FRM Financial Risk Meter

Mihoci, Andrija; Althof, Michael; Chen, Cathy Yi‐Hsuan; Härdle, Wolfgang Karl

doi:10.1108/s0731-905320200000042016

Cited by 14 publications

(3 citation statements)

References 19 publications

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“…A systemic indicator of risk was proposed (Mihoci at al., (2020), taking into account the links and interdependencies between financial institutions using information on the final results. The Financial Risk Meter (FRM) is based on minimal absolute regression and quantile regression of the choice operator, designed to capture the joint movements of events.…”

Section: The Market Research Conducting -A Literature Reviewmentioning

confidence: 99%

Financial Risks in the Conditions of Unstable Market Economy

Pakhucha

Sievidova

Siadrysta

et al. 2021

EJSD

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The necessity and significance of financial risks, which are a constant factor in the activity of any enterprise, are substantiated. It is established that for a broader understanding of the essence of financial risk it is necessary to consider it as a cumulative risk of the enterprise. The growing impact of financial risks on the results of financial and economic activities is associated with the uncertain economic situation, the instability of financial market conditions, the rapid introduction of new financial technologies and financial instruments, expanding the scope of financial relations of the enterprise. A system of financial ratios has been formed to quantify the risk of enterprises of different sizes, namely for large, medium and small enterprises, which most accurately reflect the current financial situation. Has been proven that the growing pressure of the crisis and insecurity on the part of the state will lead to the transition from the crisis in which enterprises are today, to a state of economic bankruptcy. It is established that the first place among the blocks of financial coefficients of risk assessment of enterprises is occupied by the block of business activity, then the block of profitability indicators, the block of liquidity indicators, the final block of capital structure. For each of the defined blocks the directions of increase, or optimization that will provide financial stability of the enterprise are presented.

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Section: The Market Research Conducting -A Literature Reviewmentioning

confidence: 99%

Financial Risks in the Conditions of Unstable Market Economy

Pakhucha

Sievidova

Siadrysta

et al. 2021

EJSD

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“…CRIX employs Akaike Information Criterion (AIC, Akaike (1987)) that determine the number of constituents quarterly according to the explanatory power each CC has over the market movements. CRIX was used as a proxy to the CC market before in research papers by Elendner et al (2018), Klein et al (2018), Mihoci et al (2019), and was adopted as a benchmark by commercial projects like Smarter Than Crypto, Crypto20, F5 Crypto Index, and also used by the European Central Bank as a market indicator in the report dedicated to understanding the "crypto-asset phenomenon" (Chimienti et al (2019)). These use cases confirm the applicability of CRIX as an appropriate basis for VCRIX.…”

Section: Cryptocurrency Indexmentioning

confidence: 99%

VCRIX - A Volatility Index for Crypto-Currencies

Kim¹,

Trimborn

Härdle

2019

SSRN Journal

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Public interest, explosive returns, and diversification opportunities gave stimulus to the adoption of traditional financial tools to crypto-currencies.While the CRIX index offered the first scientifically-backed proxy to the cryptomarket (analogous to S&P 500), the introduction of Bitcoin futures by Cboe became the milestone in the creation of the derivatives market for cryptocurrencies. Following the intuition of the "fear index" VIX for the American stock market, the VCRIX volatility index was created to capture the investor expectations about the crypto-currency ecosystem. VCRIX is built based on CRIX and offers a forecast for the mean annualized volatility of the next 30 days, re-estimated daily. The model was back-tested for its forecasting power, resulting in low MSE performance and further examined by the simulation of VIX (resulting in a correlation of 78% between the actual VIX and VIX * Financial support from NUS FRC grant R-155-000-199-114 "Augmented machine learning and network analysis with applications to cryptocurrencies and blockchains" and IRTG 1792 "High Dimensional Non Stationary Time Series", Humboldt-Universität zu Berlin is gratefully acknowledged.† kolesnal [at]hu-berlin.de ‡ simon.trimborn[at]nus.edu.sg § haerdle[at]hu-berlin.de 1 estimated with the VCRIX model). VCRIX provides forecasting functionality and serves as a proxy for the investors' expectations in the absence of the developed derivatives market. These features provide enhanced decision making capacities for market monitoring, trading strategies, and potentially option pricing.

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“…Furthermore, Zhu and Pan (2020) argue that a single network parameter may not be satisfactory as it treats all nodes of the network homogeneously. In particular, the NAR implies that each node is affected by its neighbors in the same extent, while in reality, we may have, e.g., financial institutions that are affected less or more than the others (see Mihoci et al (2020)). Hence they propose to detect communities in a network based on the given adjacency matrix and suggest that the nodes in each community share a separate network effect parameter.…”

Section: Introductionmentioning

confidence: 99%

SONIC: SOcial Network with Influencers and Communities

Chen

Härdle

Klochkov³

2019

SSRN Journal

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The integration of social media characteristics into an econometric framework requires modeling a high dimensional dynamic network with dimensions of parameter typically much larger than the number of observations. To cope with this problem, we introduce SONIC, a new highdimensional network model that assumes that (1) only few influencers drive the network dynamics; (2) the community structure of the network is characterized by homogeneity of response to specific influencers, implying their underlying similarity. An estimation procedure is proposed based on a greedy algorithm and LASSO regularization. Through theoretical study and simulations, we show that the matrix parameter can be estimated even when sample size is smaller than the size of the network. Using a novel dataset retrieved from one of leading social media platforms-StockTwits and quantifying their opinions via natural language processing, we model the opinions network dynamics among a select group of users and further detect the latent communities. With a sparsity regularization, we can identify important nodes in the network.

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FRM Financial Risk Meter

Cited by 14 publications

References 19 publications

Financial Risks in the Conditions of Unstable Market Economy

Financial Risks in the Conditions of Unstable Market Economy

VCRIX - A Volatility Index for Crypto-Currencies

SONIC: SOcial Network with Influencers and Communities

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