and the exercise price; calculating a premium for the transaction price of the first energy by aggregating the cash flow; and And outputting to the output device.

  According to the present invention, an energy premium without a futures market can be calculated.

  Hereinafter, a transaction price evaluation apparatus 10 according to an embodiment of the present invention will be described. In this embodiment, an electric power company proposed by a business partner when conducting an option transaction with a financial institution or a trading company in order to hedge the LNG price increase risk associated with the purchase of LNG gas used for power generation. It is assumed that the option premium is considered appropriate or the option premium to be proposed to the business partner is determined.

  The LNG price is linked to the JCC (All Japan Crude Oil CIF price; corresponding to the “first energy transaction price” of the present invention). JCC does not have a futures market. In this embodiment, JCC's option premium is based on the futures price of Brent (North Sea Brent crude oil, which corresponds to the “second energy” of the present invention), which is an energy with a futures market. Is calculated.

=== Device Configuration ===
FIG. 1 shows a hardware configuration of the transaction price evaluation apparatus 10 of the present embodiment. As shown in FIG. 1, the transaction price evaluation device 10 includes a CPU 101, a memory 102, a storage device 103, a recording medium reading device 104, an input device 105, and an output device 106.

  The storage device 103 is, for example, a hard disk that stores programs and data. The recording medium reading device 104 reads a program or data recorded on the recording medium 107 to the storage device 103, for example, a CD-ROM drive, a DVD-ROM drive, a flexible disk drive, a magnetic tape device, a memory card reader, or the like. is there. As the recording medium 107, for example, a CD-ROM, a DVD-ROM, a flexible disk, a magnetic tape, a memory card, or the like can be used according to the recording medium reader 104.

The CPU 101 implements various functions by reading a program stored in the storage device 103 into the memory 102 and executing it.
The input device 105 accepts data input and is, for example, a keyboard, a mouse, a touch panel, a tablet, or a microphone. Further, the transaction price evaluation device 10 may include a plurality of these.
The output device 106 is, for example, a CRT or a liquid crystal display, a printer, or a speaker that outputs data. Further, the transaction price evaluation device 10 may include a plurality of output devices 106.

  FIG. 2 is a functional block diagram of the transaction price evaluation apparatus 10. As shown in FIG. 2, the transaction price evaluation apparatus 10 includes a futures price input unit 111, a spot price input unit 112, a setting information input unit 113, a regression analysis unit 114, a normal random number generation unit 115, a futures price evaluation unit 116, a premium An evaluation unit 117, an output unit 118, a futures information storage unit 151, an actual information storage unit 152, and a setting information storage unit 153 are provided.

The futures information storage unit 151 stores futures information including the future price of Brent. FIG. 3 shows a configuration example of futures information stored in the futures information storage unit 151. As shown in FIG. 3, the futures information includes a transaction date, a futures price, and an exercise deadline.
The futures price input unit 111 receives input of futures information and registers the received futures information in the futures information storage unit 151. Note that the futures price input unit 111 may accept inputs of futures information and spot information via the input device 105 such as a keyboard or a mouse, and manages futures information such as a securities company. You may connect with a server apparatus and you may make it acquire from the server apparatus.

The physical information storage unit 152 stores physical information including JCC and its transaction date. FIG. 4 shows a configuration example of actual information stored in the actual information storage unit 152. As shown in FIG. 4, the actual information includes the JCC transaction date and JCC (transaction price).
The physical information input unit 112 receives input of physical information and registers the received physical information in the physical information storage unit 152. Note that the physical information input unit 112 may accept input of physical information via the input device 105 such as a keyboard or a mouse, for example, or a government agency or a securities company that announces JCC. It is also possible to connect to a server device that manages the physical information and acquire it from the server device.

The setting information storage unit 153 stores setting information including various setting items. FIG. 5 shows a configuration of setting information stored in the setting information storage unit 153. As shown in FIG. 5, the setting information includes the JCC option exercise price, the risk-free interest rate, the number of simulations for determining the future price of Brent, the simulation target period (hereinafter referred to as the simulation period), the average period, Includes confidence intervals, Brent futures price volatility, volatility fluctuation ranges, and the like. The risk-free interest rate is the yield that guarantees principal and interest payment, and is used to convert JCC into present value. As will be described later, the average period is a predetermined period when calculating the average value of the futures price in the transition of the futures price (hereinafter referred to as a pass) obtained by simulation in a predetermined period. The confidence interval is a confidence interval in the result of regression analysis described later. As will be described later, an upper limit and a lower limit of the option premium caused by the regression error in the confidence interval are calculated. The volatility fluctuation range is information for designating a range of values that the volatility can take. In the present embodiment, the volatility also follows a normal distribution, and the confidence interval is used as the fluctuation range.
The setting information input unit 113 receives input of setting information and registers the received setting information in the setting information storage unit 153.

The regression analysis unit 114 performs a regression analysis using the history of the future price of the Brent stored in the futures information storage unit 151 and the history of the JCC stored in the actual information storage unit 152.
The normal random number generation unit 115 generates a normal random number used for the simulation.
The futures price evaluation unit 116 calculates the predicted value of the future price of the Brent after the simulation period. Simulation period (in days) is T, futures price _{F T} of Brent after simulation period T is the futures price of Brent at the time when a reference _{F 0,} the normal random numbers normal random number generating unit 115 generates ε , Where σ is the volatility of the Brent futures price, the following equation (1) is obtained.

Incidentally, showing the Brent futures price _{F T} after simulation period T, the state obtained directly by the above formula (1) in FIG. 6. However, in the case of Asian options, it is necessary to evaluate the average of the underlying assets during the period, so it is necessary to recalculate the futures price every predetermined period. In this case, the futures price F _t at a certain time _t is obtained by the following equation (2), where Δt is a predetermined period.

  FIG. 7 shows how the future price transition (path) at each time point t = 1, 2, 3,... T is obtained using (2) above.

  In the present embodiment, in order to reduce the processing load required for generating a path over a long period in the simulation, the path is obtained using the above equation (2) only during the averaging period of the setting information. FIG. 8 shows a state in which the path is calculated only during the average period. In FIG. 8, the futures price from the time 0 to the beginning of the average period is calculated by the above formula (1), and the path of the futures price within the average period is calculated by the formula (2).

  The premium evaluation unit 117 calculates an option premium for JCC. The premium evaluation unit 117 calculates the JCC option price by applying the Brent future price calculated by the futures price evaluation unit 116 to the regression equation obtained by the regression analysis performed by the regression analysis unit 114, and calculates the JCC option price. And the cash flow that is the difference between the option exercise price in the setting information. Note that the future price calculation by the future price evaluation unit 116 and the cash flow calculation by the premium evaluation unit 117 are performed for the number of simulations set in the setting information. The premium evaluation unit 117 calculates the expected value of the cash flow calculated for the number of simulations and determines the JCC option premium. Details of the JCC option premium calculation process will be described later.

In addition to the JCC option premiums described above, the premium evaluation unit 117 corresponds to two option premiums corresponding to the upper and lower limits of the volatility fluctuation range and upper and lower limits of the confidence interval of the regression error. Two option premiums are calculated.
The output unit 118 outputs the JCC option premium determined by the premium evaluation unit 117 to the output device 106.

=== Option Premium Output Processing ===
FIG. 9 is a diagram showing a flow of output processing of the JCC option premium by the transaction price evaluation apparatus 10.
The transaction price evaluation device 10 performs a regression analysis using the Brent future price stored in the futures information storage unit 151 and the JCC stored in the spot information storage unit 152 (S201). As a result of the regression analysis, for example, a regression equation such as the following equation (3) is obtained.
JCC = 0.9737 x Brent futures price + 0.8816 (3)

Subsequently, the transaction price evaluation device 10 reads out the Brent future price at time 0 from the futures information storage unit 151, reads the future price (F ₀ ), the volatility (σ) of the setting information, and the number of days of the simulation period (T ), Gaussian random numbers normal random number generating unit 115 generates the (epsilon) was applied to the above equation (1) to calculate the futures price _{F S} at the start of the averaging period (S) (S202). The transaction price evaluation apparatus 10 applies F _S to the initial value of F _t−1 and the predetermined period Δt to 1 day, and applies it to the above equation (2) to generate a path from the S time point to the T time point (S203). . The transaction price evaluation device 10 calculates the JCC (option price) at the time _T by applying the future price FT of the Brent at the time T to the regression equation obtained in the above (S201). The transaction price evaluation device 10 calculates the difference between the calculated option price and the exercise price of the setting information as a cash flow (S205).
The transaction price evaluation device 10 repeats the above processes (S202) to (S205) for the number of times of setting information simulation.

  Next, the transaction price evaluation device 10 calculates the expected value of the cash flow (S206). This expected value is the option premium. The transaction price evaluation device 10 calculates a quotient of the option premium and a value obtained by adding 1.0 to the risk-free interest rate of the setting information, and corrects (discounts) the option premium to the present value (S207). The transaction price evaluation device 10 outputs the calculated option premium to the output device 106 (S208).

  As described above, according to the transaction price evaluation apparatus 10 of the present embodiment, the option premium of JCC without a futures market can be calculated and output using the futures price of a Brent with a futures market. Therefore, an electric power company or the like that conducts JCC option transactions can present a reasonable transaction price to a trading partner even when it is not a market transaction such as a relative transaction. Similarly, the validity of the transaction price presented by the trading partner can be evaluated.

  FIG. 10 is a calculation example of JCC option premiums (evaluation results) for a plurality of strike prices. In the example of FIG. 10, the premium proposed by the customer is about 20% higher than the evaluation result for any strike price. An operator such as an option transaction can make the above determination with reference to the option premium output from the transaction price evaluation device 10 of the present embodiment. Accordingly, an operator of option trading or the like can easily examine the validity of the option premium proposed by the business partner. Therefore, an electric power company or the like that conducts LNG gas option transactions can appropriately hedge the risk of price fluctuation of the LNG gas using a reasonable option premium. Therefore, since the electric power company etc. can purchase LNG gas at a stable price, it is possible to provide a stable and reasonable power service and the like.

=== Volatility fluctuation range ===
In the above description, the volatility of Brent is assumed to be constant. However, if there is a fluctuation range in volatility, the option premium as an evaluation result also has a fluctuation range according to the fluctuation range. In this case, the transaction price evaluation device of the present embodiment performs a simulation based on each of the upper and lower limits of the volatility fluctuation range, and outputs an option premium corresponding to each.

  For example, when 95% is set as the fluctuation range in the setting information as in the example of FIG. 5 and the volatility in the 95% confidence interval is 26.2% to 34.2%, the transaction price evaluation device 10 In the formula (1) or the formula (2), two option premiums are calculated when 26.2 is set as the volatility σ and when 34.2 is set. FIG. 11 shows an example of calculating the option premium according to the fluctuation range of volatility. In the example shown in FIG. 11, it can be seen that the proposal amount of the supplier exceeds the option premium calculated by the transaction price evaluation device 10 in most cases. As described above, the option premium incorporating the prediction error due to the volatility fluctuation is output, so that an option trader or the like can evaluate the validity of the option premium from the viewpoint of the volatility fluctuation. For example, in the above example, options traders can negotiate the transaction price by determining that the proposal amount from the counterparty is not appropriate, and consider the risk premium related to factors other than volatility. It can also be.

=== Regression error ===
Further, the transaction price evaluation device 10 of the present embodiment can output two option premiums according to the regression error by the regression analysis described above. In this case, the transaction price evaluation device 10 performs simulation for each of the upper and lower limits of the value that the regression error can take in the confidence interval of the setting information, and calculates two option premiums. That is, the transaction price evaluation device 10 calculates an option premium as a regression error Ε by replacing the following formulas (1 ′) and (2 ′) with the above formulas (1) and (2).

  For example, as shown in FIG. 5, when 68% is set as the confidence interval in the setting information and the regression error is −0.76 to 0.76 in the 68% confidence interval, the transaction price evaluation device 10 In the formula (1) or the formula (2), two option premiums are calculated when the regression error − is set to −0.76 and when 0.76 is set. FIG. 12 is an example of calculating the option premium according to the regression error. In the example shown in FIG. 12, it is understood that the proposal amount of the supplier is a reasonable amount because it is equal to or less than the upper limit value of the option premium due to the regression error. In this way, the option premium incorporating the prediction error due to the regression error is output, so that the validity of the option premium can be evaluated from the viewpoint of the regression error.

  In the present embodiment, the transaction price evaluation device 10 calculates an option premium related to an option transaction. However, for example, a premium related to a transaction price such as a swap transaction or a forward transaction may be calculated. .

  In the present embodiment, JCC based on Brent is calculated, but other futures prices and spot prices can be used. For example, the WTI future price may be used instead of the Brent future price.

  Further, in the present embodiment, the futures price and the spot price are managed on a daily basis, but may be managed on a monthly basis, for example. Thereby, it is possible to deal with option transactions and the like on a monthly basis. In this case, for example, for futures prices in daily units, an average value for one month may be handled as futures prices in monthly units.

  Further, in the present embodiment, a pass for only the average period is generated. However, depending on the processing capability of the transaction price evaluation apparatus 10, a pass for the entire period may be generated.

  In the present embodiment, the path in the average period is generated. However, the future price at the time T may be calculated using only the equation (1) without generating the path. In this case, the processing load related to the simulation can be reduced.

  Further, although the transaction price evaluation device 10 of the present embodiment sets only one exercise price in the setting information, it is also possible to perform simulation for a plurality of exercise prices at the same time. In this case, for example, as shown in FIGS. 10 to 12, option premiums calculated for a plurality of exercise prices can be displayed in a list, so that visibility is improved and convenient for the user of the transaction price evaluation apparatus 10.

  Although the present embodiment has been described above, the above embodiment is intended to facilitate understanding of the present invention and is not intended to limit the present invention. The present invention can be changed and improved without departing from the gist thereof, and the present invention includes equivalents thereof.

1 shows a hardware configuration of a transaction price evaluation device 10 according to an embodiment of the present invention. It is a functional block diagram of transaction price evaluation apparatus 10 by one embodiment of the present invention. It is a figure which shows the structural example of futures information by one embodiment of this invention. It is a figure which shows the structural example of the physical information by one embodiment of this invention. It is a figure which shows the structure of the setting information by one embodiment of this invention. According to an embodiment of the present invention, the Brent futures price F _T after simulation period T, is a diagram showing a state obtained directly by the equation (1). It is a figure which shows a mode that the transition (path) of the futures price in each time point was calculated | required using Formula (2) by one embodiment of this invention. It is a figure which shows a mode that the path | pass was calculated only within the average period by one embodiment of this invention. It is a figure which shows the flow of an output process of the option premium of JCC by one embodiment of this invention. It is a figure which shows the example of calculation of the premium (evaluation result) calculated about the some exercise price by one embodiment of this invention. It is an example calculation of the option premium according to the fluctuation range of volatility by one embodiment of the present invention. It is an example calculation of the option premium according to the regression error by one embodiment of the present invention.

Explanation of symbols

DESCRIPTION OF SYMBOLS 10 Transaction price evaluation apparatus 101 CPU 102 Memory 103 Storage apparatus 104 Recording medium reader 105 Input apparatus 106 Output apparatus 107 Recording medium 111 Future price input part 112 Spot price input part 113 Setting information input part 114 Regression analysis part 115 Normal random number generation part 116 Futures Price Evaluation Unit 117 Premium Evaluation Unit 118 Output Unit 151 Futures Information Storage Unit 152 Information Storage Unit 153 Setting Information Storage Unit

Claims (10)

A method for evaluating the transaction price of a first energy without a futures market,
A computer including a CPU, a memory, and an output device
Storing the history of the spot price of the first energy, the history of the future price of the second energy with a futures market, and the volatility related to the future price in the memory;
Performing a regression analysis of the spot price of the first energy based on the futures price of the second energy;
Accepting an input of an exercise price, which is a transaction price of the first energy at a future time point,
Based on the history of the futures price, the volatility and the regular random number, a future futures price that is a predicted value of the futures price related to the second energy at the future time point is calculated a predetermined number of times,
Applying each of the future futures prices to the regression equation obtained by the regression analysis to calculate a future transaction price that is a predicted value of the transaction price of the first energy at the future time point; and Calculate the cash flow that is the difference from the strike price,
Calculate the premium for the transaction price of the first energy by aggregating the cash flow;
Outputting the premium to the output device;
Energy transaction price evaluation method characterized by An energy transaction price evaluation method according to claim 1,
The computer
When the future time is T, the volatility is σ, the normal random number is ε, the future price at the start of the period to be processed is F ₀ , and the future price at the future time is F _T , formula

Calculating the futures price based on
Energy transaction price evaluation method characterized by An energy transaction price evaluation method according to claim 1,
The computer
Calculating the future transaction price for each predetermined period, and applying an average value of the future futures price for each predetermined period to the regression equation;
Energy transaction price evaluation method characterized by An energy transaction price evaluation method according to claim 3,
The computer
When the futures price at a certain time t is F _t , the futures price before the predetermined period from the time t is F _t−1 , the predetermined period is Δt, the volatility is σ, and the normal random number is ε, formula

Calculating the futures price based on
Energy transaction price evaluation method characterized by An energy transaction price evaluation method according to claim 1,
The computer
Accepting input of period specifying information for specifying an average period, which is a period for obtaining the average value,
Calculating the future transaction price for each predetermined period in the average period, and applying an average value of the future transaction price for each predetermined period to the regression equation;
Energy transaction price evaluation method characterized by An energy transaction price evaluation method according to claim 1,
The computer
Accepts input of risk-free interest rate,
Discounting the premium according to the accepted risk-free interest rate, outputting the discounted premium to the output device;
Energy transaction price evaluation method characterized by An energy transaction price evaluation method according to claim 1,
The computer
A first cash flow that is a difference between the price obtained by adding a regression error by the regression analysis to the future transaction price and the exercise price, and a difference between the price obtained by subtracting the regression error from the future transaction price and the exercise price To calculate a second cash flow that is
Calculating the first premium totaling the first cash flow and the second premium totaling the second cash flow;
Outputting the first and second premiums to the output device;
Energy transaction price evaluation method characterized by An energy transaction price evaluation method according to claim 1,
The computer
Accepting input of fluctuation range specifying information for specifying the fluctuation range of the volatility,
The first future future price based on the lower limit value of the fluctuation range specified by the received fluctuation range designation information, the futures price, and the normal random number, and the upper limit value of the fluctuation range, the futures price, and the normal value A second future future price based on a random number is calculated the predetermined number of times;
The first future transaction price obtained by applying each of the first future futures prices to the regression equation, and the second future transaction price obtained by applying each of the second future futures prices to the regression equation. A first cash flow that is the difference between the first future transaction price and the exercise price, and a second cash flow that is the difference between the second future transaction price and the exercise price; To calculate
Calculating the first premium totaling the first cash flow and the second premium totaling the second cash flow;
Outputting the first and second premiums to the output device;
Energy transaction price evaluation method characterized by An apparatus for evaluating the transaction price of a first energy without a futures market,
CPU, memory and output device,
A physical history storage unit that stores a history of physical prices of the first energy;
A futures history storage unit that stores a history of future prices of second energy with a futures market;
A volatility storage unit that stores volatility related to the futures price;
A regression analysis unit that performs regression analysis of the spot price of the first energy based on the futures price of the second energy;
An exercise price input unit for receiving an input of an exercise price, which is a transaction price at a future time of the first energy,
A futures future price calculator that calculates a future futures price that is a predicted value of a futures price related to the second energy at the future time point a predetermined number of times based on the history of the futures price, the volatility, and a normal random number;
Applying each of the future futures prices to the regression equation obtained by the regression analysis to calculate a future transaction price that is a predicted value of the transaction price of the first energy at the future time point; and A cash flow calculation unit for calculating a cash flow which is a difference from the exercise price;
A premium calculator that calculates the premium for the transaction price of the first energy by aggregating the cash flow;
An output unit for outputting the premium to the output device;
A transaction price evaluation device for energy characterized by comprising: A program for evaluating a transaction price of a first energy without a futures market, comprising: a CPU including a CPU, a memory, and an output device;
Storing the history of the spot price of the first energy, the history of the future price of a second energy with a futures market, and the volatility associated with the future price in the memory;
Performing a regression analysis of the spot price of the first energy based on the futures price of the second energy;
Receiving an strike price to be a transaction price at a future time of the first energy;
Based on the future price history, the volatility, and a normal random number, calculating a future future price that is a predicted value of a future price related to the second energy at the future time point a predetermined number of times;
Applying each of the future futures prices to the regression equation obtained by the regression analysis to calculate a future transaction price that is a predicted value of the transaction price of the first energy at the future time point; and Calculating a cash flow which is a difference from the exercise price;
Summing up the cash flow to calculate a premium for the transaction price of the first energy;
Outputting the premium to the output device;
A program for running

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