Value at Risk (VaR)

Value-at-Risk-Based Portfolio Insurance: Performance Evaluation and Benchmarking Against CPPI in a Markov-Modulated Regime-Switching Market ArXiv ID: 2305.12539 “View on arXiv” Authors: Unknown Abstract Designing dynamic portfolio insurance strategies under market conditions switching between two or more regimes is a challenging task in financial economics. Recently, a promising approach employing the value-at-risk (VaR) measure to assign weights to risky and riskless assets has been proposed in [“Jiang C., Ma Y. and An Y. “The effectiveness of the VaR-based portfolio insurance strategy: An empirical analysis” , International Review of Financial Analysis 18(4) (2009): 185-197”]. In their study, the risky asset follows a geometric Brownian motion with constant drift and diffusion coefficients. In this paper, we first extend their idea to a regime-switching framework in which the expected return of the risky asset and its volatility depend on an unobservable Markovian term which describes the cyclical nature of asset returns in modern financial markets. We then analyze and compare the resulting VaR-based portfolio insurance (VBPI) strategy with the well-known constant proportion portfolio insurance (CPPI) strategy. In this respect, we employ a variety of performance evaluation criteria such as Sharpe, Omega and Kappa ratios to compare the two methods. Our results indicate that the CPPI strategy has a better risk-return tradeoff in most of the scenarios analyzed and maintains a relatively stable return profile for the resulting portfolio at the maturity. ...

Value at Risk Models inFinance ArXiv ID: ssrn-356220 “View on arXiv” Authors: Unknown Abstract The main objective of this paper is to survey and evaluate the performance of the most popular univariate VaR methodologies, paying particular attention to thei Keywords: Value at Risk (VaR), Univariate methodologies, Performance evaluation, Risk Management Complexity vs Empirical Score Math Complexity: 6.5/10 Empirical Rigor: 8.0/10 Quadrant: Holy Grail Why: The paper involves advanced econometrics (CAViaR, GARCH, EVT) and Monte Carlo simulations, indicating high math complexity; its extensive simulation study with specific data-generating processes and performance comparisons provides strong empirical rigor. flowchart TD A["Research Goal: Evaluate performance of popular univariate VaR models"] --> B["Data Input: Daily Financial Return Series"] B --> C["Methodology: VaR Model Application Parametric, Historical, Monte Carlo"] C --> D["Computational Process: Backtesting & Performance Metrics Kupiec Test, Traffic Lights, Loss Functions"] D --> E["Key Findings: Model Suitability & Accuracy Outcomes Performance Rankings"]