Assessing network risk with FRM: links with pricing kernel volatility and application to cryptocurrencies

A systemic risk measure is proposed accounting for links and mutual dependencies between financial institutions utilizing tail event information. Financial Risk Meter (FRM) is based on least absolute shrinkage and selection operator quantile regression designed to capture tail event co-movements. The FRM focus lies on understanding active set data characteristics and the presentation of interdependencies in a network topology. Two FRM indices are presented, namely, FRM@Americas and FRM@Europe. The FRM indices detect systemic risk at selected areas and identify risk factors. In practice, FRM is applied to the return time series of selected financial institutions and macroeconomic risk factors. The authors identify companies exhibiting extreme “co-stress” as well as “activators” of stress. With the SRM@EuroArea, the authors extend to the government bond asset class, and to credit default swaps with FRM@iTraxx. FRM is a good predictor for recession probabilities, constituting the FRM-implied recession probabilities. Thereby, FRM indicates tail event behavior in a network of financial risk factors.

Management Insights

This dissertation consists of four essays, focusing on the relationship between financial risks, financial market uncertainty and macroeconomic conditions. The first essay estimates the effects of anticipated and unanticipated monetary policy changes on jump variation by employing high frequency non-parametric jump detection methods. We use an event study approach and a structural VAR framework in examining stock price jump variation for the aggregate economy, the financial, health, energy and telecommunication-information technology sectors (Tel-Info). We find that anticipated changes in the Fed funds have no significant effect on jumps. In contrast, jump variation in the price of financial market data increases with monetary policy surprises. We document evidence of asymmetries in the response of jumps to monetary policy changes. Monetary policy surprises and positive changes in the Fed target rate induce increment in jumps. Similar results exist in the sector analysis. In addition, this study uncovers no evidence of endogenous response between jumps and monetary policy surprises. The second essay estimates the response of uncertainty/risk aversion to monetary policy actions in both the financial sector and the aggregate economy using a Structural Vector Autoregressive (SVAR) model. When compared with other sectors, our constructs reveal that financial risk aversion/uncertainty has greater correlation with the aggregate risk aversion and uncertainty. Our analysis reveals that financial risk aversion and uncertainty exhibit stronger interdependence with monetary policy actions than aggregate uncertainty and risk aversion. The third essay provides the dynamic characterization of the link between ex-ante financial distress risk and the real economy. Using 219, 990 firm-year observations, an ex-ante measure of financial distress is generated at sector level. By employing simultaneous equation model, we provide a comprehensive set-up for predicting ex-ante financial distress risk and examining its effect on GDP growth. Over the period of 1970-2012, the results from the US firms reveal that ex-ante financial distress strongly relate to GDP growth. Ex-ante distress risk contracts GDP growth by up to 10%. Similar contractions in the growth of GDP are uncovered when a single equation approach is used in establishing the relationship between financial distress and growth in GDP. In addition, the results remain consistent when a confirmatory analysis is generated by using a weighted sector index of financial distress. The fourth essay examines liquidity risk and financial integration using bank level flows for 95 countries. The results suggest that global banking network influences liquidity risk. The more the banks are connected to each other the more they are prone to liquidity risk and the result is the same for intermediaries in the network formation. Borrowers that connect to important lenders are not at an advantage, but banks that have independent access to finance in the financial network are at an advantage. On a regional basis, banks in Europe, Africa and Asia and Pacific that have strong connections are prone to liquidity risk. American banks that function as intermediary in the network are prone to liquidity risk. Banks in the American region that have independent access to financing in the financial network are able to reduce liquidity creation, but their degree of connectivity worsens net stable funding.

Pricing Kernels with Stochastic Skewness and Volatility Risk

Globally, the increasing holdings of financial assets by non-financial firms, a phenomenon known as “corporate financialization,” has become a significant trend in response to economic uncertainty and the pursuit of alternative returns. This trend is particularly prominent in China, whose unique institutional environment and transitional economy provide an ideal laboratory for examining the impact of financialization on corporate risk. Departing from prior research that treats financial assets as homogeneous, this study disaggregates them by liquidity into short-term and long-term categories to uncover their heterogeneous effects on financial risk. Using a dataset of listed Chinese construction and manufacturing firms from 2014 to 2023, we find that financial asset allocation has a significant “double-edged sword” effect on financial risk. On one hand, the allocation to highly liquid, short-term financial assets creates a “reservoir effect,” significantly mitigating financial risk by easing financing constraints. On the other hand, the allocation to less liquid, long-term financial assets exhibits a “crowding-out effect,” diverting funds from core operations and thus significantly increasing financial risk. This risk-amplifying effect is particularly pronounced in state-owned enterprises (SOEs) and large-scale firms. The core contribution of this study is to reveal the opposing effects and underlying mechanisms of financial assets with different liquidities on corporate financial risk, challenging the conventional view of corporate financialization as a monolithic phenomenon. Our findings provide micro-level evidence for managers to optimize asset allocation by balancing “precautionary savings” with “speculative investments,” and offer important insights for regulators to design more targeted, differentiated policies to safeguard against systemic risk.

Financial Contagion Risk and the Stochastic Discount Factor

Physical and transition climate risks and financial risk predictability in the US banking sector

PurposeInterdependency among industries is vital for understanding economic structures and managing industrial portfolios. However, it is hard to precisely model the interconnecting structure among industries. One of the reasons is that the interdependencies show a different pattern in tail events. This paper aims to investigate industry interdependency with the tail events.Design/methodology/approachGeneral predictive model of Rapachet al.(2016) is extended to an interdependency model via least absolute shrinkage and selection operator quantile regression and network analysis. A dynamic network approach was applied on the Fama–French industry portfolios to study the time-varying interdependencies.FindingsA denser network with heterogeneous central industries is found in tail cases. Significant interdependency varieties across time are shown under dynamic network analysis. Market volatility is identified as an influential factor of industry connectedness as well as clustering tendency under both normal and tail cases. Moreover, combining dynamic network with prediction direction information into out-of-sample industry return forecasting, a lower tail case is obtained, which gives the most accurate prediction of one-month forward returns. Finally, the Sharpe ratio criterion prefers high-centrality portfolios when tail risks are considered.Originality/valueThis study examines the industry portfolio interactions under the framework of network analysis and also takes into consideration tail risks. The combination of economic interpretation and statistical methodology helps in having a clear investigation of industry interdependency. Moreover, a new trading strategy based on network centrality seems profitable in our data sample.

Analyzing the nexus between financial risk and economic risk in India: Evidence through the lens of wavelet coherence and non-parametric approaches

Local government debt plays an important role in promoting economic development, but with the continuous expansion of the scale of local government debt, coupled with the opacity of financial and debt information, local debt risk may be directly transformed into financial risk. Therefore, the potential mechanism of local government debt risk inducing systemic financial risk is studied to strictly control local government debt and prevent it at present. It is of great practical significance to mitigate systemic financial risk. At present, the research on local government debt mainly focuses on the mechanism of local government debt to amplify economic fluctuations, while the potential mechanism of local government debt risk to induce systemic financial risk is relatively few. From the perspective of local government bonds and land finance, the innovation of this paper is to depict the Chinese elements such as the maturity of local government bonds, the opacity of debt and financial information, and to integrate local government debt, land finance and financial intermediaries into the framework of general equilibrium to analyze the potential mechanism of systemic financial risk induced by local debt risk. This study finds that when the economy is facing negative productivity shocks, the decline in total output will inhibit the land demand of households and enterprises, and the decline in land prices caused by the decline in demand will reduce the land-related fiscal revenue of local governments. On the one hand, the decline of land-related fiscal revenue will reduce government expenditure; on the other hand, it will increase the pressure on local governments to repay debt and the risk of debt, which will lead to the rise of bond interest rates and the decline of bond prices. Lower bond prices will affect the balance sheet of financial intermediaries, resulting in increased leverage and risk premium of financial intermediaries. Under the balance sheet constraints, financial intermediaries will shrink corporate credit, resulting in an increase in systemic financial risk. Rising interest rates and tightening credit will restrain enterprises’ investment and land demand, further reduce total output and land prices, increase the debt risk of local governments, and form a financial accelerator mechanism in the economy, which will lead to the mutual reinforcement of the debt risk and financial risk of local governments and form a vicious circle. In addition, when financial risk rises, the default risk of local government debt is lower. Based on the trade-off between risk and return, financial intermediaries will hold more local government bonds. Credit squeeze brought by asset allocation changes will further strengthen the financial accelerator mechanism and strengthen the interaction between debt risk and financial risk. Meanwhile, although extending the bond maturity can reduce the pressure of local governments to repay their debt in the short term, the longer the bond maturity is, the greater the financial shock and economic volatility caused by local government debt risk are. The paper indicates that: for resolving local government debt risk and reducing the probability of systemic risk, we firstly need to improve the local government bond market, diversify local government bond investors, and change the current situation dominated by commercial banks; secondly, we should strengthen budget constraints and control the scale of local government debt; thirdly, we should rationally design the maturity structure of local government bonds, strengthen the transparency of local government finance and debt information, and play the role of market constraints; finally, when financial risk rises, the central bank can adopt non-traditional monetary policy to intervene in the market, which can reduce financial risk and social welfare losses.

Pricing kernel monotonicity and term structure: Evidence from China

This article is dedicated to the assessment of the dynamic fractional asset pricing model for financial risk evaluation and the use of the fractal markets theory to mathematically predict the price dynamics of assets as part of a financial risk management strategy. The article identifies recommendations for assessing financial risk based on mathematical methods for forecasting economic processes. Theoretical and empirical research methods were used. The article reveals the features of mathematical modeling of economic processes related to asset pricing in a volatile market. It is shown that financial mathematics in banking contributes to the stable development of the economy. The mathematical modeling of the price dynamics of financial assets is based on a substantive hypothesis and supported by fractal pair pricing models in order to reveal the specific market relations of business entities. According to the authors, the prospects of using forecast models to minimize the financial risks of derivative financial instruments are positive. The authors conclude that the considered methods contribute to managing financial risks and improving forecasts, including operations with derivatives.

This dissertation is composed of three loosely related chapters, all of which are empirical.In Chapter 1, I examine whether expectations are formed in a systematically different manner during periods of low volatility versus periods of high volatility. I achieve this by measuring non-linearities in relationship between the SP 500 and the VIX across different market regimes. Three distinct market regimes are identified through a Markov Process, allowing for the capture of non-constant behavior in the relationship between contemporaneous price changes and future volatility expectations. The results indicate that the effect of the underlying asset on the supply and demand dynamics of its derivative is strongest during periods of low volatility and weakest during periods of high volatility. The decrease in magnitude of the SP 500 coefficient as the market switches from low volatility to high, suggests that information scarcity (low volatility) makes additional data (price changes) more impactful. Measures to limit market volatility may make market participant prone to expect changes in the state of the system. The purpose of Chapter 2 is to draw inference from the tail behavior of financial market price volatility in order to compare and contrast volatility expectations with volatility realizations. In doing so, I discuss the implications of slowly decaying tails as they relate to systems susceptible to unpredictable and consequential events. In such cases where fat tails are identified, typical values such as the average and variance, do not properly characterize the risk and unpredictability of the dynamic process under study. Prior research has identified asset prices and asset volatility as being drawn from a power law distribution. This paper aims to quantitatively confirm this characterization, specifically for market volatility. Further, this paper identifies whether or not volatility expectations exhibit similar power law characteristics. Goodness of fit and log likelihood tests indicate that most realized volatility series are plausibly drawn from a power- law distribution. However, none of the studied implied volatility series show evidence of power-law behavior, suggesting that risk premia may exist for lower levels of volatility but does not scale proportionally to the more extreme crisis events. That is, risk premia does not scale proportionally as values move farther into the tail. In Chapter 3, co-authored with Minh Pham, we investigate how economic uncertainty, specifically stock market uncertainty, correlates to individuals' life-satisfaction. Using expected price volatility (VIX) as our anticipatory indicator and life-satisfaction as our measure of utility, our hypothesis is built on the Anticipatory Utility framework, which suggests that people also derive utility from their beliefs. After accounting for associations with the unemployment rate and stock ownership, we find a positive relationship between the VIX and low self-reported life- satisfaction. This analysis captures the contemporaneous effects of future beliefs and indicates that economic sentiment about the future plays an important role in individuals' feelings about the present. This work was inspired by a desire to understand the economic crises that redirect and ultimately redefine our socioeconomic lives, as individuals and as nations. I began my economic studies during one of the most profound crises in recent history, the global financial crisis of the late 2000s. Here again in 2021, as my studies conclude, economies grapple with another, albeit different crisis. Both the Covid-19 pandemic and the subprime financial crisis highlight a salient fact; we never really know when, why, or from where such extreme events arrive. But they do, and do so more frequently than we like or predict. Each of the chapters presented in this dissertation seek to understand the ways in which we anticipate and interact with a characteristic marker of economic and financial crises, uncertainty.

In the past 20 years, measures of economic uncertainty have been developed that are purely market price based; structural model based, using data on real fundamentals and asset prices; text based; or survey based. We compare the performance of these uncertainty measures in forecasting three real variables with irreversibilities—investment, hiring, and credit creation—as well as in explaining fluctuations in stock market and Treasury bond market volatility. In general, we find that structural model–based measures do better than measures constructed using other approaches, with a model of stock market volatility by David and Veronesi performing the best on several (but not all) dimensions. Their learning-based model's volatility places time-varying weights on inflation, earnings, and consumption news, as agents in the economy assess the impact that inflation has on the stability of real economic growth.

Using implied volatility on options to measure the relation between asset returns and variability