This document summarizes Mario Dell'Era's presentation on finding a closed solution for the Heston PDE using geometrical transformations. It describes the Heston model and the resulting PDE. Existing methods for solving the PDE numerically are outlined. Dell'Era then presents a new methodology using coordinate transformations to solve the PDE, applying three successive transformations to simplify the PDE. This results in an exponential solution for the transformed PDE.
Abstract. Regulations of the market require disclosure of information about the nature and extent of risks arising from the trades of the market instruments. There are several significant drawbacks in fixed income pricing modeling. In this paper we interpret a corporate bond price as a random variable. In this case the spot price does not a complete characteristic of the price. The price should be specified by the spot price as well as its value of market risk. This interpretation is similar to a random variable in Probability Theory where an estimate of the random variable completely defined by its cumulative distribution function. The buyer market risk is the value of the chance that the spot price is higher than it is implied by the market scenarios. First we quantify credit risk of the corporate bonds and then consider marked-to-market pricing adjustment to bond price. In the case when issuer of the corporate bond is the counterparty of the bond buyer counterparty and credit risks are coincide.
Hierarchical Deterministic Quadrature Methods for Option Pricing under the Ro...Chiheb Ben Hammouda
Seminar talk at École des Ponts ParisTech about our recently published work "Hierarchical adaptive sparse grids and quasi-Monte Carlo for option pricing under the rough Bergomi model". - Link of the paper: https://www.tandfonline.com/doi/abs/10.1080/14697688.2020.1744700
SOLVING BVPs OF SINGULARLY PERTURBED DISCRETE SYSTEMSTahia ZERIZER
In this article, we study boundary value problems of a large
class of non-linear discrete systems at two-time-scales. Algorithms are given to implement asymptotic solutions for any order of approximation.
Abstract. Regulations of the market require disclosure of information about the nature and extent of risks arising from the trades of the market instruments. There are several significant drawbacks in fixed income pricing modeling. In this paper we interpret a corporate bond price as a random variable. In this case the spot price does not a complete characteristic of the price. The price should be specified by the spot price as well as its value of market risk. This interpretation is similar to a random variable in Probability Theory where an estimate of the random variable completely defined by its cumulative distribution function. The buyer market risk is the value of the chance that the spot price is higher than it is implied by the market scenarios. First we quantify credit risk of the corporate bonds and then consider marked-to-market pricing adjustment to bond price. In the case when issuer of the corporate bond is the counterparty of the bond buyer counterparty and credit risks are coincide.
Hierarchical Deterministic Quadrature Methods for Option Pricing under the Ro...Chiheb Ben Hammouda
Seminar talk at École des Ponts ParisTech about our recently published work "Hierarchical adaptive sparse grids and quasi-Monte Carlo for option pricing under the rough Bergomi model". - Link of the paper: https://www.tandfonline.com/doi/abs/10.1080/14697688.2020.1744700
SOLVING BVPs OF SINGULARLY PERTURBED DISCRETE SYSTEMSTahia ZERIZER
In this article, we study boundary value problems of a large
class of non-linear discrete systems at two-time-scales. Algorithms are given to implement asymptotic solutions for any order of approximation.
In this short notice, we present structure of the perfect hedging. Closed form formulas clarify the fact that Black-Scholes (BS) portfolio which provides perfect hedge only at initial moment. Holding portfolio over a certain period implies additional cash flow, which could not be imbedded in BS pricing scheme, and therefore BS option price cannot be derived without additional cash flow which affects BS option price.
There are several significant drawbacks in derivative price modeling which relate to global regulations of the derivatives market. Here we present a unified approach which in stochastic market interprets option price as a random variable. Therefore spot price does not complete characteristic of the price in stochastic environment. Complete derivatives price includes the spot price as well as thevalue of market risk implied by the use of the spot price. This interpretation is similar to the notion of therandom variable in Probability Theory in which an estimate of the random variable completely defined by its cumulative distribution function
In this short notice, we present structure of the perfect hedging. Closed form formulas clarify the fact that Black-Scholes (BS) portfolio which provides perfect hedge only at initial moment. Holding portfolio over a certain period implies additional cash flow, which could not be imbedded in BS pricing scheme, and therefore BS option price cannot be derived without additional cash flow which affects BS option price.
Last my paper equity, implied, and local volatilitiesIlya Gikhman
In this paper we present a critical point on connections between stock volatility, implied
volatility, and local volatility. The essence of the Black Scholes pricing model is based on assumption
that option piece is formed by no arbitrage portfolio. Such assumption effects the change of the real
underlying stock by its risk neutral counterpart. Market practice shows even more. The volatility of the
underlying should be also changed. Such practice calls for implied volatility. Underlying with implied
volatility is specific for each option. The local volatility development presents the value of implied
volatility.
In this paper, we present somewhat alternative point of view on early exercised American options. The standard valuation of the American options the exercise moment is defined as one, which guarantees the maximum value of the option. We discuss the standard approach in the first two sections of the paper. The standard approach was initially presented in the papers [3] - [7]. Our idea is that the exercise moment of the American call / put options is defined by maximum / minimum value of underlying. It was shown that at this moment exercise and sell prices are equal.
We start with motivation, few examples of uncertainties. Then we discretize elliptic PDE with uncertain coefficients, apply TT format for permeability, the stochastic operator and for the solution. We compare sparse multi-index set approach with full multi-index+TT.
Tensor Train format allows us to keep the whole multi-index set, without any multi-index set truncation.
Stochastic Local Volatility Models: Theory and ImplementationVolatility
1) Hedging and volatility
2) Review of volatility models
3) Local volatility models with jumps and stochastic volatility
4) Calibration using Kolmogorov equations
5) PDE based methods in one dimension
5) PDE based methods in two dimensions
7) Illustrations
Adomian decomposition method for analytical solution of a continuous arithmet...TELKOMNIKA JOURNAL
One of the main issues of concern in financial mathematics has been a viable method for
obtaining analytical solutions of the Black-Scholes model associated with Arithmetic Asian Option (AAO).
In this paper, a proposed semi-analytical technique: Adomian Decomposition Method (ADM) is applied for
the first time, for analytical solution of a continuous arithmetic Asian option model. The ADM gives the
solution in explicit form with few iterations. The computational work involved is less. However, high level of
accuracy is not neglected. The obtained solution conforms with those of Rogers and Shi (J. of Applied
Probability 32: 1995, 1077-1088), and Elshegmani and Ahmad (ScienceAsia, 39S: 2013, 67–69). Thus, the
proposed method is highly recommended for analytical solution of other versions of Asian option pricing
models such as the geometric form for puts and calls, even in their time-fractional forms.
Different analytical and numerical methods are commonly used to solve transient heat conduction problems. In this problem, the use of Alternating Direct Implicit scheme (ADI) was adopted to solve temperature variation within an infinitesimal long bar of a square cross-section. The bottom right quadrant of the square cross-section of the bar was selected. The surface of the bar was maintained at constant temperature and temperature variation within the bar was evaluated within a time frame. The Laplace equation governing the 2-dimesional heat conduction was solved by iterative schemes as a result of the time variation. The modelled problem using COMSOL-MULTIPHYSICS software validated the result of the ADI analysis. On comparing the Modelled results from COMSOL MULTIPHYSICS and the results from ADI iterative scheme graphically, there was an high level of agreement between both results.
In this short notice, we present structure of the perfect hedging. Closed form formulas clarify the fact that Black-Scholes (BS) portfolio which provides perfect hedge only at initial moment. Holding portfolio over a certain period implies additional cash flow, which could not be imbedded in BS pricing scheme, and therefore BS option price cannot be derived without additional cash flow which affects BS option price.
There are several significant drawbacks in derivative price modeling which relate to global regulations of the derivatives market. Here we present a unified approach which in stochastic market interprets option price as a random variable. Therefore spot price does not complete characteristic of the price in stochastic environment. Complete derivatives price includes the spot price as well as thevalue of market risk implied by the use of the spot price. This interpretation is similar to the notion of therandom variable in Probability Theory in which an estimate of the random variable completely defined by its cumulative distribution function
In this short notice, we present structure of the perfect hedging. Closed form formulas clarify the fact that Black-Scholes (BS) portfolio which provides perfect hedge only at initial moment. Holding portfolio over a certain period implies additional cash flow, which could not be imbedded in BS pricing scheme, and therefore BS option price cannot be derived without additional cash flow which affects BS option price.
Last my paper equity, implied, and local volatilitiesIlya Gikhman
In this paper we present a critical point on connections between stock volatility, implied
volatility, and local volatility. The essence of the Black Scholes pricing model is based on assumption
that option piece is formed by no arbitrage portfolio. Such assumption effects the change of the real
underlying stock by its risk neutral counterpart. Market practice shows even more. The volatility of the
underlying should be also changed. Such practice calls for implied volatility. Underlying with implied
volatility is specific for each option. The local volatility development presents the value of implied
volatility.
In this paper, we present somewhat alternative point of view on early exercised American options. The standard valuation of the American options the exercise moment is defined as one, which guarantees the maximum value of the option. We discuss the standard approach in the first two sections of the paper. The standard approach was initially presented in the papers [3] - [7]. Our idea is that the exercise moment of the American call / put options is defined by maximum / minimum value of underlying. It was shown that at this moment exercise and sell prices are equal.
We start with motivation, few examples of uncertainties. Then we discretize elliptic PDE with uncertain coefficients, apply TT format for permeability, the stochastic operator and for the solution. We compare sparse multi-index set approach with full multi-index+TT.
Tensor Train format allows us to keep the whole multi-index set, without any multi-index set truncation.
Stochastic Local Volatility Models: Theory and ImplementationVolatility
1) Hedging and volatility
2) Review of volatility models
3) Local volatility models with jumps and stochastic volatility
4) Calibration using Kolmogorov equations
5) PDE based methods in one dimension
5) PDE based methods in two dimensions
7) Illustrations
Adomian decomposition method for analytical solution of a continuous arithmet...TELKOMNIKA JOURNAL
One of the main issues of concern in financial mathematics has been a viable method for
obtaining analytical solutions of the Black-Scholes model associated with Arithmetic Asian Option (AAO).
In this paper, a proposed semi-analytical technique: Adomian Decomposition Method (ADM) is applied for
the first time, for analytical solution of a continuous arithmetic Asian option model. The ADM gives the
solution in explicit form with few iterations. The computational work involved is less. However, high level of
accuracy is not neglected. The obtained solution conforms with those of Rogers and Shi (J. of Applied
Probability 32: 1995, 1077-1088), and Elshegmani and Ahmad (ScienceAsia, 39S: 2013, 67–69). Thus, the
proposed method is highly recommended for analytical solution of other versions of Asian option pricing
models such as the geometric form for puts and calls, even in their time-fractional forms.
Different analytical and numerical methods are commonly used to solve transient heat conduction problems. In this problem, the use of Alternating Direct Implicit scheme (ADI) was adopted to solve temperature variation within an infinitesimal long bar of a square cross-section. The bottom right quadrant of the square cross-section of the bar was selected. The surface of the bar was maintained at constant temperature and temperature variation within the bar was evaluated within a time frame. The Laplace equation governing the 2-dimesional heat conduction was solved by iterative schemes as a result of the time variation. The modelled problem using COMSOL-MULTIPHYSICS software validated the result of the ADI analysis. On comparing the Modelled results from COMSOL MULTIPHYSICS and the results from ADI iterative scheme graphically, there was an high level of agreement between both results.
Global Derivatives 2014 - Did Basel put the final nail in the coffin of CSA D...Alexandre Bon
FVA in presence of stochastic funding spreads, Inititial Margins and imperfect collateralisation conditions.
Since the birth of CSA discounting during the GFC, major regulatory changes have been reshaping collateral practices in a way that challenges the fundamental assumptions of the method.
Agenda:
- FVA for economic value & incremental pricing
- FVA via CSA discounting or Exposure simulation
- Funding spreads and exposure co-dependence
- Collateralisation regimes in the New Normal and Initial Margins
RiskMinds - Did Basel & IOSCO put the final nail in the coffin of CSA-discoun...Alexandre Bon
FVA in presence of stochastic funding spreads, Inititial Margins and imperfect collateralisation conditions.
Since the birth of CSA discounting during the GFC, major regulatory changes have been reshaping collateral practices in a way that challenges the fundamental assumptions of the method.
Agenda:
- FVA via CSA discounting or Exposure simulation
- Funding spreads and exposure co-dependence
- Collateralisation regimes in the New Normal and Initial Margins
- FVA/MVA for VaR-based IMs and the SBA-M
- FVA for economic value & incremental pricing
Long horizon simulations for counterparty risk Alexandre Bon
The Challenges of Long Horizon Simulations in the context of Counterparty Risk modeling : CVA, PFE and Regulatory reporting.
This joint presentation reviews the key decisions that need making regarding the choice of risk factor evolution models and calibration methods. In particular, we will analyse the performance of classical historical calibration methods (such as Maximum Likelihood and the Efficient Method of Moments) in estimating the volatility and drift terms of the Hull & White class of Interest Rate models ; both in terms of convergence and stability.
As most methods perform satisfactorily for volatility but disappoint on the mean reversion estimation, we propose a new modified Variance Estimation method that significantly outperform the classical approaches.
Lastly, after reviewing historical economic evidence of mean-reversion dynmics in high interest rate regime, we propose modifying classical models by making mean reversion non-linear and accelerating for high rates - that can be referred as "+R" models.
This model address unrealistically large and persistent interest rates values often observed at high quantile in PFE and CVA simulations.
Solution to Black-Scholes P.D.E. via Finite Difference Methods (MatLab)Fynn McKay
Simple implementable of Numerical Analysis to solve the famous Black-Scholes P.D.E. via Finite Difference Methods for the fair price of a European option.
Impact of Valuation Adjustments (CVA, DVA, FVA, KVA) on Bank's Processes - An...Andrea Gigli
The talk hold in London on September 10th at the 5th Annual XVA Forum on Funding, Capital and Valuation. It covered some implications of Valuation Adjustments like CVA, DVA, FVA and KVA (XVAs) in the Pricing of Derivatives, Data Model Definition, Risk Management, Accounting, Trade Workflow processing.
Option Pricing under non constant volatilityEcon 643 Fina.docxjacksnathalie
Option Pricing under non constant volatility
Econ 643: Financial Economics II
Econ 643: Financial Economics II Non constant volatility 1 / 21
Department of Economics
Introduction
Attempts have been made to fix option pricing puzzles: How to be
consistent with volatility smile and smirk.
The Gram-Charlier expansion is one of then but volatility is constant
which is inconsistent with asset return’s dynamics
We review thre approaches that aim at integrating information
embedded in past returns:
GARCH type of approach,
Stochastic volatility models: Hull and White (1987),
Stochastic volatility models: Heston (1993),
Econ 643: Financial Economics II Non constant volatility 2 / 21
The GARCH option pricing
Let St be the asset price at time t and rt = ln(St/St−1) be the log-return
process. Assume that the process rt is a (G)GARCH(1,1) process:
rt = ln
(
St
St−1
)
= µt−1 + σt−1zt, zt ∼ NID(0, 1)
σ2t = ω + α(σt−1zt − θσt−1)2 + βσ2t−1.
(1)
In this model,
µt−1 = E(rt|Jt−1) is a known function of past returns.
Ex: µt = 0, µt = µ = cst, µt = µ + λσt, µt = r + λσt − 12σ
2
t , etc.
σ2t−1 = Var(rt|Jt−1) is the conditional variance of rt given the
information Jt−1 available at t − 1.
Econ 643: Financial Economics II Non constant volatility 3 / 21
GARCH: How to price options on S?
We can rely on the risk-neutral approach:
C = e−rτ E∗ (max(ST − X, 0)) ,
where E∗ is the expectation under risk-neutral dynamics.
What is the risk-neutral dynamics of St if ln(St/St−1) is a
GARCH(1,1)?
Under risk-neutral dyn., E∗
(
St
St−1
)
= er and Var∗(rt|Jt−1) = σ2t−1
(same as under historical measure). Hence, if rt ∼ GARCH(1, 1)
under risk-neutral, the corresponding mean has to be
µ∗t−1 = r −
σ2
t−1
2
. That is:
rt = r −
σ2
t−1
2
+ σt−1z
∗
t , z
∗
t ∼ NID(0, 1)
σ2t = ω + α(σt−1z
∗
t + r −
σ2
t−1
2
− µt−1 − θσt−1)2 + βσ2t−1.
(2)
Econ 643: Financial Economics II Non constant volatility 4 / 21
GARCH: Simulating the option price
To obtain the price C by simulation:
Simulate B paths of stock price using the risk-neutral dynamics (2):
(S
(b)
t+1, S
(b)
t+2, . . . , S
(b)
T
) for b = 1, . . . , B (e.g. B = 5000).
Obtain the simulated price as
Ĉ = e−rτ Ê(max(ST − X, 0)),
with
Ê(max(ST − X, 0)) =
1
B
B
∑
b=1
max(S
(b)
T
− X, 0).
Econ 643: Financial Economics II Non constant volatility 5 / 21
Option pricing under stochastic volatility
GARCH option pricing is convenient but evidence are out that
volatility is more likely stochastic.
Option pricing under SV is quite challenging because of the extra
source of uncertainty brought by the volatility equation.
The induced PDE (by SV) for option pricing can be derived but is
hard to solve.
The most common SV option pricing models are from Hull and White
(1987) and Heston (1993).
Econ 643: Financial Economics II Non constant volatility 6 / 21
Hull and White (1987)
Consider the price process St and its instantaneous variance process
V − t = σ2t obeying the dynamics:
dS.
Computational Tools and Techniques for Numerical Macro-Financial ModelingVictor Zhorin
A set of numerical tools used to create and analyze non-linear macroeconomic models with financial sector is discussed. New methods and results for computing Hansen-Scheinkman-Borovicka shock-price and shock-exposure elasticities for variety of models are presented. Spectral approximation technology (chebfun):
numerical computation in Chebyshev functions piece-wise smooth functions
breakpoints detection
rootfinding
functions with singularities
fast adaptive quadratures continuous QR, SVD, least-squares linear operators
solution of linear and non-linear ODE
Frechet derivatives via automatic differentiation PDEs in one space variable plus time
Stochastic processes:
(quazi) Monte-Carlo simulations, Polynomial Expansion (gPC), finite-differences (FD) non-linear IRF
Boroviˇcka-Hansen-Sc[heinkman shock-exposure and shock-price elasticities Malliavin derivatives
Many states:
Dimensionality Curse Cure
low-rank tensor decomposition
sparse Smolyak grids
extreme times in finance heston model.pptArounaGanou2
Stochastic Volatility Models. 3. I - CTRW formalism. First developed by Montroll and Weiss (1965); Aimed to study the microstructure of random processe
Pricing average price advertising options when underlying spot market prices ...Bowei Chen
Advertising options have been recently studied as a special type of guaranteed contracts in online advertising, which are an alternative sales mechanism to real-time auctions. An advertising option is a contract which gives its buyer a right but not obligation to enter into transactions to purchase page views or link clicks at one or multiple pre-specified prices in a specific future period. Different from typical guaranteed contracts, the option buyer pays a lower upfront fee but can have greater flexibility and more control of advertising. Many studies on advertising options so far have been restricted to the situations where the option payoff is determined by the underlying spot market price at a specific time point and the price evolution over time is assumed to be continuous. The former leads to a biased calculation of option payoff and the latter is invalid empirically for many online advertising slots. This paper addresses these two limitations by proposing a new advertising option pricing framework. First, the option payoff is calculated based on an average price over a specific future period. Therefore, the option becomes path-dependent. The average price is measured by the power mean, which contains several existing option payoff functions as its special cases. Second, jump-diffusion stochastic models are used to describe the movement of the underlying spot market price, which incorporate several important statistical properties including jumps and spikes, non-normality, and absence of autocorrelations. A general option pricing algorithm is obtained based on Monte Carlo simulation. In addition, an explicit pricing formula is derived for the case when the option payoff is based on the geometric mean. This pricing formula is also a generalized version of several other option pricing models discussed in related studies.
Similar to Workshop 2013 of Quantitative Finance (20)
The European Unemployment Puzzle: implications from population agingGRAPE
We study the link between the evolving age structure of the working population and unemployment. We build a large new Keynesian OLG model with a realistic age structure, labor market frictions, sticky prices, and aggregate shocks. Once calibrated to the European economy, we quantify the extent to which demographic changes over the last three decades have contributed to the decline of the unemployment rate. Our findings yield important implications for the future evolution of unemployment given the anticipated further aging of the working population in Europe. We also quantify the implications for optimal monetary policy: lowering inflation volatility becomes less costly in terms of GDP and unemployment volatility, which hints that optimal monetary policy may be more hawkish in an aging society. Finally, our results also propose a partial reversal of the European-US unemployment puzzle due to the fact that the share of young workers is expected to remain robust in the US.
what is the future of Pi Network currency.DOT TECH
The future of the Pi cryptocurrency is uncertain, and its success will depend on several factors. Pi is a relatively new cryptocurrency that aims to be user-friendly and accessible to a wide audience. Here are a few key considerations for its future:
Message: @Pi_vendor_247 on telegram if u want to sell PI COINS.
1. Mainnet Launch: As of my last knowledge update in January 2022, Pi was still in the testnet phase. Its success will depend on a successful transition to a mainnet, where actual transactions can take place.
2. User Adoption: Pi's success will be closely tied to user adoption. The more users who join the network and actively participate, the stronger the ecosystem can become.
3. Utility and Use Cases: For a cryptocurrency to thrive, it must offer utility and practical use cases. The Pi team has talked about various applications, including peer-to-peer transactions, smart contracts, and more. The development and implementation of these features will be essential.
4. Regulatory Environment: The regulatory environment for cryptocurrencies is evolving globally. How Pi navigates and complies with regulations in various jurisdictions will significantly impact its future.
5. Technology Development: The Pi network must continue to develop and improve its technology, security, and scalability to compete with established cryptocurrencies.
6. Community Engagement: The Pi community plays a critical role in its future. Engaged users can help build trust and grow the network.
7. Monetization and Sustainability: The Pi team's monetization strategy, such as fees, partnerships, or other revenue sources, will affect its long-term sustainability.
It's essential to approach Pi or any new cryptocurrency with caution and conduct due diligence. Cryptocurrency investments involve risks, and potential rewards can be uncertain. The success and future of Pi will depend on the collective efforts of its team, community, and the broader cryptocurrency market dynamics. It's advisable to stay updated on Pi's development and follow any updates from the official Pi Network website or announcements from the team.
how can i use my minded pi coins I need some funds.DOT TECH
If you are interested in selling your pi coins, i have a verified pi merchant, who buys pi coins and resell them to exchanges looking forward to hold till mainnet launch.
Because the core team has announced that pi network will not be doing any pre-sale. The only way exchanges like huobi, bitmart and hotbit can get pi is by buying from miners.
Now a merchant stands in between these exchanges and the miners. As a link to make transactions smooth. Because right now in the enclosed mainnet you can't sell pi coins your self. You need the help of a merchant,
i will leave the telegram contact of my personal pi merchant below. 👇 I and my friends has traded more than 3000pi coins with him successfully.
@Pi_vendor_247
how to sell pi coins at high rate quickly.DOT TECH
Where can I sell my pi coins at a high rate.
Pi is not launched yet on any exchange. But one can easily sell his or her pi coins to investors who want to hold pi till mainnet launch.
This means crypto whales want to hold pi. And you can get a good rate for selling pi to them. I will leave the telegram contact of my personal pi vendor below.
A vendor is someone who buys from a miner and resell it to a holder or crypto whale.
Here is the telegram contact of my vendor:
@Pi_vendor_247
where can I find a legit pi merchant onlineDOT TECH
Yes. This is very easy what you need is a recommendation from someone who has successfully traded pi coins before with a merchant.
Who is a pi merchant?
A pi merchant is someone who buys pi network coins and resell them to Investors looking forward to hold thousands of pi coins before the open mainnet.
I will leave the telegram contact of my personal pi merchant to trade with
@Pi_vendor_247
USDA Loans in California: A Comprehensive Overview.pptxmarketing367770
USDA Loans in California: A Comprehensive Overview
If you're dreaming of owning a home in California's rural or suburban areas, a USDA loan might be the perfect solution. The U.S. Department of Agriculture (USDA) offers these loans to help low-to-moderate-income individuals and families achieve homeownership.
Key Features of USDA Loans:
Zero Down Payment: USDA loans require no down payment, making homeownership more accessible.
Competitive Interest Rates: These loans often come with lower interest rates compared to conventional loans.
Flexible Credit Requirements: USDA loans have more lenient credit score requirements, helping those with less-than-perfect credit.
Guaranteed Loan Program: The USDA guarantees a portion of the loan, reducing risk for lenders and expanding borrowing options.
Eligibility Criteria:
Location: The property must be located in a USDA-designated rural or suburban area. Many areas in California qualify.
Income Limits: Applicants must meet income guidelines, which vary by region and household size.
Primary Residence: The home must be used as the borrower's primary residence.
Application Process:
Find a USDA-Approved Lender: Not all lenders offer USDA loans, so it's essential to choose one approved by the USDA.
Pre-Qualification: Determine your eligibility and the amount you can borrow.
Property Search: Look for properties in eligible rural or suburban areas.
Loan Application: Submit your application, including financial and personal information.
Processing and Approval: The lender and USDA will review your application. If approved, you can proceed to closing.
USDA loans are an excellent option for those looking to buy a home in California's rural and suburban areas. With no down payment and flexible requirements, these loans make homeownership more attainable for many families. Explore your eligibility today and take the first step toward owning your dream home.
Poonawalla Fincorp and IndusInd Bank Introduce New Co-Branded Credit Cardnickysharmasucks
The unveiling of the IndusInd Bank Poonawalla Fincorp eLITE RuPay Platinum Credit Card marks a notable milestone in the Indian financial landscape, showcasing a successful partnership between two leading institutions, Poonawalla Fincorp and IndusInd Bank. This co-branded credit card not only offers users a plethora of benefits but also reflects a commitment to innovation and adaptation. With a focus on providing value-driven and customer-centric solutions, this launch represents more than just a new product—it signifies a step towards redefining the banking experience for millions. Promising convenience, rewards, and a touch of luxury in everyday financial transactions, this collaboration aims to cater to the evolving needs of customers and set new standards in the industry.
Falcon stands out as a top-tier P2P Invoice Discounting platform in India, bridging esteemed blue-chip companies and eager investors. Our goal is to transform the investment landscape in India by establishing a comprehensive destination for borrowers and investors with diverse profiles and needs, all while minimizing risk. What sets Falcon apart is the elimination of intermediaries such as commercial banks and depository institutions, allowing investors to enjoy higher yields.
Currently pi network is not tradable on binance or any other exchange because we are still in the enclosed mainnet.
Right now the only way to sell pi coins is by trading with a verified merchant.
What is a pi merchant?
A pi merchant is someone verified by pi network team and allowed to barter pi coins for goods and services.
Since pi network is not doing any pre-sale The only way exchanges like binance/huobi or crypto whales can get pi is by buying from miners. And a merchant stands in between the exchanges and the miners.
I will leave the telegram contact of my personal pi merchant. I and my friends has traded more than 6000pi coins successfully
Tele-gram
@Pi_vendor_247
What website can I sell pi coins securely.DOT TECH
Currently there are no website or exchange that allow buying or selling of pi coins..
But you can still easily sell pi coins, by reselling it to exchanges/crypto whales interested in holding thousands of pi coins before the mainnet launch.
Who is a pi merchant?
A pi merchant is someone who buys pi coins from miners and resell to these crypto whales and holders of pi..
This is because pi network is not doing any pre-sale. The only way exchanges can get pi is by buying from miners and pi merchants stands in between the miners and the exchanges.
How can I sell my pi coins?
Selling pi coins is really easy, but first you need to migrate to mainnet wallet before you can do that. I will leave the telegram contact of my personal pi merchant to trade with.
Tele-gram.
@Pi_vendor_247
What price will pi network be listed on exchangesDOT TECH
The rate at which pi will be listed is practically unknown. But due to speculations surrounding it the predicted rate is tends to be from 30$ — 50$.
So if you are interested in selling your pi network coins at a high rate tho. Or you can't wait till the mainnet launch in 2026. You can easily trade your pi coins with a merchant.
A merchant is someone who buys pi coins from miners and resell them to Investors looking forward to hold massive quantities till mainnet launch.
I will leave the telegram contact of my personal pi vendor to trade with.
@Pi_vendor_247
US Economic Outlook - Being Decided - M Capital Group August 2021.pdfpchutichetpong
The U.S. economy is continuing its impressive recovery from the COVID-19 pandemic and not slowing down despite re-occurring bumps. The U.S. savings rate reached its highest ever recorded level at 34% in April 2020 and Americans seem ready to spend. The sectors that had been hurt the most by the pandemic specifically reduced consumer spending, like retail, leisure, hospitality, and travel, are now experiencing massive growth in revenue and job openings.
Could this growth lead to a “Roaring Twenties”? As quickly as the U.S. economy contracted, experiencing a 9.1% drop in economic output relative to the business cycle in Q2 2020, the largest in recorded history, it has rebounded beyond expectations. This surprising growth seems to be fueled by the U.S. government’s aggressive fiscal and monetary policies, and an increase in consumer spending as mobility restrictions are lifted. Unemployment rates between June 2020 and June 2021 decreased by 5.2%, while the demand for labor is increasing, coupled with increasing wages to incentivize Americans to rejoin the labor force. Schools and businesses are expected to fully reopen soon. In parallel, vaccination rates across the country and the world continue to rise, with full vaccination rates of 50% and 14.8% respectively.
However, it is not completely smooth sailing from here. According to M Capital Group, the main risks that threaten the continued growth of the U.S. economy are inflation, unsettled trade relations, and another wave of Covid-19 mutations that could shut down the world again. Have we learned from the past year of COVID-19 and adapted our economy accordingly?
“In order for the U.S. economy to continue growing, whether there is another wave or not, the U.S. needs to focus on diversifying supply chains, supporting business investment, and maintaining consumer spending,” says Grace Feeley, a research analyst at M Capital Group.
While the economic indicators are positive, the risks are coming closer to manifesting and threatening such growth. The new variants spreading throughout the world, Delta, Lambda, and Gamma, are vaccine-resistant and muddy the predictions made about the economy and health of the country. These variants bring back the feeling of uncertainty that has wreaked havoc not only on the stock market but the mindset of people around the world. MCG provides unique insight on how to mitigate these risks to possibly ensure a bright economic future.
Turin Startup Ecosystem 2024 - Ricerca sulle Startup e il Sistema dell'Innov...Quotidiano Piemontese
Turin Startup Ecosystem 2024
Una ricerca de il Club degli Investitori, in collaborazione con ToTeM Torino Tech Map e con il supporto della ESCP Business School e di Growth Capital
how to sell pi coins in South Korea profitably.DOT TECH
Yes. You can sell your pi network coins in South Korea or any other country, by finding a verified pi merchant
What is a verified pi merchant?
Since pi network is not launched yet on any exchange, the only way you can sell pi coins is by selling to a verified pi merchant, and this is because pi network is not launched yet on any exchange and no pre-sale or ico offerings Is done on pi.
Since there is no pre-sale, the only way exchanges can get pi is by buying from miners. So a pi merchant facilitates these transactions by acting as a bridge for both transactions.
How can i find a pi vendor/merchant?
Well for those who haven't traded with a pi merchant or who don't already have one. I will leave the telegram id of my personal pi merchant who i trade pi with.
Tele gram: @Pi_vendor_247
#pi #sell #nigeria #pinetwork #picoins #sellpi #Nigerian #tradepi #pinetworkcoins #sellmypi
how to swap pi coins to foreign currency withdrawable.DOT TECH
As of my last update, Pi is still in the testing phase and is not tradable on any exchanges.
However, Pi Network has announced plans to launch its Testnet and Mainnet in the future, which may include listing Pi on exchanges.
The current method for selling pi coins involves exchanging them with a pi vendor who purchases pi coins for investment reasons.
If you want to sell your pi coins, reach out to a pi vendor and sell them to anyone looking to sell pi coins from any country around the globe.
Below is the contact information for my personal pi vendor.
Telegram: @Pi_vendor_247
how to swap pi coins to foreign currency withdrawable.
Workshop 2013 of Quantitative Finance
1. Quantitative Finance: stochastic volatility market models
Closed Solution for Heston PDE by
Geometrical Transformations
XIV WorkShop of Quantitative Finance
Mario Dell’Era
Pisa University
June 24, 2014
Mario Dell’Era Closed Solution for Heston PDE by Geometrical Transformations
2. Quantitative Finance: stochastic volatility market models
Heston Model
dSt = rSt dt +
√
νt St d ˜W
(1)
t S ∈ [0, +∞)
dνt = K(Θ − νt )dt + α
√
νt d ˜W
(2)
t ν ∈ (0, +∞)
under a risk-neutral martingale measure Q.
From Itˆo’s lemma we have the following PDE:
∂f
∂t
+
1
2
νS
2 ∂2
f
∂S2
+ ρναS
∂2
f
∂S∂ν
+
1
2
να
2 ∂2
f
∂ν2
+ κ(Θ − ν)
∂f
∂ν
+ rS
∂f
∂S
− rf = 0
Mario Dell’Era Closed Solution for Heston PDE by Geometrical Transformations
3. Quantitative Finance: stochastic volatility market models
Heston Model
dSt = rSt dt +
√
νt St d ˜W
(1)
t S ∈ [0, +∞)
dνt = K(Θ − νt )dt + α
√
νt d ˜W
(2)
t ν ∈ (0, +∞)
under a risk-neutral martingale measure Q.
From Itˆo’s lemma we have the following PDE:
∂f
∂t
+
1
2
νS
2 ∂2
f
∂S2
+ ρναS
∂2
f
∂S∂ν
+
1
2
να
2 ∂2
f
∂ν2
+ κ(Θ − ν)
∂f
∂ν
+ rS
∂f
∂S
− rf = 0
Mario Dell’Era Closed Solution for Heston PDE by Geometrical Transformations
4. Quantitative Finance: stochastic volatility market models
Numerical methods
(1) Fourier Transform: S.L. Heston (1993)
(2) Finite Difference: T. Kluge (2002)
(3) Monte Carlo: B. Jourdain (2005)
Approximation method
(1) Analytic and Geometric Methods for Heat Kernel: I. Avramidi
(2007)
(2) Implied Volatility: M. Forde, A. Jacquier (2009)
(3) Geometrical Approximation method: M. Dell’Era (2010)
Mario Dell’Era Closed Solution for Heston PDE by Geometrical Transformations
5. Quantitative Finance: stochastic volatility market models
Numerical methods
(1) Fourier Transform: S.L. Heston (1993)
(2) Finite Difference: T. Kluge (2002)
(3) Monte Carlo: B. Jourdain (2005)
Approximation method
(1) Analytic and Geometric Methods for Heat Kernel: I. Avramidi
(2007)
(2) Implied Volatility: M. Forde, A. Jacquier (2009)
(3) Geometrical Approximation method: M. Dell’Era (2010)
Mario Dell’Era Closed Solution for Heston PDE by Geometrical Transformations
6. Quantitative Finance: stochastic volatility market models
Numerical methods
(1) Fourier Transform: S.L. Heston (1993)
(2) Finite Difference: T. Kluge (2002)
(3) Monte Carlo: B. Jourdain (2005)
Approximation method
(1) Analytic and Geometric Methods for Heat Kernel: I. Avramidi
(2007)
(2) Implied Volatility: M. Forde, A. Jacquier (2009)
(3) Geometrical Approximation method: M. Dell’Era (2010)
Mario Dell’Era Closed Solution for Heston PDE by Geometrical Transformations
7. Quantitative Finance: stochastic volatility market models
Numerical methods
(1) Fourier Transform: S.L. Heston (1993)
(2) Finite Difference: T. Kluge (2002)
(3) Monte Carlo: B. Jourdain (2005)
Approximation method
(1) Analytic and Geometric Methods for Heat Kernel: I. Avramidi
(2007)
(2) Implied Volatility: M. Forde, A. Jacquier (2009)
(3) Geometrical Approximation method: M. Dell’Era (2010)
Mario Dell’Era Closed Solution for Heston PDE by Geometrical Transformations
8. Quantitative Finance: stochastic volatility market models
Numerical methods
(1) Fourier Transform: S.L. Heston (1993)
(2) Finite Difference: T. Kluge (2002)
(3) Monte Carlo: B. Jourdain (2005)
Approximation method
(1) Analytic and Geometric Methods for Heat Kernel: I. Avramidi
(2007)
(2) Implied Volatility: M. Forde, A. Jacquier (2009)
(3) Geometrical Approximation method: M. Dell’Era (2010)
Mario Dell’Era Closed Solution for Heston PDE by Geometrical Transformations
9. Quantitative Finance: stochastic volatility market models
Numerical methods
(1) Fourier Transform: S.L. Heston (1993)
(2) Finite Difference: T. Kluge (2002)
(3) Monte Carlo: B. Jourdain (2005)
Approximation method
(1) Analytic and Geometric Methods for Heat Kernel: I. Avramidi
(2007)
(2) Implied Volatility: M. Forde, A. Jacquier (2009)
(3) Geometrical Approximation method: M. Dell’Era (2010)
Mario Dell’Era Closed Solution for Heston PDE by Geometrical Transformations
10. Quantitative Finance: stochastic volatility market models
Numerical methods
(1) Fourier Transform: S.L. Heston (1993)
(2) Finite Difference: T. Kluge (2002)
(3) Monte Carlo: B. Jourdain (2005)
Approximation method
(1) Analytic and Geometric Methods for Heat Kernel: I. Avramidi
(2007)
(2) Implied Volatility: M. Forde, A. Jacquier (2009)
(3) Geometrical Approximation method: M. Dell’Era (2010)
Mario Dell’Era Closed Solution for Heston PDE by Geometrical Transformations
11. Quantitative Finance: stochastic volatility market models
Numerical methods
(1) Fourier Transform: S.L. Heston (1993)
(2) Finite Difference: T. Kluge (2002)
(3) Monte Carlo: B. Jourdain (2005)
Approximation method
(1) Analytic and Geometric Methods for Heat Kernel: I. Avramidi
(2007)
(2) Implied Volatility: M. Forde, A. Jacquier (2009)
(3) Geometrical Approximation method: M. Dell’Era (2010)
Mario Dell’Era Closed Solution for Heston PDE by Geometrical Transformations
12. Quantitative Finance: stochastic volatility market models
Coordinate Transformations technique
We have elaborated a new methodology based on changing of variables
which is independent of payoffs and does not need to use the inverse Fourier
transform algorithm or numerical methods as Finite Difference and Monte
Carlo simulations. In particular, we will compute the price of Vanilla Options,
in order to validate numerically the Geometrical Transformations technique.
Mario Dell’Era Closed Solution for Heston PDE by Geometrical Transformations
13. Quantitative Finance: stochastic volatility market models
1st
Transformations:
8
><
>:
x = ln S, x ∈ (−∞, +∞)
˜ν = ν/α, ˜ν ∈ [0, +∞)
f(t, S, ν) = f1(t, x, ˜ν)e−r(T−t)
(1)
thus one has:
∂f1
∂t
+
1
2
˜ν
∂2
f1
∂x2
+ 2ρ
∂2
f1
∂x∂˜ν
+
∂2
f1
∂˜ν2
!
+
„
r −
1
2
α˜ν
«
∂f1
∂x
+
κ
α
(θ − α˜ν)
∂f1
∂˜ν
= 0
f1(T, x, ˜ν) = Φ1(x) ρ ∈ (−1, +1), α ∈ R
+
x ∈ (−∞, +∞) ˜ν ∈ [0, +∞) t ∈ [0, T]
Mario Dell’Era Closed Solution for Heston PDE by Geometrical Transformations
14. Quantitative Finance: stochastic volatility market models
1st
Transformations:
8
><
>:
x = ln S, x ∈ (−∞, +∞)
˜ν = ν/α, ˜ν ∈ [0, +∞)
f(t, S, ν) = f1(t, x, ˜ν)e−r(T−t)
(1)
thus one has:
∂f1
∂t
+
1
2
˜ν
∂2
f1
∂x2
+ 2ρ
∂2
f1
∂x∂˜ν
+
∂2
f1
∂˜ν2
!
+
„
r −
1
2
α˜ν
«
∂f1
∂x
+
κ
α
(θ − α˜ν)
∂f1
∂˜ν
= 0
f1(T, x, ˜ν) = Φ1(x) ρ ∈ (−1, +1), α ∈ R
+
x ∈ (−∞, +∞) ˜ν ∈ [0, +∞) t ∈ [0, T]
Mario Dell’Era Closed Solution for Heston PDE by Geometrical Transformations
15. Quantitative Finance: stochastic volatility market models
2nd
Transformations:
8
><
>:
ξ = x − ρ˜ν ξ ∈ (−∞, +∞)
η = −˜ν
p
1 − ρ2 η ∈ (−∞, 0]
f1(t, x, ˜ν) = f2(t, ξ, η)
(2)
Again we have:
∂f2
∂t
−
αη
2
p
1 − ρ2
(1 − ρ
2
)
∂2
f2
∂ξ2
+
∂2
f2
∂η2
!
+
αη
2
p
1 − ρ2
„
1 −
2κρ
α
«
∂f2
∂ξ
−
αη
2
p
1 − ρ2
„
2κ
α
p
1 − ρ2
«
∂f2
∂η
+
„
r −
κρθ
α
«
∂f2
∂ξ
−
θκ
α
p
1 − ρ2
∂f2
∂η
= 0
f2(T, ξ, η) = Φ2(ξ, η), ρ ∈ (−1, +1), α ∈ R
+
.
ξ ∈ (−∞, +∞), η ∈ (−∞, 0], t ∈ [0, T].
Mario Dell’Era Closed Solution for Heston PDE by Geometrical Transformations
16. Quantitative Finance: stochastic volatility market models
2nd
Transformations:
8
><
>:
ξ = x − ρ˜ν ξ ∈ (−∞, +∞)
η = −˜ν
p
1 − ρ2 η ∈ (−∞, 0]
f1(t, x, ˜ν) = f2(t, ξ, η)
(2)
Again we have:
∂f2
∂t
−
αη
2
p
1 − ρ2
(1 − ρ
2
)
∂2
f2
∂ξ2
+
∂2
f2
∂η2
!
+
αη
2
p
1 − ρ2
„
1 −
2κρ
α
«
∂f2
∂ξ
−
αη
2
p
1 − ρ2
„
2κ
α
p
1 − ρ2
«
∂f2
∂η
+
„
r −
κρθ
α
«
∂f2
∂ξ
−
θκ
α
p
1 − ρ2
∂f2
∂η
= 0
f2(T, ξ, η) = Φ2(ξ, η), ρ ∈ (−1, +1), α ∈ R
+
.
ξ ∈ (−∞, +∞), η ∈ (−∞, 0], t ∈ [0, T].
Mario Dell’Era Closed Solution for Heston PDE by Geometrical Transformations
17. Quantitative Finance: stochastic volatility market models
3rd
Transformations:
8
>>><
>>>:
γ = ξ +
`
r − κρθ
α
´
(T − t) γ ∈ (−∞, +∞)
φ = −η + κθ
α
p
1 − ρ2(T − t) φ ∈ [0, +∞)
τ = 1
2
R T
t
νsds τ ∈ [0, +∞)
f2(t, ξ, η) = f3(τ, γ, φ)
which give us the following PDE:
∂f3
∂τ
= (1 − ρ
2
)
∂2
f3
∂γ2
+
∂2
f3
∂φ2
!
−
„
1 −
2κρ
α
«
∂f3
∂γ
−
„
2κ
α
p
1 − ρ2
«
∂f3
∂φ
= 0
Mario Dell’Era Closed Solution for Heston PDE by Geometrical Transformations
18. Quantitative Finance: stochastic volatility market models
3rd
Transformations:
8
>>><
>>>:
γ = ξ +
`
r − κρθ
α
´
(T − t) γ ∈ (−∞, +∞)
φ = −η + κθ
α
p
1 − ρ2(T − t) φ ∈ [0, +∞)
τ = 1
2
R T
t
νsds τ ∈ [0, +∞)
f2(t, ξ, η) = f3(τ, γ, φ)
which give us the following PDE:
∂f3
∂τ
= (1 − ρ
2
)
∂2
f3
∂γ2
+
∂2
f3
∂φ2
!
−
„
1 −
2κρ
α
«
∂f3
∂γ
−
„
2κ
α
p
1 − ρ2
«
∂f3
∂φ
= 0
Mario Dell’Era Closed Solution for Heston PDE by Geometrical Transformations
21. Quantitative Finance: stochastic volatility market models
The solution is known in the literature (Andrei D. Polyanin, Handbook of
Linear Partial Differential Equations, 2002, p. 188), and it can be written as
integral, whose kernel G(0, γ , φ |τ, γ, δ) is a bivariate gaussian function:
G(0, γ , φ |τ, γ, φ) =
1
4πτ(1 − ρ2)
2
4e
−
(γ −γ)2+(φ −φ)2
4τ(1−ρ2) − e
−
(γ −γ)2+(φ +φ)2
4τ(1−ρ2)
3
5 ,
therefore
f4(τ, γ, φ) =
Z +∞
0
dφ
Z +∞
−∞
dγ f4(0, γ , φ )G(0, γ , φ |τ, γ, φ)
+ (1 − ρ
2
)
Z τ
0
du
Z +∞
−∞
dγ f4(u, γ , 0)
»
∂G(0, γ , δ |τ − u, γ, δ)
∂φ
–
φ =0
.
Mario Dell’Era Closed Solution for Heston PDE by Geometrical Transformations
22. Quantitative Finance: stochastic volatility market models
The solution is known in the literature (Andrei D. Polyanin, Handbook of
Linear Partial Differential Equations, 2002, p. 188), and it can be written as
integral, whose kernel G(0, γ , φ |τ, γ, δ) is a bivariate gaussian function:
G(0, γ , φ |τ, γ, φ) =
1
4πτ(1 − ρ2)
2
4e
−
(γ −γ)2+(φ −φ)2
4τ(1−ρ2) − e
−
(γ −γ)2+(φ +φ)2
4τ(1−ρ2)
3
5 ,
therefore
f4(τ, γ, φ) =
Z +∞
0
dφ
Z +∞
−∞
dγ f4(0, γ , φ )G(0, γ , φ |τ, γ, φ)
+ (1 − ρ
2
)
Z τ
0
du
Z +∞
−∞
dγ f4(u, γ , 0)
»
∂G(0, γ , δ |τ − u, γ, δ)
∂φ
–
φ =0
.
Mario Dell’Era Closed Solution for Heston PDE by Geometrical Transformations
23. Quantitative Finance: stochastic volatility market models
Using the natural variables we may rewrite the solution as follows:
f(t, S, ν) = e
−r(T−t)+aτ+bγ+cδ
Z +∞
0
dφ
Z +∞
−∞
dγ f4(0, γ , φ )G(0, γ , φ |τ, γ, φ)
+ (1 − ρ
2
)e
−r(T−t)+aτ+bγ+cφ
Z τ
0
du
Z +∞
−∞
dγ f4(u, γ , 0)
×
»
∂G(0, γ , φ |τ − u, γ, φ)
∂φ
–
φ =0
.
Mario Dell’Era Closed Solution for Heston PDE by Geometrical Transformations
24. Quantitative Finance: stochastic volatility market models
Vanilla Option Pricing
In order to test above option pricing formula, we are going to consider as
option a Vanilla Call with strike price K and maturity T. In the new variable the
payoff (ST − K)+
is equal to e−bγ−cφ
(eγ+ρφ/
√
1−ρ2
− K)+
. Substituting this
latter in the above equation we have:
f(t, S, ν) = e
−r(T−t)+aτ+bγ+cφ
×
Z +∞
0
dφ
Z +∞
−∞
dγ e
−bγ −cφ
(e
γ +ρφ /
√
1−ρ2
− K)
+
G(0, γ , φ |τ, γ, φ)
+(1−ρ
2
)e
−r(T−t)+aτ+bγ+cφ
Z τ
0
du
Z +∞
−∞
dγ f4(u, γ , 0)
»
∂G(0, γ , φ |τ − u, γ, φ)
∂φ
–
φ =0
,
Mario Dell’Era Closed Solution for Heston PDE by Geometrical Transformations
25. Quantitative Finance: stochastic volatility market models
Vanilla Option Pricing
In order to test above option pricing formula, we are going to consider as
option a Vanilla Call with strike price K and maturity T. In the new variable the
payoff (ST − K)+
is equal to e−bγ−cφ
(eγ+ρφ/
√
1−ρ2
− K)+
. Substituting this
latter in the above equation we have:
f(t, S, ν) = e
−r(T−t)+aτ+bγ+cφ
×
Z +∞
0
dφ
Z +∞
−∞
dγ e
−bγ −cφ
(e
γ +ρφ /
√
1−ρ2
− K)
+
G(0, γ , φ |τ, γ, φ)
+(1−ρ
2
)e
−r(T−t)+aτ+bγ+cφ
Z τ
0
du
Z +∞
−∞
dγ f4(u, γ , 0)
»
∂G(0, γ , φ |τ − u, γ, φ)
∂φ
–
φ =0
,
Mario Dell’Era Closed Solution for Heston PDE by Geometrical Transformations
26. Quantitative Finance: stochastic volatility market models
Considering the particular case, for τ which goes to zero (i.e T → 0), the
solution reduces itself to:
f(t, St , νt )
= St
»
N
“
−ψ1(0), −a1,1
p
1 − ρ2
”
− e
−2
“
ρ− κ
α
”“ νt
α
+ κ
α
θ(T−t)
”
N
“
−ψ2(0), −a1,2
p
1 − ρ2
”–
−Ke
−r(T−t)
»
N
“
− ˜ψ1(0), −a2,1
p
1 − ρ2
”
− e
2 κ
α
“ νt
α
+ κ
α
θ(T−t)
”
N
“
− ˜ψ2(0), −a2,2
p
1 − ρ2
”–
,
Mario Dell’Era Closed Solution for Heston PDE by Geometrical Transformations
27. Quantitative Finance: stochastic volatility market models
ψ1(0) = −
h
νt
α + κ
α θ(T − t) + (ρ − κ
α )
R T
t νsds
i
qR T
t
νsds
,
ψ2(0) =
h
νt
α + κ
α θ(T − t) − (ρ − κ
α )
R T
t νsds
i
qR T
t
νsds
,
˜ψ1(0) = −
h
νt
α + κ
α θ(T − t) − κ
α
R T
t νsds
i
qR T
t
νsds
,
˜ψ2(0) =
h
νt
α + κ
α θ(T − t) + κ
α
R T
t νsds
i
qR T
t
νsds
,
Mario Dell’Era Closed Solution for Heston PDE by Geometrical Transformations
28. Quantitative Finance: stochastic volatility market models
a1,1 =
h
ln(K/St ) − r(T − t) − 1
2
R T
t
νsds
i
q
(1 − ρ2)
R T
t
νsds
,
a1,2 =
h
ln(K/St ) + 2 ρ
α νt − (r − 2 κθρ
α )(T − t) − 1
2
R T
t
νsds
i
q
(1 − ρ2)
R T
t
νsds
,
a2,1 =
h
ln(K/St ) − r(T − t) + 1
2
R T
t
νsds
i
q
(1 − ρ2)
R T
t
νsds
,
a2,2 =
h
ln(K/St ) + 2 ρ
α νt − (r − 2 κθρ
α )(T − t) + 1
2
R T
t
νsds
i
q
(1 − ρ2)
R T
t
νsds
.
Mario Dell’Era Closed Solution for Heston PDE by Geometrical Transformations
29. Quantitative Finance: stochastic volatility market models
Numerical Validation
The approximation τ → 0 will be here interpreted as option pricing for few
days. From 1 day up to 10 days are suitable maturities to prove our validation
hypothesis, at varying of volatility. Parameter values are those in Bakshi, Cao
and Chen (1997) namely κ = 1.15, Θ = 0.04, α = 0.39 and ρ = −0.64. We
have chosen r = 10% K = 100, and three different maturities T. In what
follows we use the expected value of the variance process EP[νs] instead of
νs in the term
R T
t
νsds. In the tables hereafter one can see the results of
numerical experiments:
Mario Dell’Era Closed Solution for Heston PDE by Geometrical Transformations
30. Quantitative Finance: stochastic volatility market models
Numerical Validation
The approximation τ → 0 will be here interpreted as option pricing for few
days. From 1 day up to 10 days are suitable maturities to prove our validation
hypothesis, at varying of volatility. Parameter values are those in Bakshi, Cao
and Chen (1997) namely κ = 1.15, Θ = 0.04, α = 0.39 and ρ = −0.64. We
have chosen r = 10% K = 100, and three different maturities T. In what
follows we use the expected value of the variance process EP[νs] instead of
νs in the term
R T
t
νsds. In the tables hereafter one can see the results of
numerical experiments:
Mario Dell’Era Closed Solution for Heston PDE by Geometrical Transformations
31. Quantitative Finance: stochastic volatility market models
Table: At the money, S0 = 100, K = 100, with parameter values: κ = 1.5,
θ = 0.04, α = 0.39, ρ = −0.64, r = 0.10 and Maturity 1 day.
Volatility Fourier method Dell’Era method
30% 0.6434 0.6442
40% 0.8543 0.8541
50% 1.0643 1.0641
60% 1.2743 1.2742
70% 1.4843 1.4845
80% 1.6943 1.6949
90% 1.9042 1.9055
100% 2.1142 2.1162
Mario Dell’Era Closed Solution for Heston PDE by Geometrical Transformations
32. Quantitative Finance: stochastic volatility market models
Table: At the money, S0 = 100, K = 100, with parameter values: κ = 1.5,
θ = 0.04, α = 0.39, ρ = −0.64, r = 0.10 and Maturity 5 days.
Volatility Fourier method Dell’Era method
30% 1.4763 1.4748
40% 1.9430 1.9407
50% 2.4101 2.4081
60% 2.8772 2.8769
70% 3.3444 3.3472
80% 3.8115 3.8190
90% 4.2785 4.2927
100% 4.7454 4.7683
Mario Dell’Era Closed Solution for Heston PDE by Geometrical Transformations
33. Quantitative Finance: stochastic volatility market models
Table: At the money, S0 = 100, K = 100, with parameter values: κ = 1.5,
θ = 0.04, α = 0.39, ρ = −0.64, r = 0.10 and Maturity 10 days.
Volatility Fourier method Dell’Era method
30% 2.1234 2.1191
40% 2.7787 2.7722
50% 3.4348 3.4294
60% 4.0912 4.0905
70% 4.7477 4.7557
80% 5.4040 5.4254
90% 6.0601 6.1002
100% 6.7158 6.7806
Mario Dell’Era Closed Solution for Heston PDE by Geometrical Transformations
34. Quantitative Finance: stochastic volatility market models
Table: In the money, S0 = K
“
1 + 10%
p
θ(T − t)
”
, with parameter values:
κ = 1.5, θ = 0.04, α = 0.39, ρ = −0.64, r = 0.10 and Maturity 1 day.
Volatility Fourier method Dell’Era method
30% 0.6991 0.6994
40% 0.9094 0.9089
50% 1.1191 1.1187
60% 1.3289 1.3287
70% 1.5377 1.5389
80% 1.7488 1.7494
90% 1.9588 1.9600
100% 2.1688 2.1708
Mario Dell’Era Closed Solution for Heston PDE by Geometrical Transformations
35. Quantitative Finance: stochastic volatility market models
Table: In the money, S0 = K
“
1 + 10%
p
θ(T − t)
”
, with parameter values:
κ = 1.5, θ = 0.04, α = 0.39, ρ = −0.64, r = 0.10 and Maturity 5 days.
Volatility Fourier method Dell’Era method
30% 1.6049 1.6012
40% 2.0700 2.0661
50% 2.5362 2.5331
60% 3.0030 3.0019
70% 3.4700 3.4723
80% 3.9372 3.9445
90% 4.4044 4.4186
100% 4.8715 4.8947
Mario Dell’Era Closed Solution for Heston PDE by Geometrical Transformations
36. Quantitative Finance: stochastic volatility market models
Table: In the money, S0 = K
“
1 + 10%
p
θ(T − t)
”
, with parameter values:
κ = 1.5, θ = 0.04, α = 0.39, ρ = −0.64, r = 0.10 and Maturity 10 days.
Volatility Fourier method Dell’Era method
30% 2.3098 2.3012
40% 2.9621 2.9527
50% 3.6168 3.6095
60% 4.2727 4.2708
70% 4.9291 4.9366
80% 5.5856 5.6072
90% 6.2421 6.2831
100% 6.8984 6.9647
Mario Dell’Era Closed Solution for Heston PDE by Geometrical Transformations
37. Quantitative Finance: stochastic volatility market models
Table: Out the money, S0 = K
“
1 − 10%
p
θ(T − t)
”
, with parameter values:
κ = 1.5, θ = 0.04, α = 0.39, ρ = −0.64, r = 0.10 and Maturity 1 day.
Volatility Fourier method Dell’Era method
30% 0.5905 0.5918
40% 0.8013 0.8014
50% 1.0111 1.0112
60% 1.2210 1.2212
70% 1.4309 1.4313
80% 1.6407 1.6415
90% 1.8506 1.8519
100% 2.0605 2.0625
Mario Dell’Era Closed Solution for Heston PDE by Geometrical Transformations
38. Quantitative Finance: stochastic volatility market models
Table: Out the money, S0 = K
“
1 − 10%
p
θ(T − t)
”
, with parameter values:
κ = 1.5, θ = 0.04, α = 0.39, ρ = −0.64, r = 0.10 and Maturity 5 days.
Volatility Fourier method Dell’Era method
30% 1.3539 1.3546
40% 1.8208 1.8201
50% 2.2878 2.2869
60% 2.7546 2.7551
70% 3.2214 3.2247
80% 3.6882 3.6959
90% 4.1547 4.1689
100% 4.6212 4.6438
Mario Dell’Era Closed Solution for Heston PDE by Geometrical Transformations
39. Quantitative Finance: stochastic volatility market models
Table: Out the money, S0 = K
“
1 − 10%
p
θ(T − t)
”
, with parameter values:
κ = 1.5, θ = 0.04, α = 0.39, ρ = −0.64, r = 0.10 and Maturity 10 days.
Volatility Fourier method Dell’Era method
30% 1.9459 1.9459
40% 2.6019 2.5985
50% 3.2581 3.2548
60% 3.9142 3.9148
70% 4.5701 4.5787
80% 5.2257 5.2471
90% 5.8810 5.9204
100% 6.5359 6.5992
Mario Dell’Era Closed Solution for Heston PDE by Geometrical Transformations
40. Quantitative Finance: stochastic volatility market models
Conclusions
The proposed method is straightforward from theoretical viewpoint and
seems to be promising from that numerical. We reduce the Heston’s PDE in
a simpler, using , in a right order, suitable changing of variables, whose
Jacobian has not singularity points, unless for ρ = ±1. This evidence gives
us the safety that the variables chosen are well defined.
Besides, the idea to use the expected value of the variance process EP[νs],
instead of νt , provides us, in concrete, a closed solution very easy to
compute; and so, we are also able to know what is the error using the
geometric transformation technique; which is equal to the variance of the
variance process νt : Err = EP[(νt − EP[νt ])2
]. While, using Fourier technique
we are not able to know the numeric error directly, but we need to compare
Fourier prices with Monte Carlo prices, for which one can manage the
variance.
We want to remark that the shown technique is independent to the payoff and
therefore, the pricing activities have the same algorithmic complexity for
every derivatives, unlike using Fourier Transform method, for which the
complexity is tied to the payoff.
Mario Dell’Era Closed Solution for Heston PDE by Geometrical Transformations
41. Quantitative Finance: stochastic volatility market models
Conclusions
The proposed method is straightforward from theoretical viewpoint and
seems to be promising from that numerical. We reduce the Heston’s PDE in
a simpler, using , in a right order, suitable changing of variables, whose
Jacobian has not singularity points, unless for ρ = ±1. This evidence gives
us the safety that the variables chosen are well defined.
Besides, the idea to use the expected value of the variance process EP[νs],
instead of νt , provides us, in concrete, a closed solution very easy to
compute; and so, we are also able to know what is the error using the
geometric transformation technique; which is equal to the variance of the
variance process νt : Err = EP[(νt − EP[νt ])2
]. While, using Fourier technique
we are not able to know the numeric error directly, but we need to compare
Fourier prices with Monte Carlo prices, for which one can manage the
variance.
We want to remark that the shown technique is independent to the payoff and
therefore, the pricing activities have the same algorithmic complexity for
every derivatives, unlike using Fourier Transform method, for which the
complexity is tied to the payoff.
Mario Dell’Era Closed Solution for Heston PDE by Geometrical Transformations
42. Quantitative Finance: stochastic volatility market models
Conclusions
The proposed method is straightforward from theoretical viewpoint and
seems to be promising from that numerical. We reduce the Heston’s PDE in
a simpler, using , in a right order, suitable changing of variables, whose
Jacobian has not singularity points, unless for ρ = ±1. This evidence gives
us the safety that the variables chosen are well defined.
Besides, the idea to use the expected value of the variance process EP[νs],
instead of νt , provides us, in concrete, a closed solution very easy to
compute; and so, we are also able to know what is the error using the
geometric transformation technique; which is equal to the variance of the
variance process νt : Err = EP[(νt − EP[νt ])2
]. While, using Fourier technique
we are not able to know the numeric error directly, but we need to compare
Fourier prices with Monte Carlo prices, for which one can manage the
variance.
We want to remark that the shown technique is independent to the payoff and
therefore, the pricing activities have the same algorithmic complexity for
every derivatives, unlike using Fourier Transform method, for which the
complexity is tied to the payoff.
Mario Dell’Era Closed Solution for Heston PDE by Geometrical Transformations