Tag: Pricing

Feed: SAS Blogs. Author: Waldemar Razik. Actuarial practice has always been analytical—so how much is advanced analytics disrupting this element of insurance? We discussed this issue in a recent #SASchat. Analytics is now being used in more and more areas in insurance, and particularly to tailor pricing more precisely to customers’ risk profile. This enables pricing to be more competitive, which is crucial when many customers use price comparison sites to select insurance. Robert Ruf, Sr Solutions Architect had a valid observation: “Competitive pricing in real time is essential for many insurance products today. When benefits are highly interchangeable, customers ... Read More

Feed: Recent Announcements. Amazon Textract is a managed service that leverages computer vision and machine learning to automatically extract printed and handwritten text, tables and forms data from scanned documents with no machine learning experience necessary. Today, we are excited to announce that effective September 1, 2021, customers will see a price reduction of up to 32% and save even more on the use of AnalyzeDocument and DetectDocumentText requests in eight global AWS Regions. Beginning September 1, 2021, the usage of the AnalyzeDocument and DetectDocumentText APIs in eight AWS Regions will be billed at the same rates as our prices in ... Read More

Feed: Cloudwards. Author: Arjun Ruparelia; It’s hard to come by a VPN (virtual private network) that’s both free and trustworthy. Fortunately, Windscribe is one of those VPNs. In this tutorial, we’ll go over how to use Windscribe VPN, so you can browse the web privately and stream geoblocked content for free. Key Takeaways: Windscribe is one of the best free VPNs, has a no-logs policy and offers strong encryption.The free plan offers 10GB of free data, renewed monthly. You also have the option to “build a plan” where you can purchase access to server locations for $1 per month per location ... Read More

Feed: R-bloggers. Author: sang-heon lee. #=========================================================================## Financial Econometrics & Derivatives, ML/DL using R, Python, Tensorflow  # by Sang-Heon Lee ## https://kiandlee.blogspot.com#————————————————————————-## FX Forward Pricing#=========================================================================#graphics.off()  # clear all graphsrm(list = ls()) # remove all files from your workspace#————————————————————————–# 1. Market Information#————————————————————————–    #——————————–    # Zero curves as of 2021-06-30    #——————————–    lt.mt – list(        spot_date = 44377,   # spot date        KRWUSD.FX.Spot = 1129.945, # FX Spot Rate        KRW_CRS.zero = data.frame(            # Excel numeric date            date = c(44378, 44379, 44386, 44410,                      44441, 44474, 44564, 44747),             rate = c(0.00629994563073550,                     0.00629994563073550,                     0.00463177425055339,                     0.00329405202619836,                     0.00292249195639071,                     0.00273429173667466,                     0.00331796879551685,                     0.00397939937284654)        ),        USD_IRS.zero  = data.frame(            # Excel numeric date            date = c(44378, 44386, 44410, 44441,                     44473, 44564, 44655, 44747),             rate = c(0.000842844860217754,                     0.000908701294845472,                     0.001006361388394810,                     0.001238311778397180,                     0.001455778457003310,                     0.001533135923937390,                     0.001716219715472820,                     0.001848263900368730)        )    )    #——————————–    # Interpolation of zero curve    #——————————–    # USD IRS    v.date   – lt.mt$USD_IRS.zero$date    v.zero   – lt.mt$USD_IRS.zero$rate    func_linear_USD_IRS.zero – approxfun(v.date, v.zero,                                           method=“linear”)    # KRW CRS    v.date   – lt.mt$KRW_CRS.zero$date    v.zero   – lt.mt$KRW_CRS.zero$rate    func_linear_KRW_CRS.zero – approxfun(v.date, v.zero,                                           method=“linear”)#————————————————————————–# 2. FX Forward Trade Information#————————————————————————–    fAmt – 10000      # Foreign notional amount    dAmt – 10948600   # Domestic notional amount    fPosition – 1     # 1 if Foreign is a buy position, -1 if sell    mat_date  – 44561 # maturity date of FX Forward contract#————————————————————————–# 3. FX Forward Pricing#————————————————————————–    #——————————–    # 1) Foreign    #——————————–    # zero rate for discounting    fzero – func_linear_USD_IRS.zero(mat_date)    # discount factor    fDF – exp(–fzero*(mat_date–lt.mt$spot_date)/365)    # discounted foreign amount denominated in foreign currency    pv_fAmt_in_f – fAmt*fDF    # discounted foreign amount denominated in domestic currency    pv_fAmt_in_d – pv_fAmt_in_f*lt.mt$KRWUSD.FX.Spot    #——————————–    # 2) Domestic    #——————————–    # zero rate for discounting    dzero – func_linear_KRW_CRS.zero(mat_date)    # discount factor    dDF – exp(–dzero*(mat_date–lt.mt$spot_date)/365)    # discounted domestic amount denominated in foreign currency    pv_dAmt_in_d – dAmt*dDF    # discounted domestic amount denominated in domestic currency    pv_dAmt_in_f – pv_dAmt_in_d/lt.mt$KRWUSD.FX.Spot    # 3) Price    #——————————–    price_in_f – fPosition*(pv_fAmt_in_f – pv_dAmt_in_f)    price_in_d – fPosition*(pv_fAmt_in_d – pv_dAmt_in_d)    print(paste0(“FX Forward Price ($) = “, round(price_in_f,2)))    print(paste0(“FX Forward Price (w) = “, round(price_in_d,2))) ... Read More

Feed: R-bloggers. Author: sang-heon lee. #=========================================================================## Financial Econometrics & Derivatives, ML/DL using R, Python, Tensorflow  # by Sang-Heon Lee ## https://kiandlee.blogspot.com#————————————————————————-## OIS swap pricing by using a VBA macro in R through RDCOMClient#=========================================================================#library(RDCOMClient)graphics.off()  # clear all graphsrm(list = ls()) # remove all files from your workspace#===============================================================================# functions using RDCOMClient#===============================================================================f_read_vector – function(xlWbk1, sheet1, range1){    sheet – xlWbk1$Worksheets(sheet1)    range – sheet$Range(range1)    data  – do.call(“cbind”,range[[“Value”]])    data  – matrix(unlist(data), dim(data)[1], dim(data)[2])    return(data)}f_write_vector – function(xlWbk1, sheet1, range1, data1) {    sheet – xlWbk1$Worksheets(sheet1)    range – sheet$Range(range1)    range[[“Value”]] – asCOMArray(data1)}#===========================================================# MAIN#===========================================================    # set working directory    setwd(“D:/SHLEE/blog/excel_com”)    # Create Excel Application    xlApp – COMCreate(“Excel.Application”)    # Open the Macro Excel book    fn – “sample_ois.xlsm”    xlWbk – xlApp$Workbooks()$Open(paste0(getwd(),“/”,fn))    # use TRUE for Excel Spreadsheet to be visible    xlApp[[‘Visible’]] – TRUE # FALSE #===========================================================# Communicate between R and Excel#===========================================================    # Arguments for Excel Spreadsheet and VBA macro    sheet      – “Sheet1”    range_in   – “D4:E11”    range_out  – “H4:I11”    macro_name – “macro1”    #————————————————–    # Pass Input Market Swap Rates to Excel    # and Get OIS DFs and adjusted FWD Rates from Excel    #————————————————–    # 1) write input values from R to Excel    #    1st column : OIS swap rates    #    2nd column : Libor Swap Rates    m.input – rbind(c(0.00100, 0.00500),                      c(0.00620, 0.01040),                      c(0.01100, 0.01580),                      c(0.01640, 0.02120),                      c(0.02004, 0.02440),                      c(0.02354, 0.02760),                      c(0.02676, 0.03080),                      c(0.02958, 0.03400))    f_write_vector(xlWbk, sheet, range_in, m.input)     # 2) run Excel macro    xlApp$Run(macro_name)    # 3) read output values from R to Excel    m.output – f_read_vector(xlWbk, sheet, range_out)    print(cbind(m.input, m.output))    #————————————————–    # Libor IRS pricing with OIS discounting    # Check if a swap price is at par for each maturity    #————————————————–    v.Lib_SR – m.input [,2] # Libor Swap Rates (input)    v.OIS_DF – m.output[,1] # OIS discount factor (output)    v.adj_FD – m.output[,2] # Adjusted Forward Rate (output)    for(i in 1:nrow(m.output)) {        v.fixed_leg – sum(v.Lib_SR[i]*(3/12)*v.OIS_DF[1:i])        v.float_leg – sum(v.adj_FD[1:i]*(3/12)*v.OIS_DF[1:i])         swap_pr – v.fixed_leg – v.float_leg        print(paste0(i,“-quarter swap price = “, swap_pr))    }#===========================================================# save and quit#===========================================================xlWbk$close(TRUE); xlApp$Quit()  ... Read More

Feed: Cloudwards. Author: Sandra Pattison; If you’re toying with the idea of buying a Roku, chances are you’re pretty confused about the pricing side of it. You might have the same questions circling around your head, like: How much is Roku? Are channels free? Do I have to sign up for a subscription? Can I rent a movie on Roku? This Roku pricing guide aims to answer all of your questions. Although Roku is one of the most budget-friendly streaming devices you can get, there are different costs involved when tallying up the total price of using one. For starters, ... Read More

Feed: R-bloggers. Author: Di Cook. #=========================================================================## Financial Econometrics & Derivatives, ML/DL using R, Python, Tensorflow # by Sang-Heon Lee ## https://kiandlee.blogspot.com #————————————————————————-## Coupon Bond Valuation with in advance interest payment#=========================================================================#graphics.off()  # clear all graphsrm(list=ls())   # clear all datasets#—————————————————————–## Specification#—————————————————————–## valuation date   : 2018-03-22# maturity date    : 2019-07-29# coupon rate      : annual 6.2%, quarterly coupon with in advance# principal amount : Bullet repayment at maturity #—————————————————————–#    cpn.rate – 0.062    # coupon rate    int.rate – 0.062    # interest rate    mat.qrt  – 6        # # of interest payments    # initialization    cpn.amt – 0  # coupon payments    prn.amt – 0  # principal amount    price   – 0  # bond price    # 2018.04.26’s discounted value of coupon payments    for (t in 0:(mat.qrt–2)) {         cpn.amt – cpn.amt + (cpn.rate/4)*10000/                             (1+int.rate/4)^t    }    # 2018.04.26’s discounted value of     # 4-day coupon payment at 2019.07.25    cpn.amt – cpn.amt + (cpn.rate/4)*(4/93)*10000/                         (1+int.rate/4)^(mat.qrt–1)    # 2018.04.26’s discounted value of     #principal amount at 2019.07.29    prn.amt – 10000/              ((1+(int.rate/4)*(4/93))*(1+int.rate/4)^(mat.qrt–1))    # 2018.04.26’s bond price    price – cpn.amt + prn.amt    print(paste(” bond price before discounting at pricing date:”, price))    #—————————————————————–#    # 2018.03.22’s discounted value of all cash flow    # = present value of all cash flow    # = bond price at valuation date    #—————————————————————–#    # present value discounted at pricing date using simple interest     #—————————————————————–#    price = price/(1+(int.rate/4)*(35/90))    print(paste(“bond price discounting at pricing date using simple interest :”, price))    dirty.price      – round(price,2)    accrued.interest – round((10000*int.rate/4)*(35/90),2)    clean.price      – round(dirty.price + accrued.interest,2)    print(“clean price = dirty price + accrued interest”)     print(paste(clean.price, “=”, price, “+”, accrued.interest)) ... Read More

Feed: Recent Announcements. Starting May 1st 2021, all data transfer over a VPC Peering connection that stays within an Availability Zone (AZ) is now free. All data transfer over a VPC Peering connection that crosses Availability Zones will continue to be charged at the standard in-region data transfer rates. You can use the Availability Zone-ID to uniquely and consistently identify an Availability Zone across different AWS accounts. Customers use VPC Peering to inter-connect VPCs within a region. VPC Peering is commonly used when interconnecting a small number of VPCs in a region to achieve full mesh connectivity. AWS Transit Gateway and AWS ... Read More

Feed: Cloudwards. Author: Fergus O'Sullivan; Integromat is an automation platform in the same vein as IFTTT or Zapier in that you need little to no coding knowledge to use it, but it can still create complex workflows that will reduce the time many humdrum tasks take to just seconds. As you can read in our full Integromat guide, we really like it for its functionality, and in this article we’ll go over why we like the Integromat pricing model. Key Takeaways: Integromat has one of the best free plans on the market, offering two multi-step scenarios straight off the bat.Plans ... Read More

Feed: Recent Announcements. AWS Snow Family now enables you to order, track, and manage long-term pricing Snowball Edge jobs directly from the console or via APIs. Many Snowball Edge customers deploy Snowball Edge devices for one or more years. By paying the device fees upfront, Snowball Edge’s long-term pricing options enable you to use Snowball Edge devices for 1 year or 3 years at up to a 62% discount compared to on-demand per-day fees. This pricing is available for all Snowball Edge device types in all regions where Snowball Edge is available. Previously, customers would have to reach out to ... Read More