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on Risk Management |
| By: | Donggyu Kim (Department of Economics, University of California Riverside); Minseog Oh |
| Abstract: | This paper introduces a dynamic minimum variance portfolio (MVP) model using nonlinear volatility dynamic models, based on high-frequency financial data. Specifically, we impose an autoregressive dynamic structure on MVP processes, which helps capture the MVP dynamics directly. To evaluate the dynamic MVP model, we estimate the inverse volatility matrix using the constrained 1-minimization for inverse matrix estimation (CLIME) and calculate daily realized non-normalized MVP weights. Based on the realized non-normalized MVP weight estimator, we propose the dynamic MVP model, which we call the dynamic realized minimum variance portfolio (DR-MVP) model. To estimate a large number of parameters, we employ the least absolute shrinkage and selection operator (LASSO) and predict the future MVPand establish its asymptotic properties. Using high-frequency trading data, we apply the proposed method to MVP prediction. |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:ucr:wpaper:202421 |
| By: | Sung Hoon Choi; Donggyu Kim (Department of Economics, University of California Riverside) |
| Abstract: | In this paper, we introduce a novel method for predicting intraday instantaneous volatility based on Itˆo semimartingale models using high-frequency financial data. Several studies have highlighted stylized volatility time series features, such as interday auto-regressive dynamics and the intraday U-shaped pattern. To accommodate these volatility features, we propose an interday-by-intraday instantaneous volatility matrix process that can be decomposed into low-rank conditional expected instantaneous volatility and noise matrices. To predict the low-rank conditional expected instantaneous volatility matrix, we propose the Two-sIde Projected-PCA (TIP-PCA) procedure. We establish asymptotic properties of the proposed estimators and conduct a simulation study to assess the finite sample performance of the proposed prediction method. Finally, we apply the TIP-PCA method to an out-of-sample instantaneous volatility vector prediction study using high-frequency data from the S&P 500 index and 11 sector index funds. |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:ucr:wpaper:202423 |
| By: | Eduardo Abi Jaber (CMAP - Centre de Mathématiques Appliquées de l'Ecole polytechnique - Inria - Institut National de Recherche en Informatique et en Automatique - X - École polytechnique - IP Paris - Institut Polytechnique de Paris - CNRS - Centre National de la Recherche Scientifique); Camille Illand (AXA Investment Managers, Multi Asset Client Solutions, Quantitative Research - AXA); Shaun Xiaoyuan Li (UP1 - Université Paris 1 Panthéon-Sorbonne, AXA Investment Managers, Multi Asset Client Solutions, Quantitative Research - AXA) |
| Abstract: | We consider the joint SPX-VIX calibration within a general class of Gaussian polynomial volatility models in which the volatility of the SPX is assumed to be a polynomial function of a Gaussian Volterra process defined as a stochastic convolution between a kernel and a Brownian motion. By performing joint calibration to daily SPX-VIX implied volatility surface data between 2012 and 2022, we compare the empirical performance of different kernels and their associated Markovian and non-Markovian models, such as rough and non-rough pathdependent volatility models. In order to ensure an efficient calibration and a fair comparison between the models, we develop a generic unified method in our class of models for fast and accurate pricing of SPX and VIX derivatives based on functional quantization and Neural Networks. For the first time, we identify a conventional one-factor Markovian continuous stochastic volatility model that is able to achieve remarkable fits of the implied volatility surfaces of the SPX and VIX together with the term structure of VIX futures. What is even more remarkable is that our conventional one-factor Markovian continuous stochastic volatility model outperforms, in all market conditions, its rough and non-rough path-dependent counterparts with the same number of parameters. |
| Keywords: | SPX and VIX modeling, Stochastic volatility, Gaussian Volterra processes, Quantization, Neural Networks |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-03902513 |
| By: | Einmahl, John (Tilburg University, School of Economics and Management); Peng, Liang |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:tiu:tiutis:970231c1-c8e0-4f52-a0a4-fe7b74063c2d |
| By: | Anusha Chari; Karlye Dilts Stedman; Christian T. Lundblad |
| Abstract: | A new, high frequency measure of investor sentiment outperforms similar measures in forecasting investment activity in emerging markets. |
| Keywords: | risk-on/risk-off; global investor risk aversion; extreme events; tail risk; portfolio reallocation; return predictability |
| JEL: | F21 F36 F65 G11 G12 G15 G23 |
| Date: | 2024–11–26 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedkrw:99293 |
| By: | Jianqing Fan; Donggyu Kim (Department of Economics, University of California Riverside); Minseok Shin |
| Abstract: | Several novel statistical methods have been developed to estimate large integrated volatility matrices based on high-frequency financial data. To investigate their asymptotic behaviors, they require a sub-Gaussian or finite high-order moment assumption for observed log-returns, which cannot account for the heavy-tail phenomenon of stock-returns. Recently, a robust estimator was developed to handle heavy-tailed distributions with some bounded fourth-moment assumption. However, we often observe that log-returns have heavier tail distribution than the finite fourth-moment and that the degrees of heaviness of tails are heterogeneous across asset and over time. In this paper, to deal with the heterogeneous heavy-tailed distributions, we develop an adaptive robust integrated volatility estimator that employs pre-averaging and truncation schemes based on jump-diffusion processes. We call this an adaptive robust pre-averaging realized volatility (ARP) estimator. We show that the ARP estimator has a sub-Weibull tail concentration with only finite 2α-th moments for any α > 1. In addition, we establish matching upper and lower bounds to show that the ARP estimation procedure is optimal. To estimate large integrated volatility matrices using the approximate factor model, the ARP estimator is further regularized using the principal orthogonal complement thresholding (POET) method. The numerical study is conducted to check the finite sample performance of the ARP estimator. |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:ucr:wpaper:202419 |
| By: | Leonie Bräuer (University of Geneva; Swiss Finance Institute, Students); Harald Hau (University of Geneva) |
| Abstract: | Using comprehensive new contract level data (EMIR) for the period 2019-2023, we explore how the FX derivative trading by European funds compares to a feasible theoretical benchmark of optimal hedging. We find that hedging behavior by all fund types is often partial, unitary (i.e., with a single currency focus), and sub-optimal. Overall, the observed FX derivative trading does not significantly reduce the return risk of the average European investment funds, even though optimal hedging strategies could do so without incurring substantial trading costs. |
| Keywords: | Global Currency Hedging, Institutional Investors, Mean-Variance Optimization |
| JEL: | E44 F31 F32 G11 G15 G23 |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:chf:rpseri:rp24103 |
| By: | Christopher J. Neely |
| Abstract: | Presidential elections create uncertainty about future economic policy that translates into volatility in asset prices. How has the VIX performed around U.S. elections since 1988? |
| Keywords: | asset price volatility; stock market; stock market volatility; presidential elections |
| Date: | 2024–12–02 |
| URL: | https://d.repec.org/n?u=RePEc:fip:l00001:99209 |
| By: | Jianqing Fan; Donggyu Kim (Department of Economics, University of California Riverside); Minseok Shin; Yazhen Wang |
| Abstract: | This paper introduces a novel Ito diffusion process for both factor and idiosyncratic volatilities whose eigenvalues follow the vector auto-regressive (VAR) model. We call it the factor and idiosyncratic VAR-Ito (FIVAR-Ito) model. The FIVAR-Ito model considers dynamics of the factor and idiosyncratic volatilities and involve many parameters. In addition, the empirical studies have shown that the financial returns often exhibit heavy tails. To address these two issues simultaneously, we propose a penalized optimization procedure with a truncation scheme for a parameter estimation. We apply the proposed parameter estimation procedure to predicting large volatility matrices and investigate its asymptotic properties. Using high-frequency trading data, the proposed method is applied to large volatility matrix prediction and minimum variance portfolio allocation. |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:ucr:wpaper:202415 |
| By: | Azamat Abdymomunov; Zheng Duan; Anne Lundgaard Hansen; Ulas Misirli |
| Abstract: | We propose an approach for generating financial market scenarios for stress testing financial firms' market risk exposures. This approach can be used by industry practitioners and regulators for their stress scenario design. Our approach attempts to maximize risk capture with a relatively small number of scenarios. A single scenario could miss potential vulnerabilities, while stress tests using a large number of scenarios could be operationally costly. The approach has two components. First, we model relationships among market risk factors to set scenario shock magnitudes consistently across markets. Second, we use these models to generate a large number of scenarios and select those most likely to have tail-loss impacts and substantial variation across scenarios. |
| Keywords: | stress test; bank supervision; market risk |
| Date: | 2024–12–19 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedrwp:99330 |
| By: | Federico Giorgi (DEF, University of Rome "Tor Vergata"); Stefano Herzel (DEF, University of Rome "Tor Vergata"); Paolo Pigato (DEF, University of Rome "Tor Vergata") |
| Abstract: | We propose an algorithm, based on Reinforcement Learning, to hedge the payoff on a European call option. The algorithm is first tested in a model where the problem has a well known analytic solution, so that we can compare the strategy obtained by the algorithm to the theoretical optimal one. In a more realistic case, considering transaction costs, the algorithm outperforms the standard delta hedging strategy. |
| Keywords: | Reinforcement Learning; Dynamic Strategies; Risk management |
| Date: | 2024–12–17 |
| URL: | https://d.repec.org/n?u=RePEc:rtv:ceisrp:586 |
| By: | Huixin Bi; Andrew Foerster; Nora Traum |
| Abstract: | Central bank asseUsing a two-country monetary-union framework with financial frictions, we study sovereign default and liquidity risks and quantify the efficacy of asset purchases. Default risk increases with government indebtedness and shifts in the fiscal limit perceived by investors. Liquidity risks increase when the default probability affects credit market tightness. The framework indicates that shifts in fiscal limits, more than rising government debt, played a crucial role for Italy around 2012. While both default and liquidity risks can dampen economic and financial conditions, the model suggests that the magnifying effect from liquidity risks can be more consequential. In this context, asset purchases can stabilize economic conditions especially under scenarios of elevated financial stress.t purchases can effectively stabilize economic conditions, especially in scenarios of elevated financial stress. |
| Keywords: | Monetary and fiscal policy interaction; unconventional monetary policy; Regime-Switching Models |
| JEL: | E58 E63 F45 |
| Date: | 2024–12–03 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedkrw:99294 |
| By: | Natee Amornsiripanitch; Siddhartha Biswas; John Orellana; David Zink |
| Abstract: | Using data on expected flood damage and National Flood Insurance Program policies, we estimate annual flood risk protection gaps and underinsurance among single-family residences in the contiguous United States. Annually, 70 percent ($17.1 billion) of total flood losses would be uninsured. Underinsurance, defined as protection gaps among properties with positive flood risk and incentives to purchase full flood insurance coverage, totals $15.7 billion annually. Eighty percent of at-risk households are underinsured, and average underinsurance is $7, 208 per year. Underinsurance persists both inside and outside the Federal Emergency Management Agency’s special flood hazard areas, suggesting frictions in the provision of risk information and regulatory compliance. Seventy percent of uninsured households would benefit from purchasing flood insurance, even as prevailing prices rise. Household beliefs about climate risks are strongly correlated with underinsurance. |
| Keywords: | climate risk; physical risk; flood; underinsurance |
| JEL: | G22 G52 Q54 |
| Date: | 2024–12–23 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedpwp:99282 |
| By: | Sung Hoon Choi; Donggyu Kim (Department of Economics, University of California Riverside) |
| Abstract: | In this paper, we develop a novel large volatility matrix estimation procedure for analyzing global financial markets. Practitioners often use lower-frequency data, such as weekly or monthly returns, to address the issue of different trading hours in the international financial market. However, this approach can lead to inefficiency due to information loss. To mitigate this problem, our proposed method, called Structured Principal Orthogonal complEment Thresholding (S-POET), incorporates observation structural information for both global and national factor models. We establish the asymptotic properties of the S-POET estimator, and also demonstrate the drawbacks of conventional covariance matrix estimation procedures when using lower-frequency data. Finally, we apply the S-POET estimator to an out-of-sample portfolio allocation study using international stock market data. |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:ucr:wpaper:202424 |
| By: | Einmahl, John (Tilburg University, Center For Economic Research); Peng, Liang |
| Keywords: | Estimating equation; risk measure; Semi-supervised inference; variance reduction |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:tiu:tiucen:970231c1-c8e0-4f52-a0a4-fe7b74063c2d |
| By: | Yalin Gündüz (Deutsche Bundesbank); Steven Ongena (University of Zurich - Department Finance; Swiss Finance Institute; KU Leuven; NTNU Business School; Centre for Economic Policy Research (CEPR)); Gunseli Tumer-Alkan (VU University Amsterdam; Vrije Universiteit Amsterdam, School of Business and Economics); Yuejuan Yu (Shandong University) |
| Abstract: | We assess the differential impact of the “Big Bang” and “Small Bang” contract and convention changes on market participants across CDS markets. We couple comprehensive bank-firm level CDS trading data from DTCC to the German credit register containing bilateral bank-firm credit exposures. We find that after the Bangs, the cost of buying CDS contracts becomes lower for non-dealer banks, and that – because of this decrease in insurance cost – these banks extend relatively more credit to CDS traded and affected firms compared to dealers, and hedge more effectively. Hence, standardization lowers the cost of credit insurance and increases credit availability. |
| Keywords: | Credit default swaps, credit exposure, hedging, bank lending, Depository Trust and Clear-ing Corporation (DTCC) |
| Date: | 2024–05 |
| URL: | https://d.repec.org/n?u=RePEc:chf:rpseri:rp2483 |
| By: | Bouwhuis, Dirck (Tilburg University, School of Economics and Management); Hendrickx, Ruud (Tilburg University, School of Economics and Management); Herings, P.J.J. (Tilburg University, School of Economics and Management) |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:tiu:tiutis:c8fb20e1-4ce9-4779-bacd-032df0108891 |