Cristiano Oliveira, MSc.

Great literature review on inflation-linked products by Jarrow and Yildirim. Covers bonds, derivatives, pricing models, and empirical findings. Link (open access): https://lnkd.in/df5Pm5BB

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Most cited paper "Equity Portfolio Diversification" by William N. Goetzmann and Alok Kumar in Review of Finance, Volume 12, Issue 3, 2008, Pages 433–463, https://lnkd.in/dD64A-s Published: 28 March 2008 Abstract This study shows that U.S. individual investors hold under-diversified portfolios, where the level of under-diversification is greater among younger, low-income, less-educated, and less-sophisticated investors. The level of under-diversification is also correlated with investment choices that are consistent with over-confidence, trend-following behavior, and local bias. Furthermore, investors who over-weight stocks with higher volatility and higher skewness are less diversified. In contrast, there is little evidence that portfolio size or transaction costs constrains diversification. Under-diversification is costly to most investors, but a small subset of investors under-diversify because of superior information. JEL G11 - Portfolio Choice; Investment DecisionsG12 - Asset Pricing; Trading volume; Bond Interest Rates Oxford University Press OUP Academic Yale School of Management Texas McCombs School of Business European Finance Association

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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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Causal Relationship Between VIX ETPs and Futures in Low- and High-Volatility Regimes Research has focused on examining the lead-lag relationship between VIX ETPs and VIX futures. The paper similarly investigates this relationship but goes further by examining the causal relationship across different regimes. Specifically, it categorizes the market into two regimes: regime 1, classified as low-mean, low-volatility, and regime 2, classified as high-mean, high-volatility. Findings: - Cointegration tests reveal stable, long-term equilibrium relationships between VIX ETPs and futures.    - Regime-shifting models show that causality varies over time with changes in volatility, especially distinguishing between ETFs and ETNs. - In the models, Regime 1 is categorized as low-mean, low-volatility, and Regime 2 as high-mean, high-volatility, with Regime 2 exhibiting about 25 times greater volatility. - For ETFs, causality remains relatively stable between low- and high-volatility regimes, while ETNs show more time-varying causal relationships. - Markets tend to revert to equilibrium faster in Regime 1 than in Regime 2. - Demand pressures for VIX ETNs and futures vary by regime; for example, SPVXSTR shows higher sensitivity to VXX in Regime 1, while XIV is more sensitive to TVIX in Regime 2. Reference: Michael O’Neill and Gulasekaran Rajaguru, On the analysis of time-varying causality between VIX exchange-traded products and VIX futures contracts in high and low volatility regimes, Journal of Accounting Literature, 2024 Join the quant community—subscribe to the newsletter! Link in profile. #volatility #vix #futures #derivatives ABSTRACT The authors observe time variation in causality with the volatility of volatility. In particular, demand pressures for VIX ETNs and futures can change in different regimes. The authors observe two regimes where regime 1 is classified as low-mean low-volatility, while regime 2 is classified as high-mean high-volatility. The convergence to the long-run equilibrium in the low-mean low-volatility regime is faster than the high-mean high-volatility regime. The nature of the time varying lead lag relations demonstrates the opportunities for arbitrage. The linear causal relations between VXX and VIX futures are well established, with leads and lags generally found to be short-lived with arbitrage relations holding. The authors go further to capture the time-varying causal relationships through a Markovian process. The authors establish the nonlinear causal relations between inverse and leveraged products where causal relations are not yet documented.

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Cross-Asset Technical Outlook 📊 Our 'week ahead' report is out for distribution. Talking points : 👉🏽ES, NQ targets for next week 👉🏽EM futures (MES1) targets for next week 👉🏽Where next for our Nikkei longs (NK1) 👉🏽BBDXY outlook 👉🏽USD options skew and implications for underlying 👉🏽USD agg. speculative positioning 👉🏽Breakout in G-10 FX carry index 👉🏽G-10 momentum leaders and laggards 👉🏽AUD/USD long-term pattern breakout 👉🏽AUD/CNH and AUD/SGD 👉🏽USD/NOK July 2023 deja-vu 👉🏽USD/PLN and USD/HUF 👉🏽BRL/ZAR tactical play 👉🏽Asia/$ outlook after holding key support for 2nd week 👉🏽UST 2-yr yield and Citi US Eco Surprise 👉🏽TU1 game plan 👉🏽UST 5y nominal and real yield outlook - big level breaks on reals 👉🏽 SOFR opportunities next week 👉🏽 ERM5-SFRM5 outlook after big level gives way 👉🏽 UST curve outlook - 2s10s and 5s10s... steepening checkmate! 👉🏽 Gold, Silver, Oil, Copper - bias and targets If you are an institutional participant, contact me for a copy of this report. IB me on Bbg of email at [email protected]

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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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🚨 Understanding Stationarity of Relative Commodity Prices: A Key to Probabilistic Forecasting & Hedge Fund Trading Strategies 🚨 In the world of commodities trading, stationarity plays a critical role. When the relative prices of commodities demonstrate stationarity, it means that their statistical properties (like mean and variance) remain constant over time. This offers a huge advantage for hedge funds looking to build robust trading strategies. Here’s why it matters: 🔍 Probabilistic Forecasting: If the relative prices of two or more commodities are stationary, hedge funds can model price behavior with greater accuracy. With past trends being more predictable, probabilistic forecasts become more reliable, helping traders make informed decisions. 📊 Trading Strategy Development: By understanding stationarity, funds can design mean-reversion strategies—buying when prices deviate significantly from the mean and selling when they revert. Additionally, stationarity allows for pair trading, where the relationship between two commodities can guide profitable trades based on price movements. 🔧 Optimizing Risk Management: The stability of relative prices enables better risk modeling and scenario analysis. This enhances the ability to manage volatility in uncertain market conditions. Embracing stationarity and using it to forecast commodity price behavior is a powerful way for hedge funds to gain an edge in today’s fast-moving markets. 📈 #HedgeFunds #Commodities #ArtificialIntelligence #TradingStrategy #QuantitativeTrading #FinanceInnovation

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Weekly #Centralbanks #ReferenceRate Decisions recap: Increase 25-15 bps: Bank of #Japan - 0.25% Unchanged: #FederalReserve - 5.50% Central Bank of #Brazil - 10.5% Decrease 25bps: Bank of #England - 5.00% #czech National Bank - 4.75% Decrease 75bps: Bank of #Mozambique - 14.25% #InterestRates decisions here: https://lnkd.in/dEy7DC_n #FOMC

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