This paper analyzes ETFs with Taiwan exposure, finding heavy tails and asymmetric volatility.
problem Heavy tails and asymmetric volatility in Taiwan-related ETFs.
method Tail-risk diagnostics, asymmetric volatility modeling, and portfolio optimization under mean--variance and CVaR criteria.
result CVaR optimization produces more concentrated allocations, favoring SMH during the post-COVID AI-driven expansion.
The study analyzes ETFs' portfolio optimization and tail-risk management.
problem Analyzing the performance of actively managed ETFs in managing risk and diversification.
method Daily Bloomberg data for 30 funds, evaluating various strategies under long-only and long-short constraints.
result Tangency-type portfolios generally outperform buy-and-hold benchmarks, while minimum-variance and CVaR-minimizing portfolios sacrifice upside for downside control.
Commodity ETFs' portfolio optimization under heavy-tailed returns.
problem Optimizing commodity ETF portfolios under heavy-tailed return behavior.
method Passive buy-and-hold vs. rolling-window optimized portfolios.
result Improved risk-adjusted performance with minimum-risk and CVaR-based portfolios.
This paper proposes a continuous timing strategy for growth vs. defensive style allocation.
problem Dynamic allocation of growth and defensive ETF baskets using macro-market timing signals.
method Continuous smooth score combining multiple factors, mapped to G/D weights, smoothed with EWMA.
result Continuous style timing strategy outperforms static benchmarks in risk-adjusted returns.
RGRR allocates between QQQ and DIA based on relative states, improving Sharpe and CAGR.
problem Optimizing ETF allocation between QQQ and DIA for better risk-adjusted returns.
method Screened relative and macro states, globally screened interactions, fixed position mapping, walk-forward validation.
result RGRR improves Sharpe and CAGR compared to 100% QQQ and 50/50 QQQ-DIA allocations.
Study uses reinforcement learning to optimize portfolios under recursive utility.
problem Improving portfolio allocation using risk-sensitive objectives.
method Approximated certainty equivalent via Monte Carlo, trained actor-critic algorithms (PPO, A2C).
result Recursive-utility agent outperforms discounted baseline in Sharpe ratio, max drawdown, and cumulative return.
The paper introduces a portfolio construction method using Black-Litterman model and factors.
problem Developing an efficient portfolio construction method using Black-Litterman model and factors.
method The method involves selecting 20 factors based on global market, asset class, and stock characteristics, applying various weight allocation methods including Black-Litterman model, and incorporating deep learning for dynamic weight updates.
result The model using Black-Litterman and deep learning outperforms other weight allocation schemes.
The paper discusses building ETF risk models using a multilevel classification taxonomy.
problem Building accurate risk models for ETFs.
method First, build a multilevel classification taxonomy for ETFs. Then, use this taxonomy to define risk factors and build risk models.
result The approach can accurately classify and model ETF risks.
2024 saw Bitcoin ETF approval, offering regulated exposure.
problem Understanding unique liquidity risks in Bitcoin ETFs.
method Analyzed premium/discount patterns in first four months.
result Premium/discount behavior differs from traditional ETFs.
Deep learning shows ETF imbalances are more informative than market imbalances.
problem Determining causality between ETF and market imbalances.
method Deep learning econometric methodology applied to stock and ETF transactions.
result ETF imbalance messages are more informative than market imbalance messages.
Study examines how arbitrage between ETF and futures affects market liquidity during crashes.
problem Impact of arbitrage between leveraged ETF and futures on market liquidity during market crashes.
method Artificial market simulations to investigate liquidity changes in L-ETF and futures markets.
result Arbitrage trading affects liquidity supply from one market to another during market crashes.
Neural networks predict ETF performance using financial data.
problem Data shortage for ETFs.
method Train neural networks on financial statement data of individual stocks to predict ETF performance.
result Proposed method outperforms baselines.
This study uses HMM and RL to dynamically allocate equities, Treasuries, and gold based on market regimes.
problem Developing a dynamic portfolio allocation strategy for different market conditions.
method Characterizes market regimes using Markov switching models and HMM, then applies RL for allocation decisions.
result RL-based allocation outperforms passive strategies, providing lower drawdowns and higher Sharpe ratios.
The growth of the exhange-traded fund (ETF) industry has given rise to the trading of options written on ETFs and their leveraged counterparts {(LETFs)}. We study the relationship between the ETF and LETF implied volatility surfaces when the underlying ETF is modeled by a general class of local-stochastic volatility mo…
ETFs with 2x and 3x leverage underperformed the S&P 500 index due to compounding and volatility.
problem ETFs with higher leverage failed to match the performance of the underlying index.
method Analyzed the performance of leveraged ETFs compared to the S&P 500 index, accounting for compounding and volatility.
result Two-thirds of the underperformance was due to compounding and volatility, with the rest due to covariance.
Study finds IBS useful for predicting ETF price movements.
problem Predicting short-term price movements in country ETFs.
method Quantitative analysis of historical price data using Mean Reversion.
result IBS can be a useful technical indicator for ETFs.
Exchange Traded Funds (ETFs) have been gaining increasing popularity in the investment community as is evidenced by the high growth both in the number of ETFs and their net assets since 2000. As ETFs are in nature similar to index mutual funds, in this paper we examined if this growing demand for ETFs can be explained …
Funds inflate their returns due to price pressure, leading to wealth reallocation and market crashes.
problem Funds inflate their returns due to price pressure, leading to wealth reallocation and market crashes.
method Decomposed fund returns into price pressure and fundamental components, and identified the impact of price chasing on fund flows.
result Funds' self-inflated returns lead to wealth reallocation and market crashes, and can be predicted by fund illiquidity.
Using detailed statistical analyses of the size distribution of a universe of equity exchange-traded funds (ETFs), we discover a discrete hierarchy of sizes, which imprints a log-periodic structure on the probability distribution of ETF sizes that dominates the details of the asymptotic tail. This allows us to propose …
This paper considers the problem of isolating a small number of exchange traded funds (ETFs) that suffice to capture the fundamental dimensions of variation in U.S. financial markets. First, the data is fit to a vector-valued Bayesian regression model, which is a matrix-variate generalization of the well known stochast…
Commodity exchange-traded funds (ETFs) are a significant part of the rapidly growing ETF market. They have become popular in recent years as they provide investors access to a great variety of commodities, ranging from precious metals to building materials, and from oil and gas to agricultural products. In this article…
The paper analyzes risk spillovers between AI ETFs, AI tokens, and green markets.
problem Risk spillovers among AI ETFs, AI tokens, and green markets.
method R2 decomposition method
result AI ETFs and clean energy act as risk transmitters, while AI tokens and green assets act as receivers.
Paper improves ETF tail-risk monitoring reliability.
problem Unreliable ETF risk monitoring under degraded data.
method Combines quality checks, prediction, scoring, and adjustment.
result Improves tail-risk monitoring, especially during stressed periods.
Study examines ETFs for Pakistan exposure, highlighting risks and performance.
problem Investment risks and performance in Pakistan-exposed ETFs.
method Historical and dynamic optimization analyses of 30 ETFs.
result Dynamic optimization offers improved performance metrics.
Bounds on long-term returns of leveraged ETFs are given.
problem Uncertainty in long-term returns of leveraged ETFs.
method Quadratic bounds on log-returns based on daily log-returns of the underlying index.
result Sufficient conditions for outperformance and underperformance of leveraged ETFs.
Paper compares ETF and futures carry rates in segmented Bitcoin markets.
problem Limitations in cross-margining between spot Bitcoin and CME futures.
method Estimates carry rates from IBIT options and CME futures, uses put-call parity and daily ETF holdings.
result Mean and median wedge in carry rates is 2.58 and 2.52 percent, respectively.
Leveraged ETFs can boost returns but increase risk.
problem Risk and return trade-off in leveraged ETF investing.
method Bootstrapped Monte-Carlo simulations of mixed stock and bond portfolios.
result Leverage can amplify returns without significantly increasing risk for long-term investors.
Leveraged ETFs can outperform their targets in certain market conditions, contrary to the volatility drag hypothesis.
problem The long-term performance decay of leveraged ETFs due to volatility drag.
method Unified framework incorporating AR(1) and AR-GARCH models, continuous-time regime switching, and flexible rebalancing frequencies.
result Return dynamics, including return autocorrelation, volatility clustering, and regime persistence, determine LETF performance.
ETF on CRIX reduces crypto risk and diversifies growth.
problem High volatility in cryptocurrencies makes them risky investments.
method Dynamic ETF construction on CRIX, considering fees, spreads, and rebalancing.
result ETF remains robust in core, low trading costs, increased liquidity.
Study examines strategies to reduce volatility in leveraged ETF markets.
problem Rebalancing trades in leveraged ETFs can destabilize financial markets.
method Agent-based simulation to compare different trading strategies.
result Increasing the minimum number of orders in rebalancing trades reduces market volatility.
Study examines volatility-based strategy for Chinese ETF options, improving returns in volatile markets.
problem Lack of effective trading strategies in volatile Chinese equity markets.
method Volatility forecasting using GARCH models to dynamically adjust positions and exposures.
result Dynamic adjustment of positions and exposures enhances returns in volatile markets.
This paper studies the empirical tracking performance of leveraged ETFs on gold, and their price relationships with gold spot and futures. For tracking the gold spot, we find that our optimized portfolios with short-term gold futures are highly effective in replicating prices. The market-traded gold ETF (GLD) also exhi…
STRAPSim measures ETF portfolio similarity better than existing methods.
problem Measuring portfolio similarity for ETFs and portfolios.
method Semantic, two-level, residual-aware portfolio similarity computation.
result STRAPSim outperforms existing methods in predictive accuracy and ranking alignment.
ETF approval boosts Bitcoin's correlation with equities, stabilizes with gold, and maintains negative correlation with fiat currencies.
problem Impact of Bitcoin ETF approval on Bitcoin's relationships with traditional assets.
method Rolling correlation analysis, Chow tests, and DCC-GARCH models.
result Bitcoin's correlation with equities increased significantly post-ETF approval, while its relationship with gold stabilized and remained negatively correlated with fiat currencies.
MARCD uses generative scenarios to improve portfolio decisions during regime shifts.
problem Improving portfolio decisions under regime shifts and drawdowns.
method MARCD employs a Gaussian HMM for regime inference, a diffusion generator for scenario production, and a CVaR allocator with tail-weighted and crisis-aware components.
result MARCD reduces maximum drawdowns by 34% compared to baseline methods over 2020-2025.
Study examines herding behavior in stocks, US ETFs, and cryptocurrencies.
problem Understanding herding behavior in different types of investment vehicles.
method Cross-sectional Absolute Deviation model, Minimum Spanning Tree, Louvain community detection.
result Herding behavior exists at all times across all types of investment vehicles at a subset level.
This paper identifies and analyzes biases in risk-adjusted index weighting methods, affecting social welfare and market fairness.
problem Biases in risk-adjusted index weighting methods lead to tracking errors and fraud in indices and ETFs.
method Characterizes and analyzes the biases and adverse effects of risk-adjusted index weighting methods.
result These biases reduce social welfare and can enable harmful arbitrage activities.
In this article, we consider the small-time asymptotics of options on a \emph{Leveraged Exchange-Traded Fund} (LETF) when the underlying Exchange Traded Fund (ETF) exhibits both local volatility and jumps of either finite or infinite activity. Our main results are closed-form expressions for the leading order terms of …
Study recovers investor preferences from portfolio data using synthetic data and robust optimization.
problem Recovering latent investor preferences from observed portfolio allocations under uncertainty.
method Inverse portfolio optimization framework integrating robust optimization and regret-based inference.
result Accurate recovery of transaction cost parameters and partial identifiability of ESG penalties under preference misspecification and market shocks.
Dropout is a popular regularization technique in deep learning. Yet, the reason for its success is still not fully understood. This paper provides a new interpretation of Dropout from a frame theory perspective. By drawing a connection to recent developments in analog channel coding, we suggest that for a certain famil…
Study predicts US stock market will continue to fall post-COVID-19.
problem Analyzing the recovery trend of the US stock market post-COVID-19.
method Used Deep Learning, Neuro Network, and Time-series analysis on S&P 500, Nasdaq 100, and Dow Jones Industrial Average data.
result LSTM model predicts US stock market will continue to fall post-COVID-19.
GG distribution improves option pricing for negatively skewed spot price distributions.
problem Inaccurate Black-Scholes model for negatively skewed spot price distributions.
method Applied Generalized Gamma (GG) distribution as a Risk-Neutral Density (RND) for Heston's SV model.
result GG distribution better matches market option data with negatively skewed spot price distributions.
Framework improves ETF volatility forecasting by adapting to market conditions.
problem Challenges in volatility forecasting due to shifting market conditions and varying model performance.
method Risk-sensitive specialist routing using online risk-sensitive evaluation and state-dependent gating.
result Reduces forecast loss by 24% and underprediction loss by 22% compared to rolling-best baseline.
Enhanced Transformer models predict ETF portfolio performance by optimizing covariance and semi-covariance matrices.
problem Static covariance estimates fail to capture dynamic market fluctuations and non-linear correlations.
method Transformer-based models for real-time covariance and semi-covariance predictions.
result Portfolios optimized with semi-covariance matrix outperform those with standard covariance matrix, especially in volatile conditions.
Pipeline combines ETF preprocessing with tabular model for cross-modal inference.
problem Transferability of tabular models across different modalities.
method Fixed comparison object, ETF preprocessing, in-context inference.
result Pipeline is broadly competitive, runs faster, and produces well-calibrated probabilities.
Study non-parametric frequency-domain system identification from finite samples.
problem Frequency-domain system identification from limited data.
method Empirical Transfer Function Estimate (ETFE) under sub-Gaussian colored noise and stability assumptions.
result ETFE estimates are concentrated around true values with a finite-sample rate of Ntot−1/3 for all frequencies in the H∞ norm. The paper finds optimal ways to combine ETFs to minimize costs for investors.
problem Finding the best combination of ETFs to match a target gearing ratio at the lowest expense.
method Linear programming and convex geometry to prove the two-fund theorem for ETFs.
result The cheapest way to achieve a target gearing ratio is by combining the two nearest undominated ETF products.
We analyze neural collapse in neural networks, showing that features collapse to vertices of a Simplex ETF.
problem Understanding and optimizing the features learned in the last layer of neural networks during training.
method Simplified unconstrained feature model, studying the global optimization landscape of cross-entropy loss with weight decay.
result The global minimizers of the loss are Simplex ETFs, and other critical points are strict saddles with negative curvature.