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The Paper provides a detailed examination of the Vanna-Volga method applied to Normal volatilities, particularly in the context of interest rate markets. Here are some key points and insights from the Paper: Vanna-Volga Method Overview: The Vanna-Volga method is widely used in FX markets to interpolate/extrapolate volatility smiles using three Lognormal market quotes. This method is originally designed for Lognormal volatilities but is free of distributional assumptions, making it adaptable to other models such as Normal volatilities. Application to Normal Volatilities: The paper modifies the Vanna-Volga method to apply it to Normal volatilities, which are the current standard for interest rate markets. Only minor modifications are needed to adapt the Vanna-Volga method from Lognormal to Normal volatilities. Advantages Over SABR Model: The Vanna-Volga method can easily fit both classical convex and concave (frown) volatility smiles, which are sometimes observed in interest rate markets. It is particularly useful for modeling situations with anticipated interest rate jumps and concave patterns towards the lower end of volatility smiles. Hedging Arguments and Greeks: The paper derives the necessary Greeks for the Bachelier (Normal) case, including Delta, Vega, Volga, Vanna, and Gamma. These Greeks are used to construct hedging portfolios and to understand the sensitivities of options to changes in underlying factors. Comparison with Black 76 Model: The Bachelier (Normal) model assumes a driftless Brownian motion for the forward price, leading to simpler option pricing formulae compared to the Black 76 (Lognormal) model. There are similarities between the Greeks derived for Normal and Lognormal models, but the inclusion of the forward price in Lognormal Greeks is a key difference. Practical Implications: The Normal Vanna-Volga method can achieve machine-precision level smile construction without the need for Taylor-series approximations. This makes it a robust and practical tool for interest rate market practitioners. Kickstart your Quant Interview Prep Check out Quant Insider Stack - https://lnkd.in/gcfdUEfg A Bundle of Interview Byte, Quantopia Library, and Quant Insider Project Handbook ‘Interview Byte’ contains 500+ Interview questions (https://lnkd.in/gkqcrrKf) Quantopia Library is the goldmine for building your domain knowledge (https://lnkd.in/geThBB4d) Quant Insider Project Handbook has 10 HFT/hedge fund industry-oriented projects(https://lnkd.in/gWBEn78U) Quant Insider Career Catalyst is your guide to all interview prep tips, preparation roadmap and job application strategies (https://lnkd.in/gVhA4tNG) Checkout our course Machine Learning for Finance - Designed by Industry Veterans Hariom Tatsat, CQF, FRM with years of working at Wallstreet - https://lnkd.in/gtJDWcus Use Coupon code - "EARLYBIRD20" for 20% off on the course

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This week, USDCHF will test key resistance while the SP500 continues to seek direction. With Japan's change in stance towards further interest rate hikes, traders will be bracing for shifts across major markets as they digest this news. Start trading with VT Markets now ▶️ https://lnkd.in/gy_V3ddY #VTMarkets #MarketRoundup #MarketAnalysis #Forex #Trading

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'Common risk factors in cross-sectional FX options returns' by Xuanchen Zhang, Raymond H Y So and Tarik Driouchi is now available in Review of Finance, Volume 28, Issue 3. Abstract: We identify a comprehensive list of thirty-eight characteristics for predicting cross-sectional FX options returns. We find that three factors—long-term straddle momentum, implied volatility, and illiquidity—can generate economically and statistically significant risk premia not explained by other return predictors. Meanwhile, the predictability of the other characteristics becomes insignificant after accounting for the FX option three-factor model. The significance of the three factors is confirmed through a series of robustness tests covering different data sources, alternative options strategies, diversification effects, bootstrapping, and omitting crisis years. Read the article: https://lnkd.in/ethzW-HF #reviewoffinance #finance

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Understanding the effect of Volatility on Gamma in Options Trading (Video Credit - https://lnkd.in/gBrqZmKW ) In options trading, gamma is a second-order Greek that measures the rate of delta change with respect to changes in the underlying asset's price. The Relationship Between Volatility and Gamma Volatility,, significantly affects gamma. Here's how: 1. Flattening of the Gamma Curve with High Volatility Peak Reduction at At-The-Money (ATM) Options: When volatility increases, the peak of the gamma curve at the ATM strike price diminishes. This means that the highest gamma value, which typically occurs at the ATM option, is reduced. Spread Across Strike Prices: High volatility causes the gamma values to distribute more evenly across a wider range of strike prices. Instead of a sharp peak at the ATM strike, the gamma curve becomes flatter and broader. 2.Gradual Change in Delta Slower Delta Movement: With a flatter gamma curve, the delta changes more gradually as the underlying asset's price moves. This is because the gamma is lower at the peak, causing delta to be less sensitive to small price movements in the underlying asset. Implications for Hedging: Traders relying on the delta for hedging will find that adjustments need to be made less frequently when volatility is high because the delta is changing at a slower rate. Visualizing the Effect Imagine plotting gamma against various strike prices: Low Volatility Scenario: The gamma curve is sharp and peaked at the ATM strike price. Delta changes rapidly near the ATM strike. High Volatility Scenario: The gamma curve is flatter and wider. Delta changes more slowly across a broader range of strike prices. Practical Implications for Traders Risk Management: Understanding that gamma is lower at the ATM strike during high volatility helps traders adjust their hedging strategies. They may need to monitor a wider range of strike prices due to the spread-out gamma. Option Pricing: High volatility increases option premiums but decreases gamma at the peak. Traders should account for this when pricing options and forecasting potential movements. Portfolio Adjustments: A flatter gamma curve means that the portfolio's sensitivity to the underlying asset's price changes is reduced near the ATM strike but increased at other strikes. Portfolio adjustments should reflect this shifted sensitivity. Kickstart your Quant Interview Prep ‘Interview Byte’ contains 500+ Interview questions (https://lnkd.in/gkqcrrKf) Quant Insider Project Handbook has 15 industry-oriented projects, which include 10 industry-oriented projects based on challenges conducted by Top HFT's and Hedge Funds. (https://lnkd.in/gWBEn78U) Check out Quant Insider Stack - https://lnkd.in/gcfdUEfg A Bundle of Interview Byte (500+ Interview questions) and Quant Insider Project Handbook

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Global markets are still adjusting to a regime of tightened monetary policy to combat persistently higher inflation and market sentiment remains volatile. How should South African investors approach this “new normal”? We offer some thoughts. #investing #interestrates #inflation #bonds #equities

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Fixed Income Equitization In this article, Paul Daley, Head of Fixed Info Lab at BondWave LLC, shares his thoughts on the progress of equitization in the fixed income markets: #CLOB, transparency, portfolio trading & more. #fixedincome #bondtrading #equitizationbonds #portfoliotrading https://lnkd.in/dEiZWUMj

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At Velociti Investment Managers, we craft personalised investment strategies based on your client’s unique goals. Our model portfolio range serves as the foundation, allowing us to implement a client-specific ALM strategy that’s built for success. Let’s take control of asset allocation together. Licensed Insurer | Short Term: FSP 44456 | Financial Services: FSP 50026 | Investment Managers FSP 52520 #FinancialAdvisors #WealthManagement #InvestmentStrategy

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