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}
}
global_geo_obj.html(weather_info);
var global_geo = jQuery('#forecast');
get_forecast_details(city, 4, global_geo, country);
})
});
});
function forecast_status(msg) {
jQuery('#forecast-header').html(msg);
}
function get_forecast_details(city, days_count, global_geo, country) {
global_geo.html('Loading forecast ...');
jQuery.ajax({
data: {
city: city,
report: 'daily'
},
dataType: 'jsonp',
url: 'https://upge.wn.com/api/upge/cheetah-photo-search/weather_forecast_4days',
success: function(data) {
if(!data) { text = ('weater data temporarily not available'); }
// loop through the list of weather info
weather_info = '';
var weather_day_loop = 0;
jQuery.each(data.list, function(idx, value) {
if (idx < 1) {
return;
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if (weather_day_loop >= days_count) {
return false;
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weather = value.weather.shift()
clouds = value.clouds
d = new Date(value.dt*1000)
t = d.getMonth()+1 + '-' + d.getDate() + '-' + d.getFullYear()
moment.lang('en', {
calendar : {
lastDay : '[Yesterday]',
sameDay : '[Today]',
nextDay : '[Tomorrow]',
lastWeek : '[last] dddd',
nextWeek : 'dddd',
sameElse : 'L'
}
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mobj = moment(value.dt*1000)
// skip today
if (t == today) {
return;
}
tempC = parseInt(parseFloat(value.temp.day)-273.15)
tempF = parseInt(tempC*1.8+32)
today = t;
weather_day_loop += 1;
weather_info += '
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});
global_geo.html(weather_info);
}
});
}
//-->
-
Options Valuation Expert Steve Heston
Finance professor Steve Heston, creator of the Heston Model for valuing financial options, discusses how he brings his expertise into the Master of Finance and Master of Quantitative Finance programs at the University of Maryland's Robert H. Smith School of Business.
published: 19 Jun 2018
-
Brooklyn Quant Experience Lecture Series: Steve Heston
The Department of Finance and Risk Engineering welcomed Steve Heston, Professor of Finance at the University of Maryland, College Park, to the BQE Lecture Series on September 24, 2020, to present his paper on "Option Momentum."
published: 01 Oct 2020
-
Steve Heston -- Recovering the Variance Premium
Steve Heston (University of Maryland) presents his new paper titled "Recovering the Variance Premium." The slides and the paper are available at www.virtualderivatives.org.
published: 15 Jul 2020
-
Brooklyn Quant Experience (BQE) Lecture Series: Steve Heston
The Department of Finance & Risk Engineering welcomed Dr. Steve Heston from the University of Maryland, College Park, as part of the Brooklyn Quant Experience (BQE) Lecture Series on April 16, 2020 live on Zoom. Dr. Steve Heston presented his talk "Recovering the Variance Premium".
published: 27 Apr 2020
-
What Is the Heston Model?
The Heston Model is a stochastic volatility model used to price European options. It is developed by associate finance professor Steven Heston and is comparable to the Black-Scholes model. The Heston Model uses stochastic methods to calculate and forecast option pricing with the assumption that volatility is arbitrary. It is different from other models and has its own methodology for pricing options. The Heston Model seeks to provide for one of the main limitations of the Black-Scholes model, which holds volatility constant.
1cd0
published: 18 Feb 2024
-
Understanding Heston Model
Explain : Heston Model
The Heston Model, named after Steve Heston, is a type of stochastic volatility model used to price European options.
published: 02 Dec 2021
-
Trading Options Risk-Free with HESTON MODEL in Python
In this video we'll see how to use the Breeden-Litzenberger formula to derive the risk-neutral density function from European call options with stochastic volatility under the Heston model.
From Wikipedia:
In finance, the Heston model, named after Steven L. Heston, is a mathematical model that describes the evolution of the volatility of an underlying asset. It is a stochastic volatility model: such a model assumes that the volatility of the asset is not constant, nor even deterministic, but follows a random process.
Heston model parameters:
S = spot price
K = strike price
T = maturity
r = risk-free rate
d = dividend yield
v0 = initial volatility
kappa = mean reversion rate
theta = mean reversion level
sigma = vol-of-vol
rho = correlation between spot price and volatility
Chapters:
00:0...
published: 28 Jan 2023
-
Q1. Heston Model Trading Strategy #shorts #short #YRIA
@you_raise_i_ask
You raise a question that you are interested and I will use ChatGPT 4.0 to answer your question straightaway.
Q1. I am a junior quant trader and I have just learnt about Heston model today. Can you explain to me what is Heston model from a industry professional’s perspective and how I can use this model in my trading? Also, can you write me a Python script with a strategy using the Heston model and also the script to backtest the strategy.
A1. The Heston model is a widely used option pricing model in the finance industry, particularly for pricing European options. It was introduced by Steven Heston in 1993 and is an extension of the Black-Scholes model. The main difference is that the Heston model accounts for stochastic volatility, which means that the volatility is a ...
published: 03 Apr 2023
-
Derivation of Heston Stochastic Volatility Model PDE
Derives the Partial Differential Equation (PDE) that the price of a derivative/option satisfies under the Heston Stochastic Volatility. This is the so called pricing PDE or valuation PDE. Also explains the market price of risk (the lambda term) that is integral to the derivation of the Heston PDE. Content by timeline:
00:00 - Introduction and motivation behind Heston Stochastic Volatility
04:06 - Derivation of the Heston PDE
15:03 - Informal derivation of the market price of volatility risk
18:16 - Derivation of the market price of volatility risk
published: 18 Dec 2019
-
The Heston Model (Part I)
★★ Save 10% on All Quant Next Courses with the Coupon Code: QuantNextYoutube10 ★★
★★ For students and graduates, we offer a 50% discount on all courses, please contact us if you are interested ★★
★★ Visit us: https://quant-next.com/product/options-pricing-and-risk-management-part-3/ ★★
★★ Contact us: [email protected] ★★
★★ Follow us: https://www.linkedin.com/company/quant-next/ ★★
In this video we will introduce the Heston model which is one of the most used stochastic volatility model.
It assumes that the variance is stochastic, it is correlated with the asset price and follows a mean-reverting Cox Ingersoll Ross (CIR) process.
0:00 Introduction
0:22 The Black-Scholes Model and its Limits
0:31 The Volatility Changes with Time
1:02 The Volatility Clusters
1:14 T...
published: 20 Mar 2023
2:55
Options Valuation Expert Steve Heston
Finance professor Steve Heston, creator of the Heston Model for valuing financial options, discusses how he brings his expertise into the Master of Finance and ...
Finance professor Steve Heston, creator of the Heston Model for valuing financial options, discusses how he brings his expertise into the Master of Finance and Master of Quantitative Finance programs at the University of Maryland's Robert H. Smith School of Business.
https://wn.com/Options_Valuation_Expert_Steve_Heston
Finance professor Steve Heston, creator of the Heston Model for valuing financial options, discusses how he brings his expertise into the Master of Finance and Master of Quantitative Finance programs at the University of Maryland's Robert H. Smith School of Business.
- published: 19 Jun 2018
- views: 1542
1:16:38
Brooklyn Quant Experience Lecture Series: Steve Heston
The Department of Finance and Risk Engineering welcomed Steve Heston, Professor of Finance at the University of Maryland, College Park, to the BQE Lecture Serie...
The Department of Finance and Risk Engineering welcomed Steve Heston, Professor of Finance at the University of Maryland, College Park, to the BQE Lecture Series on September 24, 2020, to present his paper on "Option Momentum."
https://wn.com/Brooklyn_Quant_Experience_Lecture_Series_Steve_Heston
The Department of Finance and Risk Engineering welcomed Steve Heston, Professor of Finance at the University of Maryland, College Park, to the BQE Lecture Series on September 24, 2020, to present his paper on "Option Momentum."
- published: 01 Oct 2020
- views: 529
1:01:57
Steve Heston -- Recovering the Variance Premium
Steve Heston (University of Maryland) presents his new paper titled "Recovering the Variance Premium." The slides and the paper are available at www.virtualderi...
Steve Heston (University of Maryland) presents his new paper titled "Recovering the Variance Premium." The slides and the paper are available at www.virtualderivatives.org.
https://wn.com/Steve_Heston_Recovering_The_Variance_Premium
Steve Heston (University of Maryland) presents his new paper titled "Recovering the Variance Premium." The slides and the paper are available at www.virtualderivatives.org.
- published: 15 Jul 2020
- views: 560
1:37:32
Brooklyn Quant Experience (BQE) Lecture Series: Steve Heston
The Department of Finance & Risk Engineering welcomed Dr. Steve Heston from the University of Maryland, College Park, as part of the Brooklyn Quant Experience (...
The Department of Finance & Risk Engineering welcomed Dr. Steve Heston from the University of Maryland, College Park, as part of the Brooklyn Quant Experience (BQE) Lecture Series on April 16, 2020 live on Zoom. Dr. Steve Heston presented his talk "Recovering the Variance Premium".
https://wn.com/Brooklyn_Quant_Experience_(Bqe)_Lecture_Series_Steve_Heston
The Department of Finance & Risk Engineering welcomed Dr. Steve Heston from the University of Maryland, College Park, as part of the Brooklyn Quant Experience (BQE) Lecture Series on April 16, 2020 live on Zoom. Dr. Steve Heston presented his talk "Recovering the Variance Premium".
- published: 27 Apr 2020
- views: 377
3:49
What Is the Heston Model?
The Heston Model is a stochastic volatility model used to price European options. It is developed by associate finance professor Steven Heston and is comparable...
The Heston Model is a stochastic volatility model used to price European options. It is developed by associate finance professor Steven Heston and is comparable to the Black-Scholes model. The Heston Model uses stochastic methods to calculate and forecast option pricing with the assumption that volatility is arbitrary. It is different from other models and has its own methodology for pricing options. The Heston Model seeks to provide for one of the main limitations of the Black-Scholes model, which holds volatility constant.
1cd0
https://wn.com/What_Is_The_Heston_Model
The Heston Model is a stochastic volatility model used to price European options. It is developed by associate finance professor Steven Heston and is comparable to the Black-Scholes model. The Heston Model uses stochastic methods to calculate and forecast option pricing with the assumption that volatility is arbitrary. It is different from other models and has its own methodology for pricing options. The Heston Model seeks to provide for one of the main limitations of the Black-Scholes model, which holds volatility constant.
1cd0
- published: 18 Feb 2024
- views: 397
2:19
Understanding Heston Model
Explain : Heston Model
The Heston Model, named after Steve Heston, is a type of stochastic volatility model used to price European options.
Explain : Heston Model
The Heston Model, named after Steve Heston, is a type of stochastic volatility model used to price European options.
https://wn.com/Understanding_Heston_Model
Explain : Heston Model
The Heston Model, named after Steve Heston, is a type of stochastic volatility model used to price European options.
- published: 02 Dec 2021
- views: 724
11:46
Trading Options Risk-Free with HESTON MODEL in Python
In this video we'll see how to use the Breeden-Litzenberger formula to derive the risk-neutral density function from European call options with stochastic volat...
In this video we'll see how to use the Breeden-Litzenberger formula to derive the risk-neutral density function from European call options with stochastic volatility under the Heston model.
From Wikipedia:
In finance, the Heston model, named after Steven L. Heston, is a mathematical model that describes the evolution of the volatility of an underlying asset. It is a stochastic volatility model: such a model assumes that the volatility of the asset is not constant, nor even deterministic, but follows a random process.
Heston model parameters:
S = spot price
K = strike price
T = maturity
r = risk-free rate
d = dividend yield
v0 = initial volatility
kappa = mean reversion rate
theta = mean reversion level
sigma = vol-of-vol
rho = correlation between spot price and volatility
Chapters:
00:00 - Introduction
00:42 - Heston model's parameters
02:18 - Heston model's pdf
08:42 - Testing functions
10:12 - Plot of density function
https://wn.com/Trading_Options_Risk_Free_With_Heston_Model_In_Python
In this video we'll see how to use the Breeden-Litzenberger formula to derive the risk-neutral density function from European call options with stochastic volatility under the Heston model.
From Wikipedia:
In finance, the Heston model, named after Steven L. Heston, is a mathematical model that describes the evolution of the volatility of an underlying asset. It is a stochastic volatility model: such a model assumes that the volatility of the asset is not constant, nor even deterministic, but follows a random process.
Heston model parameters:
S = spot price
K = strike price
T = maturity
r = risk-free rate
d = dividend yield
v0 = initial volatility
kappa = mean reversion rate
theta = mean reversion level
sigma = vol-of-vol
rho = correlation between spot price and volatility
Chapters:
00:00 - Introduction
00:42 - Heston model's parameters
02:18 - Heston model's pdf
08:42 - Testing functions
10:12 - Plot of density function
- published: 28 Jan 2023
- views: 841
0:43
Q1. Heston Model Trading Strategy #shorts #short #YRIA
@you_raise_i_ask
You raise a question that you are interested and I will use ChatGPT 4.0 to answer your question straightaway.
Q1. I am a junior quant trader ...
@you_raise_i_ask
You raise a question that you are interested and I will use ChatGPT 4.0 to answer your question straightaway.
Q1. I am a junior quant trader and I have just learnt about Heston model today. Can you explain to me what is Heston model from a industry professional’s perspective and how I can use this model in my trading? Also, can you write me a Python script with a strategy using the Heston model and also the script to backtest the strategy.
A1. The Heston model is a widely used option pricing model in the finance industry, particularly for pricing European options. It was introduced by Steven Heston in 1993 and is an extension of the Black-Scholes model. The main difference is that the Heston model accounts for stochastic volatility, which means that the volatility is a random process and can change over time. This feature enables the Heston model to capture the dynamics of the market more accurately compared to the Black-Scholes model, which assumes constant volatility.
As a junior quant trader, you can use the Heston model to better price options and manage your options portfolio, as well as implement trading strategies based on the model's predictions.
To get started with a strategy using the Heston model in Python, you can use the QuantLib library, which provides a wide range of tools for quantitative finance. Here's a sample Python script that demonstrates how to use the Heston model for European option pricing:
import QuantLib as ql
import numpy as np
import matplotlib.pyplot as plt
# Set up the Heston model
def create_heston_model(params, day_count, calendar):
kappa, theta, sigma, rho, v0 = params
spot_handle = ql.QuoteHandle(ql.SimpleQuote(spot_price))
flat_ts = ql.YieldTermStructureHandle(ql.FlatForward(calculation_date, risk_free_rate, day_count))
dividend_yield = ql.YieldTermStructureHandle(ql.FlatForward(calculation_date, dividend_rate, day_count))
heston_process = ql.HestonProcess(flat_ts, dividend_yield, spot_handle, v0, kappa, theta, sigma, rho)
return ql.HestonModel(heston_process)
# Parameters
spot_price = 100
risk_free_rate = 0.03
dividend_rate = 0.01
calculation_date = ql.Date(4, 4, 2023)
maturity_date = ql.Date(4, 4, 2024)
day_count = ql.Actual365Fixed()
calendar = ql.NullCalendar()
# Heston model parameters
kappa = 1.15
theta = 0.1
sigma = 0.3
rho = -0.6
v0 = 0.05
params = (kappa, theta, sigma, rho, v0)
# Create the Heston model
model = create_heston_model(params, day_count, calendar)
# Price a European call option
option_type = ql.Option.Call
strike_price = 100
payoff = ql.PlainVanillaPayoff(option_type, strike_price)
exercise = ql.EuropeanExercise(maturity_date)
option = ql.VanillaOption(payoff, exercise)
# Use an analytic pricing engine
engine = ql.AnalyticHestonEngine(model)
option.setPricingEngine(engine)
# Calculate the option price
option_price = option.NPV()
print(f"European call option price: {option_price:.4f}")
In this example, we use the QuantLib library to create a Heston model, define a European call option, and then price the option using the Heston model. The script can be easily extended to implement a trading strategy and backtesting based on the Heston model.
Please note that this script provides a basic example of using the Heston model for pricing a European option, and you will need to perform your own research and adjustments to create a profitable trading strategy. Additionally, you may need to add more code to handle data input, transaction costs, portfolio management, and risk management for a complete backtesting system.
https://wn.com/Q1._Heston_Model_Trading_Strategy_Shorts_Short_Yria
@you_raise_i_ask
You raise a question that you are interested and I will use ChatGPT 4.0 to answer your question straightaway.
Q1. I am a junior quant trader and I have just learnt about Heston model today. Can you explain to me what is Heston model from a industry professional’s perspective and how I can use this model in my trading? Also, can you write me a Python script with a strategy using the Heston model and also the script to backtest the strategy.
A1. The Heston model is a widely used option pricing model in the finance industry, particularly for pricing European options. It was introduced by Steven Heston in 1993 and is an extension of the Black-Scholes model. The main difference is that the Heston model accounts for stochastic volatility, which means that the volatility is a random process and can change over time. This feature enables the Heston model to capture the dynamics of the market more accurately compared to the Black-Scholes model, which assumes constant volatility.
As a junior quant trader, you can use the Heston model to better price options and manage your options portfolio, as well as implement trading strategies based on the model's predictions.
To get started with a strategy using the Heston model in Python, you can use the QuantLib library, which provides a wide range of tools for quantitative finance. Here's a sample Python script that demonstrates how to use the Heston model for European option pricing:
import QuantLib as ql
import numpy as np
import matplotlib.pyplot as plt
# Set up the Heston model
def create_heston_model(params, day_count, calendar):
kappa, theta, sigma, rho, v0 = params
spot_handle = ql.QuoteHandle(ql.SimpleQuote(spot_price))
flat_ts = ql.YieldTermStructureHandle(ql.FlatForward(calculation_date, risk_free_rate, day_count))
dividend_yield = ql.YieldTermStructureHandle(ql.FlatForward(calculation_date, dividend_rate, day_count))
heston_process = ql.HestonProcess(flat_ts, dividend_yield, spot_handle, v0, kappa, theta, sigma, rho)
return ql.HestonModel(heston_process)
# Parameters
spot_price = 100
risk_free_rate = 0.03
dividend_rate = 0.01
calculation_date = ql.Date(4, 4, 2023)
maturity_date = ql.Date(4, 4, 2024)
day_count = ql.Actual365Fixed()
calendar = ql.NullCalendar()
# Heston model parameters
kappa = 1.15
theta = 0.1
sigma = 0.3
rho = -0.6
v0 = 0.05
params = (kappa, theta, sigma, rho, v0)
# Create the Heston model
model = create_heston_model(params, day_count, calendar)
# Price a European call option
option_type = ql.Option.Call
strike_price = 100
payoff = ql.PlainVanillaPayoff(option_type, strike_price)
exercise = ql.EuropeanExercise(maturity_date)
option = ql.VanillaOption(payoff, exercise)
# Use an analytic pricing engine
engine = ql.AnalyticHestonEngine(model)
option.setPricingEngine(engine)
# Calculate the option price
option_price = option.NPV()
print(f"European call option price: {option_price:.4f}")
In this example, we use the QuantLib library to create a Heston model, define a European call option, and then price the option using the Heston model. The script can be easily extended to implement a trading strategy and backtesting based on the Heston model.
Please note that this script provides a basic example of using the Heston model for pricing a European option, and you will need to perform your own research and adjustments to create a profitable trading strategy. Additionally, you may need to add more code to handle data input, transaction costs, portfolio management, and risk management for a complete backtesting system.
- published: 03 Apr 2023
- views: 285
29:03
Derivation of Heston Stochastic Volatility Model PDE
Derives the Partial Differential Equation (PDE) that the price of a derivative/option satisfies under the Heston Stochastic Volatility. This is the so called pr...
Derives the Partial Differential Equation (PDE) that the price of a derivative/option satisfies under the Heston Stochastic Volatility. This is the so called pricing PDE or valuation PDE. Also explains the market price of risk (the lambda term) that is integral to the derivation of the Heston PDE. Content by timeline:
00:00 - Introduction and motivation behind Heston Stochastic Volatility
04:06 - Derivation of the Heston PDE
15:03 - Informal derivation of the market price of volatility risk
18:16 - Derivation of the market price of volatility risk
https://wn.com/Derivation_Of_Heston_Stochastic_Volatility_Model_Pde
Derives the Partial Differential Equation (PDE) that the price of a derivative/option satisfies under the Heston Stochastic Volatility. This is the so called pricing PDE or valuation PDE. Also explains the market price of risk (the lambda term) that is integral to the derivation of the Heston PDE. Content by timeline:
00:00 - Introduction and motivation behind Heston Stochastic Volatility
04:06 - Derivation of the Heston PDE
15:03 - Informal derivation of the market price of volatility risk
18:16 - Derivation of the market price of volatility risk
- published: 18 Dec 2019
- views: 18249
7:22
The Heston Model (Part I)
★★ Save 10% on All Quant Next Courses with the Coupon Code: QuantNextYoutube10 ★★
★★ For students and graduates, we offer a 50% discount on all courses, pl...
★★ Save 10% on All Quant Next Courses with the Coupon Code: QuantNextYoutube10 ★★
★★ For students and graduates, we offer a 50% discount on all courses, please contact us if you are interested ★★
★★ Visit us: https://quant-next.com/product/options-pricing-and-risk-management-part-3/ ★★
★★ Contact us:
[email protected] ★★
★★ Follow us: https://www.linkedin.com/company/quant-next/ ★★
In this video we will introduce the Heston model which is one of the most used stochastic volatility model.
It assumes that the variance is stochastic, it is correlated with the asset price and follows a mean-reverting Cox Ingersoll Ross (CIR) process.
0:00 Introduction
0:22 The Black-Scholes Model and its Limits
0:31 The Volatility Changes with Time
1:02 The Volatility Clusters
1:14 The Volatility Mean Reverts
1:36 Equities and Volatility are Negatively Correlated in General
2:15 The Heston Model
2:52 The Variance Follow a Mean-Reverting Process
4:15 Spot / Volatility Correlation
4:35 The Heston Model Parameters
7:11 Calibration to Historical Distribution
7:50 To be Continued
#optionpricing, #quantitativefinance, #financeeducation, #derivatives, #quant, #quantnext
https://wn.com/The_Heston_Model_(Part_I)
★★ Save 10% on All Quant Next Courses with the Coupon Code: QuantNextYoutube10 ★★
★★ For students and graduates, we offer a 50% discount on all courses, please contact us if you are interested ★★
★★ Visit us: https://quant-next.com/product/options-pricing-and-risk-management-part-3/ ★★
★★ Contact us:
[email protected] ★★
★★ Follow us: https://www.linkedin.com/company/quant-next/ ★★
In this video we will introduce the Heston model which is one of the most used stochastic volatility model.
It assumes that the variance is stochastic, it is correlated with the asset price and follows a mean-reverting Cox Ingersoll Ross (CIR) process.
0:00 Introduction
0:22 The Black-Scholes Model and its Limits
0:31 The Volatility Changes with Time
1:02 The Volatility Clusters
1:14 The Volatility Mean Reverts
1:36 Equities and Volatility are Negatively Correlated in General
2:15 The Heston Model
2:52 The Variance Follow a Mean-Reverting Process
4:15 Spot / Volatility Correlation
4:35 The Heston Model Parameters
7:11 Calibration to Historical Distribution
7:50 To be Continued
#optionpricing, #quantitativefinance, #financeeducation, #derivatives, #quant, #quantnext
- published: 20 Mar 2023
- views: 13971